diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..3c23be5f87fa450503b6b6b9966e43bf9843b738 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,187 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +__assets__/animations/compare/new_3.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/compare/old_3.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_07/01.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_07/02.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_07/03.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_07/04.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_08/01.gif filter=lfs diff=lfs merge=lfs -text +__assets__/animations/model_08/02.gif filter=lfs diff=lfs merge=lfs -text 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merge=lfs -text +data/raw/3dDS8RL/1.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS8RL/2.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS8RL/3.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS8RL/4.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS9/0.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS9/1.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS9/2.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS9/3.png filter=lfs diff=lfs merge=lfs -text +data/raw/3dDS9/4.png filter=lfs diff=lfs merge=lfs -text +triton-2.0.0-cp310-cp310-win_amd64.whl filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..9a686acc1afe0a6739973f2b0484f1dea7b233bd --- /dev/null +++ b/.gitignore @@ -0,0 +1,28 @@ +wandb/ +debugs/ +outputs/ +samples/ +__pycache__/ +ossutil_output/ +.ossutil_checkpoint/ +data/.cache + +scripts/ +!scripts/animate.py + +# *.ipynb +*.safetensors +*.ckpt + +models/* +!models/StableDiffusion/ +models/StableDiffusion/* +!models/StableDiffusion/*.txt +!models/Motion_Module/ +!models/Motion_Module/*.txt +models/Motion_Module/* +!models/DreamBooth_LoRA/ +!models/DreamBooth_LoRA/*.txt +!models/MotionLoRA/ +!models/MotionLoRA/*.txt +unet/* \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 32897cd3e640101ba184f8c4ccd896981de3804a..bea6f7f85665eee7c09c9a1d33f5fbe69fe6ebc8 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,212 @@ ---- -license: mit ---- +# AnimateDiff + +This repository is the official implementation of [AnimateDiff](https://arxiv.org/abs/2307.04725). + +**[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)** +
+Yuwei Guo, +Ceyuan Yang*, +Anyi Rao, +Yaohui Wang, +Yu Qiao, +Dahua Lin, +Bo Dai +

*Corresponding Author

+ + +[![arXiv](https://img.shields.io/badge/arXiv-2307.04725-b31b1b.svg)](https://arxiv.org/abs/2307.04725) +[![Project Page](https://img.shields.io/badge/Project-Website-green)](https://animatediff.github.io/) +[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Masbfca/AnimateDiff) +[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/guoyww/AnimateDiff) + +## Next +One with better controllability and quality is coming soon. Stay tuned. + +## Features +- **[2023/11/10]** Release the Motion Module (beta version) on SDXL, available at [Google Drive](https://drive.google.com/file/d/1EK_D9hDOPfJdK4z8YDB8JYvPracNx2SX/view?usp=share_link +) / [HuggingFace](https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt +) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules). High resolution videos (i.e., 1024x1024x16 frames with various aspect ratios) could be produced **with/without** personalized models. Inference usually requires ~13GB VRAM and tuned hyperparameters (e.g., #sampling steps), depending on the chosen personalized models. Checkout to the branch [sdxl](https://github.com/guoyww/AnimateDiff/tree/sdxl) for more details of the inference. More checkpoints with better-quality would be available soon. Stay tuned. Examples below are manually downsampled for fast loading. + + + + + + + + + + + + +
Original SDXLPersonalized SDXLPersonalized SDXL
+ + + +- **[2023/09/25]** Release **MotionLoRA** and its model zoo, **enabling camera movement controls**! Please download the MotionLoRA models (**74 MB per model**, available at [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules) ) and save them to the `models/MotionLoRA` folder. Example: + ``` + python -m scripts.animate --config configs/prompts/v2/5-RealisticVision-MotionLoRA.yaml + ``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Zoom InZoom OutZoom Pan LeftZoom Pan Right
Tilt UpTilt DownRolling Anti-ClockwiseRolling Clockwise
+ +- **[2023/09/10]** New Motion Module release! `mm_sd_v15_v2.ckpt` was trained on larger resolution & batch size, and gains noticeable quality improvements. Check it out at [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules) and use it with `configs/inference/inference-v2.yaml`. Example: + ``` + python -m scripts.animate --config configs/prompts/v2/5-RealisticVision.yaml + ``` + Here is a qualitative comparison between `mm_sd_v15.ckpt` (left) and `mm_sd_v15_v2.ckpt` (right): + + + + + + + + + + + +
+- GPU Memory Optimization, ~12GB VRAM to inference + + +## Quick Demo + +User Interface developed by community: + - A1111 Extension [sd-webui-animatediff](https://github.com/continue-revolution/sd-webui-animatediff) (by [@continue-revolution](https://github.com/continue-revolution)) + - ComfyUI Extension [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) (by [@Kosinkadink](https://github.com/Kosinkadink)) + - Google Colab: [Colab](https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb) (by [@camenduru](https://github.com/camenduru)) + +We also create a Gradio demo to make AnimateDiff easier to use. To launch the demo, please run the following commands: +``` +conda activate animatediff +python app.py +``` +By default, the demo will run at `localhost:7860`. +
+ + +## Model Zoo +
+Motion Modules + + | Name | Parameter | Storage Space | + |----------------------|-----------|---------------| + | mm_sd_v14.ckpt | 417 M | 1.6 GB | + | mm_sd_v15.ckpt | 417 M | 1.6 GB | + | mm_sd_v15_v2.ckpt | 453 M | 1.7 GB | + +
+ +
+MotionLoRAs + + | Name | Parameter | Storage Space | + |--------------------------------------|-----------|---------------| + | v2_lora_ZoomIn.ckpt | 19 M | 74 MB | + | v2_lora_ZoomOut.ckpt | 19 M | 74 MB | + | v2_lora_PanLeft.ckpt | 19 M | 74 MB | + | v2_lora_PanRight.ckpt | 19 M | 74 MB | + | v2_lora_TiltUp.ckpt | 19 M | 74 MB | + | v2_lora_TiltDown.ckpt | 19 M | 74 MB | + | v2_lora_RollingClockwise.ckpt | 19 M | 74 MB | + | v2_lora_RollingAnticlockwise.ckpt | 19 M | 74 MB | + +
+ +## Common Issues +
+Installation + +Please ensure the installation of [xformer](https://github.com/facebookresearch/xformers) that is applied to reduce the inference memory. +
+ + +
+Various resolution or number of frames +Currently, we recommend users to generate animation with 16 frames and 512 resolution that are aligned with our training settings. Notably, various resolution/frames may affect the quality more or less. +
+ + +
+How to use it without any coding + +1) Get lora models: train lora model with [A1111](https://github.com/continue-revolution/sd-webui-animatediff) based on a collection of your own favorite images (e.g., tutorials [English](https://www.youtube.com/watch?v=mfaqqL5yOO4), [Japanese](https://www.youtube.com/watch?v=N1tXVR9lplM), [Chinese](https://www.bilibili.com/video/BV1fs4y1x7p2/)) +or download Lora models from [Civitai](https://civitai.com/). + +2) Animate lora models: using gradio interface or A1111 +(e.g., tutorials [English](https://github.com/continue-revolution/sd-webui-animatediff), [Japanese](https://www.youtube.com/watch?v=zss3xbtvOWw), [Chinese](https://941ai.com/sd-animatediff-webui-1203.html)) + +3) Be creative togther with other techniques, such as, super resolution, frame interpolation, music generation, etc. +
+ + +
+Animating a given image + +We totally agree that animating a given image is an appealing feature, which we would try to support officially in future. For now, you may enjoy other efforts from the [talesofai](https://github.com/talesofai/AnimateDiff). +
+ +
+Contributions from community +Contributions are always welcome!! The dev branch is for community contributions. As for the main branch, we would like to align it with the original technical report :) +
+ +## Training and inference +Please refer to [ANIMATEDIFF](./__assets__/docs/animatediff.md) for the detailed setup. + +## Gallery +We collect several generated results in [GALLERY](./__assets__/docs/gallery.md). + +## BibTeX +``` +@article{guo2023animatediff, + title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning}, + author={Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Wang, Yaohui and Qiao, Yu and Lin, Dahua and Dai, Bo}, + journal={arXiv preprint arXiv:2307.04725}, + year={2023} +} +``` + +## Disclaimer +This project is released for academic use. We disclaim responsibility for user-generated content. Users are solely liable for their actions. The project contributors are not legally affiliated with, nor accountable for, users' behaviors. Use the generative model responsibly, adhering to ethical and legal standards. + + +## Contact Us +**Yuwei Guo**: [guoyuwei@pjlab.org.cn](mailto:guoyuwei@pjlab.org.cn) +**Ceyuan Yang**: [yangceyuan@pjlab.org.cn](mailto:yangceyuan@pjlab.org.cn) +**Bo Dai**: [daibo@pjlab.org.cn](mailto:daibo@pjlab.org.cn) + +## Acknowledgements +Codebase built upon [Tune-a-Video](https://github.com/showlab/Tune-A-Video). diff --git a/__assets__/animations/compare/ffmpeg b/__assets__/animations/compare/ffmpeg new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/__assets__/animations/compare/new_0.gif b/__assets__/animations/compare/new_0.gif new file mode 100644 index 0000000000000000000000000000000000000000..8681fa588e96320ae190b8d70d746ed1cbed74c5 Binary files /dev/null and b/__assets__/animations/compare/new_0.gif differ diff --git a/__assets__/animations/compare/new_1.gif b/__assets__/animations/compare/new_1.gif new file mode 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files /dev/null and b/__assets__/animations/motion_xl/01.gif differ diff --git a/__assets__/animations/motion_xl/02.gif b/__assets__/animations/motion_xl/02.gif new file mode 100644 index 0000000000000000000000000000000000000000..c72cb51e7498c95563cd23c5727feb10fb466c5e --- /dev/null +++ b/__assets__/animations/motion_xl/02.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:080596794cc7fd0d1d6cedf62d9b94fc01595c407921bd3108a0dccd9f06d389 +size 1450763 diff --git a/__assets__/animations/motion_xl/03.gif b/__assets__/animations/motion_xl/03.gif new file mode 100644 index 0000000000000000000000000000000000000000..3e2653493072cafeed5809353b1d193780dfa231 --- /dev/null +++ b/__assets__/animations/motion_xl/03.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c014681157fb3f89294ca13261695b196521fa5b32f673395358e74a49c7841 +size 1655196 diff --git a/__assets__/docs/animatediff.md b/__assets__/docs/animatediff.md new file mode 100644 index 0000000000000000000000000000000000000000..6e1f26b1cad4552bf44867bb0c0b6a5e2946ee47 --- /dev/null +++ b/__assets__/docs/animatediff.md @@ -0,0 +1,112 @@ +# AnimateDiff: training and inference setup +## Setups for Inference + +### Prepare Environment + +***We updated our inference code with xformers and a sequential decoding trick. Now AnimateDiff takes only ~12GB VRAM to inference, and run on a single RTX3090 !!*** + +``` +git clone https://github.com/guoyww/AnimateDiff.git +cd AnimateDiff + +conda env create -f environment.yaml +conda activate animatediff +``` + +### Download Base T2I & Motion Module Checkpoints +We provide two versions of our Motion Module, which are trained on stable-diffusion-v1-4 and finetuned on v1-5 seperately. +It's recommanded to try both of them for best results. +``` +git lfs install +git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/ + +bash download_bashscripts/0-MotionModule.sh +``` +You may also directly download the motion module checkpoints from [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules), then put them in `models/Motion_Module/` folder. + +### Prepare Personalize T2I +Here we provide inference configs for 6 demo T2I on CivitAI. +You may run the following bash scripts to download these checkpoints. +``` +bash download_bashscripts/1-ToonYou.sh +bash download_bashscripts/2-Lyriel.sh +bash download_bashscripts/3-RcnzCartoon.sh +bash download_bashscripts/4-MajicMix.sh +bash download_bashscripts/5-RealisticVision.sh +bash download_bashscripts/6-Tusun.sh +bash download_bashscripts/7-FilmVelvia.sh +bash download_bashscripts/8-GhibliBackground.sh +``` + +### Inference +After downloading the above peronalized T2I checkpoints, run the following commands to generate animations. The results will automatically be saved to `samples/` folder. +``` +python -m scripts.animate --config configs/prompts/1-ToonYou.yaml +python -m scripts.animate --config configs/prompts/2-Lyriel.yaml +python -m scripts.animate --config configs/prompts/3-RcnzCartoon.yaml +python -m scripts.animate --config configs/prompts/4-MajicMix.yaml +python -m scripts.animate --config configs/prompts/5-RealisticVision.yaml +python -m scripts.animate --config configs/prompts/6-Tusun.yaml +python -m scripts.animate --config configs/prompts/7-FilmVelvia.yaml +python -m scripts.animate --config configs/prompts/8-GhibliBackground.yaml +``` + +To generate animations with a new DreamBooth/LoRA model, you may create a new config `.yaml` file in the following format: +``` +NewModel: + inference_config: "[path to motion module config file]" + + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + motion_module_lora_configs: + - path: "[path to MotionLoRA model]" + alpha: 1.0 + - ... + + dreambooth_path: "[path to your DreamBooth model .safetensors file]" + lora_model_path: "[path to your LoRA model .safetensors file, leave it empty string if not needed]" + + steps: 25 + guidance_scale: 7.5 + + prompt: + - "[positive prompt]" + + n_prompt: + - "[negative prompt]" +``` +Then run the following commands: +``` +python -m scripts.animate --config [path to the config file] +``` + + +## Steps for Training + +### Dataset +Before training, download the videos files and the `.csv` annotations of [WebVid10M](https://maxbain.com/webvid-dataset/) to the local mechine. +Note that our examplar training script requires all the videos to be saved in a single folder. You may change this by modifying `animatediff/data/dataset.py`. + +### Configuration +After dataset preparations, update the below data paths in the config `.yaml` files in `configs/training/` folder: +``` +train_data: + csv_path: [Replace with .csv Annotation File Path] + video_folder: [Replace with Video Folder Path] + sample_size: 256 +``` +Other training parameters (lr, epochs, validation settings, etc.) are also included in the config files. + +### Training +To train motion modules +``` +torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/training.yaml +``` + +To finetune the unet's image layers +``` +torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/image_finetune.yaml +``` + diff --git a/__assets__/docs/gallery.md b/__assets__/docs/gallery.md new file mode 100644 index 0000000000000000000000000000000000000000..8891dd26f2c97b2524b14735e4cb045b72768bc5 --- /dev/null +++ b/__assets__/docs/gallery.md @@ -0,0 +1,93 @@ +# Gallery +Here we demonstrate several best results we found in our experiments. + + + + + + + + +
+

Model：ToonYou

+ + + + + + + + +
+

Model：Counterfeit V3.0

+ + + + + + + + +
+

Model：Realistic Vision V2.0

+ + + + + + + + +
+

Model： majicMIX Realistic

+ + + + + + + + +
+

Model：RCNZ Cartoon

+ + + + + + + + +
+

Model：FilmVelvia

+ +#### Community Cases +Here are some samples contributed by the community artists. Create a Pull Request if you would like to show your results here😚. + + + + + + + + + +
+

+Character Model：Yoimiya +(with an initial reference image, see WIP fork for the extended implementation.) + + + + + + + + + +
+

+Character Model：Paimon; +Pose Model：Hold Sign

+ + diff --git a/__assets__/figs/gradio.jpg b/__assets__/figs/gradio.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19aea7c9ca94120e54602c775b07c4b9ac089b80 Binary files /dev/null and b/__assets__/figs/gradio.jpg differ diff --git a/animatediff/data/__pycache__/dataset.cpython-310.pyc b/animatediff/data/__pycache__/dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ad18c103d4074bf71905bf9afef5a963640e4aea Binary files /dev/null and b/animatediff/data/__pycache__/dataset.cpython-310.pyc differ diff --git a/animatediff/data/dataset.py b/animatediff/data/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..6d173293a3868a69adf12ca9f2e0051b6ba1eda0 --- /dev/null +++ b/animatediff/data/dataset.py @@ -0,0 +1,194 @@ +import decord +decord.bridge.set_bridge('torch') + +import os, io, csv, math, random +import numpy as np +from einops import rearrange + +import torch +import torchvision.transforms as transforms +from torch.utils.data.dataset import Dataset +from animatediff.utils.util import zero_rank_print + + + +class WebVid10M(Dataset): + def __init__( + self, + csv_path, video_folder, + sample_size=256, sample_stride=4, sample_n_frames=16, + is_image=False, + ): + zero_rank_print(f"loading annotations from {csv_path} ...") + with open(csv_path, 'r') as csvfile: + self.dataset = list(csv.DictReader(csvfile)) + self.length = len(self.dataset) + zero_rank_print(f"data scale: {self.length}") + + self.video_folder = video_folder + self.sample_stride = sample_stride + self.sample_n_frames = sample_n_frames + self.is_image = is_image + + sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size) + self.pixel_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.Resize(sample_size[0]), + transforms.CenterCrop(sample_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + def get_batch(self, idx): + video_dict = self.dataset[idx] + videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir'] + + video_dir = os.path.join(self.video_folder, f"{videoid}.mp4") + video_reader = decord.VideoReader(video_dir) + video_length = len(video_reader) + + if not self.is_image: + clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1) + start_idx = random.randint(0, video_length - clip_length) + batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int) + else: + batch_index = [random.randint(0, video_length - 1)] + + pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous() + pixel_values = pixel_values / 255. + del video_reader + + if self.is_image: + pixel_values = pixel_values[0] + + return pixel_values, name + + def __len__(self): + return self.length + + def __getitem__(self, idx): + while True: + try: + pixel_values, name = self.get_batch(idx) + break + + except Exception as e: + idx = random.randint(0, self.length-1) + + pixel_values = self.pixel_transforms(pixel_values) + sample = dict(pixel_values=pixel_values, text=name) + return sample + + +# implement the same dataset but use the first frames of the video instead of random frames +class ImgSeqDataset(Dataset): + def __init__( + self, + csv_path, video_folder, + sample_size=256, sample_stride=4, sample_n_frames=16, + is_image=False, + ): + zero_rank_print(f"loading annotations from {csv_path} ...") + with open(csv_path, 'r') as csvfile: + self.dataset = list(csv.DictReader(csvfile)) + self.length = len(self.dataset) + zero_rank_print(f"data scale: {self.length}") + + self.video_folder = video_folder + self.sample_stride = sample_stride + self.sample_n_frames = sample_n_frames + self.is_image = is_image + self.prompt = [video_dict['name'] for video_dict in self.dataset] + self.prompt_ids = [None] + + self.width = sample_size + self.height = sample_size + + sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size) + + self.pixel_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.Resize(sample_size[0]), + transforms.CenterCrop(sample_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + def get_batch(self, idx): + video_dict = self.dataset[idx] + videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir'] + + video_dir = os.path.join(self.video_folder, f"{videoid}.mp4") + video_reader = decord.VideoReader(video_dir) + video_length = len(video_reader) + + if not self.is_image: + clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1) + start_idx = 0 + batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int) + else: + batch_index = [random.randint(0, video_length - 1)] + + pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous() + pixel_values = pixel_values / 255. + del video_reader + + if self.is_image: + pixel_values = pixel_values[0] + + return pixel_values, name + + def __len__(self): + return self.length + + def __getitem__(self, idx): + + if not self.is_image: + video_dict = self.dataset[idx] + videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir'] + + video_dir = os.path.join(self.video_folder, f"{videoid}.mp4") + # load and sample video frames + vr = decord.VideoReader(video_dir, width=self.width, height=self.height) + sample_index = list(range(0, len(vr), 1))[:self.sample_n_frames] + video = vr.get_batch(sample_index) + video = rearrange(video, "f h w c -> f c h w") + + example = { + "pixel_values": (video / 127.5 - 1.0), + "prompt_ids": self.prompt_ids[idx] + } + + return example + + while True: + try: + pixel_values, name = self.get_batch(idx) + break + + except Exception as e: + idx = random.randint(0, self.length-1) + + pixel_values = self.pixel_transforms(pixel_values) + sample = dict(pixel_values=pixel_values, text=name) + return sample + + + + +if __name__ == "__main__": + from animatediff.utils.util import save_videos_grid + + dataset = WebVid10M( + csv_path="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/results_2M_val.csv", + video_folder="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/2M_val", + sample_size=256, + sample_stride=4, sample_n_frames=16, + is_image=True, + ) + import pdb + pdb.set_trace() + + dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=16,) + for idx, batch in enumerate(dataloader): + print(batch["pixel_values"].shape, len(batch["text"])) + # for i in range(batch["pixel_values"].shape[0]): + # save_videos_grid(batch["pixel_values"][i:i+1].permute(0,2,1,3,4), os.path.join(".", f"{idx}-{i}.mp4"), rescale=True) diff --git a/animatediff/models/__pycache__/attention.cpython-310.pyc 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b/animatediff/models/__pycache__/unet.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1048928140ef09b2f2699fd513cd6698643d5515 Binary files /dev/null and b/animatediff/models/__pycache__/unet.cpython-310.pyc differ diff --git a/animatediff/models/__pycache__/unet_blocks.cpython-310.pyc b/animatediff/models/__pycache__/unet_blocks.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..70b66d61446b2570d88843b52a72cdd1b94e1349 Binary files /dev/null and b/animatediff/models/__pycache__/unet_blocks.cpython-310.pyc differ diff --git a/animatediff/models/attention.py b/animatediff/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..33fa083e237cdf3749f441ac2504161e1654134c --- /dev/null +++ b/animatediff/models/attention.py @@ -0,0 +1,300 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py + +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm + +from einops import rearrange, repeat +import pdb + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +class Transformer3DModel(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): + # Input + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length) + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length + ) + + # Output + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + ) + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + ) + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + + unet_use_cross_frame_attention = None, + unet_use_temporal_attention = None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.unet_use_cross_frame_attention = unet_use_cross_frame_attention + self.unet_use_temporal_attention = unet_use_temporal_attention + + # SC-Attn + assert unet_use_cross_frame_attention is not None + if unet_use_cross_frame_attention: + self.attn1 = SparseCausalAttention2D( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + else: + self.attn1 = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + # Cross-Attn + if cross_attention_dim is not None: + self.attn2 = CrossAttention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + else: + self.attn2 = None + + if cross_attention_dim is not None: + self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + else: + self.norm2 = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + + # Temp-Attn + assert unet_use_temporal_attention is not None + if unet_use_temporal_attention: + self.attn_temp = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): + if not is_xformers_available(): + print("Here is how to install it") + raise ModuleNotFoundError( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers", + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" + " available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + if self.attn2 is not None: + self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None): + # SparseCausal-Attention + norm_hidden_states = ( + self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) + ) + + # if self.only_cross_attention: + # hidden_states = ( + # self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states + # ) + # else: + # hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states + + # pdb.set_trace() + if self.unet_use_cross_frame_attention: + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states + else: + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states + + if self.attn2 is not None: + # Cross-Attention + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + if self.unet_use_temporal_attention: + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states) + ) + hidden_states = self.attn_temp(norm_hidden_states) + hidden_states + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states diff --git a/animatediff/models/motion_module.py b/animatediff/models/motion_module.py new file mode 100644 index 0000000000000000000000000000000000000000..75df562c65b88ab595c01de4e5f7b15c4b7d487b --- /dev/null +++ b/animatediff/models/motion_module.py @@ -0,0 +1,331 @@ +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import numpy as np +import torch.nn.functional as F +from torch import nn +import torchvision + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from diffusers.models.attention import CrossAttention, FeedForward + +from einops import rearrange, repeat +import math + + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +def get_motion_module( + in_channels, + motion_module_type: str, + motion_module_kwargs: dict +): + if motion_module_type == "Vanilla": + return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads = 8, + num_transformer_block = 2, + attention_block_types =( "Temporal_Self", "Temporal_Self" ), + cross_frame_attention_mode = None, + temporal_position_encoding = False, + temporal_position_encoding_max_len = 24, + temporal_attention_dim_div = 1, + zero_initialize = True, + ): + super().__init__() + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out) + + def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None): + hidden_states = input_tensor + hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask) + + output = hidden_states + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + + num_layers, + attention_block_types = ( "Temporal_Self", "Temporal_Self", ), + dropout = 0.0, + norm_num_groups = 32, + cross_attention_dim = 768, + activation_fn = "geglu", + attention_bias = False, + upcast_attention = False, + + cross_frame_attention_mode = None, + temporal_position_encoding = False, + temporal_position_encoding_max_len = 24, + ): + super().__init__() + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length) + + # output + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + + return output + + +class TemporalTransformerBlock(nn.Module): + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types = ( "Temporal_Self", "Temporal_Self", ), + dropout = 0.0, + norm_num_groups = 32, + cross_attention_dim = 768, + activation_fn = "geglu", + attention_bias = False, + upcast_attention = False, + cross_frame_attention_mode = None, + temporal_position_encoding = False, + temporal_position_encoding_max_len = 24, + ): + super().__init__() + + attention_blocks = [] + norms = [] + + for block_name in attention_block_types: + attention_blocks.append( + VersatileAttention( + attention_mode=block_name.split("_")[0], + cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None, + + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + for attention_block, norm in zip(self.attention_blocks, self.norms): + norm_hidden_states = norm(hidden_states) + hidden_states = attention_block( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None, + video_length=video_length, + ) + hidden_states + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states + return output + + +class PositionalEncoding(nn.Module): + def __init__( + self, + d_model, + dropout = 0., + max_len = 24 + ): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer('pe', pe) + + def forward(self, x): + x = x + self.pe[:, :x.size(1)] + return self.dropout(x) + + +class VersatileAttention(CrossAttention): + def __init__( + self, + attention_mode = None, + cross_frame_attention_mode = None, + temporal_position_encoding = False, + temporal_position_encoding_max_len = 24, + *args, **kwargs + ): + super().__init__(*args, **kwargs) + assert attention_mode == "Temporal" + + self.attention_mode = attention_mode + self.is_cross_attention = kwargs["cross_attention_dim"] is not None + + self.pos_encoder = PositionalEncoding( + kwargs["query_dim"], + dropout=0., + max_len=temporal_position_encoding_max_len + ) if (temporal_position_encoding and attention_mode == "Temporal") else None + + def extra_repr(self): + return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + batch_size, sequence_length, _ = hidden_states.shape + + if self.attention_mode == "Temporal": + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + + encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states + else: + raise NotImplementedError + + encoder_hidden_states = encoder_hidden_states + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + dim = query.shape[-1] + query = self.reshape_heads_to_batch_dim(query) + + if self.added_kv_proj_dim is not None: + raise NotImplementedError + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # attention, what we cannot get enough of + if self._use_memory_efficient_attention_xformers: + hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # Some versions of xformers return output in fp32, cast it back to the dtype of the input + hidden_states = hidden_states.to(query.dtype) + else: + if self._slice_size is None or query.shape[0] // self._slice_size == 1: + hidden_states = self._attention(query, key, value, attention_mask) + else: + hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + + if self.attention_mode == "Temporal": + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states diff --git a/animatediff/models/resnet.py b/animatediff/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..da80f17449ac10e4767c10fc6d715abb6d4c249f --- /dev/null +++ b/animatediff/models/resnet.py @@ -0,0 +1,217 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + # if self.use_conv: + # if self.name == "conv": + # hidden_states = self.conv(hidden_states) + # else: + # hidden_states = self.Conv2d_0(hidden_states) + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + raise NotImplementedError + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + use_inflated_groupnorm=None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + assert use_inflated_groupnorm != None + if use_inflated_groupnorm: + self.norm1 = InflatedGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + else: + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + + self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + time_emb_proj_out_channels = out_channels + elif self.time_embedding_norm == "scale_shift": + time_emb_proj_out_channels = out_channels * 2 + else: + raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") + + self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) + else: + self.time_emb_proj = None + + if use_inflated_groupnorm: + self.norm2 = InflatedGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + else: + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) \ No newline at end of file diff --git a/animatediff/models/unet.py b/animatediff/models/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..f6b200f103d07069dad5df7dca31c345db91452c --- /dev/null +++ b/animatediff/models/unet.py @@ -0,0 +1,497 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py + +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import os +import json +import pdb + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers import ModelMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from .unet_blocks import ( + CrossAttnDownBlock3D, + CrossAttnUpBlock3D, + DownBlock3D, + UNetMidBlock3DCrossAttn, + UpBlock3D, + get_down_block, + get_up_block, +) +from .resnet import InflatedConv3d, InflatedGroupNorm + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D" + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + + use_inflated_groupnorm=False, + + # Additional + use_motion_module = False, + motion_module_resolutions = ( 1,2,4,8 ), + motion_module_mid_block = False, + motion_module_decoder_only = False, + motion_module_type = None, + motion_module_kwargs = {}, + unet_use_cross_frame_attention = None, + unet_use_temporal_attention = None, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2 ** i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module and motion_module_mid_block, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) + else: + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states) + + down_block_res_samples += res_samples + + # mid + sample = self.mid_block( + sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None): + if subfolder is not None: + pretrained_model_path = os.path.join(pretrained_model_path, subfolder) + print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...") + + config_file = os.path.join(pretrained_model_path, 'config.json') + if not os.path.isfile(config_file): + raise RuntimeError(f"{config_file} does not exist") + with open(config_file, "r") as f: + config = json.load(f) + config["_class_name"] = cls.__name__ + config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D" + ] + config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D" + ] + + from diffusers.utils import WEIGHTS_NAME + model = cls.from_config(config, **unet_additional_kwargs) + model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME) + if not os.path.isfile(model_file): + raise RuntimeError(f"{model_file} does not exist") + state_dict = torch.load(model_file, map_location="cpu") + + m, u = model.load_state_dict(state_dict, strict=False) + print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};") + # print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n") + + params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()] + print(f"### Temporal Module Parameters: {sum(params) / 1e6} M") + + return model diff --git a/animatediff/models/unet_blocks.py b/animatediff/models/unet_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..711ad6cc28dce2c6d5f0bf4b2609b3c3c29e96c5 --- /dev/null +++ b/animatediff/models/unet_blocks.py @@ -0,0 +1,760 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py + +import torch +from torch import nn + +from .attention import Transformer3DModel +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D +from .motion_module import get_motion_module + +import pdb + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + + use_motion_module=None, + + motion_module_type=None, + motion_module_kwargs=None, +): + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, +): + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + + use_motion_module=None, + + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ] + attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=in_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) if use_motion_module else None + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet, motion_module in zip(self.attentions, self.resnets[1:], self.motion_modules): + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + + use_motion_module=None, + + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) if use_motion_module else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + output_states = () + + for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + # add motion module + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + + use_inflated_groupnorm=None, + + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) if use_motion_module else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + output_states = () + + for resnet, motion_module in zip(self.resnets, self.motion_modules): + if self.training and self.gradient_checkpointing: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states) + else: + hidden_states = resnet(hidden_states, temb) + + # add motion module + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + + use_motion_module=None, + + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) if use_motion_module else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + attention_mask=None, + ): + for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + # add motion module + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + + use_inflated_groupnorm=None, + + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) if use_motion_module else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, encoder_hidden_states=None,): + for resnet, motion_module in zip(self.resnets, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/animatediff/pipelines/__pycache__/pipeline_animation.cpython-310.pyc b/animatediff/pipelines/__pycache__/pipeline_animation.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b712a4a913cad1c255ef758f5622f005e31f9ac0 Binary files /dev/null and b/animatediff/pipelines/__pycache__/pipeline_animation.cpython-310.pyc differ diff --git a/animatediff/pipelines/pipeline_animation.py b/animatediff/pipelines/pipeline_animation.py new file mode 100644 index 0000000000000000000000000000000000000000..cf815b1327cfb67f49bb2c8f3fb150332892ce1c --- /dev/null +++ b/animatediff/pipelines/pipeline_animation.py @@ -0,0 +1,428 @@ +# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py + +import inspect +from typing import Callable, List, Optional, Union +from dataclasses import dataclass + +import numpy as np +import torch +from tqdm import tqdm + +from diffusers.utils import is_accelerate_available +from packaging import version +from transformers import CLIPTextModel, CLIPTokenizer + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers import DiffusionPipeline +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import deprecate, logging, BaseOutput + +from einops import rearrange + +from ..models.unet import UNet3DConditionModel + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class AnimationPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class AnimationPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet3DConditionModel, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + text_embeddings = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + text_embeddings = text_embeddings[0] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) + text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + uncond_embeddings = uncond_embeddings[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + # video = self.vae.decode(latents).sample + video = [] + for frame_idx in tqdm(range(latents.shape[0])): + video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample) + video = torch.cat(video) + video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) + video = (video / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + video = video.cpu().float().numpy() + return video + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, prompt, height, width, callback_steps): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + if latents is None: + rand_device = "cpu" if device.type == "mps" else device + + if isinstance(generator, list): + shape = shape + # shape = (1,) + shape[1:] + latents = [ + torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) + for i in range(batch_size) + ] + latents = torch.cat(latents, dim=0).to(device) + else: + latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) + else: + if latents.shape != shape: + raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]], + video_length: Optional[int], + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_videos_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + **kwargs, + ): + # Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # Check inputs. Raise error if not correct + self.check_inputs(prompt, height, width, callback_steps) + + # Define call parameters + # batch_size = 1 if isinstance(prompt, str) else len(prompt) + batch_size = 1 + if latents is not None: + batch_size = latents.shape[0] + if isinstance(prompt, list): + batch_size = len(prompt) + + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # Encode input prompt + prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size + if negative_prompt is not None: + negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size + text_embeddings = self._encode_prompt( + prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt + ) + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # Prepare latent variables + num_channels_latents = self.unet.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_channels_latents, + video_length, + height, + width, + text_embeddings.dtype, + device, + generator, + latents, + ) + latents_dtype = latents.dtype + + # Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype) + # noise_pred = [] + # import pdb + # pdb.set_trace() + # for batch_idx in range(latent_model_input.shape[0]): + # noise_pred_single = self.unet(latent_model_input[batch_idx:batch_idx+1], t, encoder_hidden_states=text_embeddings[batch_idx:batch_idx+1]).sample.to(dtype=latents_dtype) + # noise_pred.append(noise_pred_single) + # noise_pred = torch.cat(noise_pred) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + # Post-processing + video = self.decode_latents(latents) + + # Convert to tensor + if output_type == "tensor": + video = torch.from_numpy(video) + + if not return_dict: + return video + + return AnimationPipelineOutput(videos=video) diff --git a/animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc b/animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..44de3da781a22beca8442218205c9137696af854 Binary files /dev/null and b/animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc differ diff --git a/animatediff/utils/__pycache__/convert_lora_safetensor_to_diffusers.cpython-310.pyc b/animatediff/utils/__pycache__/convert_lora_safetensor_to_diffusers.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6664f1b32fb7aeb3bc407b01a7a306ea3738332 Binary files /dev/null and b/animatediff/utils/__pycache__/convert_lora_safetensor_to_diffusers.cpython-310.pyc differ diff --git a/animatediff/utils/__pycache__/util.cpython-310.pyc b/animatediff/utils/__pycache__/util.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..30abbec4231ee1a16a92ac735c665de73ab372d3 Binary files /dev/null and b/animatediff/utils/__pycache__/util.cpython-310.pyc differ diff --git a/animatediff/utils/convert_from_ckpt.py b/animatediff/utils/convert_from_ckpt.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee269d85987913cc97cd9985f0644b61dbdb5b4 --- /dev/null +++ b/animatediff/utils/convert_from_ckpt.py @@ -0,0 +1,959 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Conversion script for the Stable Diffusion checkpoints.""" + +import re +from io import BytesIO +from typing import Optional + +import requests +import torch +from transformers import ( + AutoFeatureExtractor, + BertTokenizerFast, + CLIPImageProcessor, + CLIPTextModel, + CLIPTextModelWithProjection, + CLIPTokenizer, + CLIPVisionConfig, + CLIPVisionModelWithProjection, +) + +from diffusers.models import ( + AutoencoderKL, + PriorTransformer, + UNet2DConditionModel, +) +from diffusers.schedulers import ( + DDIMScheduler, + DDPMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, + UnCLIPScheduler, +) +from diffusers.utils.import_utils import BACKENDS_MAPPING + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "query.weight") + new_item = new_item.replace("q.bias", "query.bias") + + new_item = new_item.replace("k.weight", "key.weight") + new_item = new_item.replace("k.bias", "key.bias") + + new_item = new_item.replace("v.weight", "value.weight") + new_item = new_item.replace("v.bias", "value.bias") + + new_item = new_item.replace("proj_out.weight", "proj_attn.weight") + new_item = new_item.replace("proj_out.bias", "proj_attn.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + if controlnet: + unet_params = original_config.model.params.control_stage_config.params + else: + unet_params = original_config.model.params.unet_config.params + + vae_params = original_config.model.params.first_stage_config.params.ddconfig + + block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) + + head_dim = unet_params.num_heads if "num_heads" in unet_params else None + use_linear_projection = ( + unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim = [5, 10, 20, 20] + + class_embed_type = None + projection_class_embeddings_input_dim = None + + if "num_classes" in unet_params: + if unet_params.num_classes == "sequential": + class_embed_type = "projection" + assert "adm_in_channels" in unet_params + projection_class_embeddings_input_dim = unet_params.adm_in_channels + else: + raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}") + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + } + + if not controlnet: + config["out_channels"] = unet_params.out_channels + config["up_block_types"] = tuple(up_block_types) + + return config + + +def create_vae_diffusers_config(original_config, image_size: int): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + vae_params = original_config.model.params.first_stage_config.params.ddconfig + _ = original_config.model.params.first_stage_config.params.embed_dim + + block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = { + "sample_size": image_size, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } + return config + + +def create_diffusers_schedular(original_config): + schedular = DDIMScheduler( + num_train_timesteps=original_config.model.params.timesteps, + beta_start=original_config.model.params.linear_start, + beta_end=original_config.model.params.linear_end, + beta_schedule="scaled_linear", + ) + return schedular + + +def create_ldm_bert_config(original_config): + bert_params = original_config.model.parms.cond_stage_config.params + config = LDMBertConfig( + d_model=bert_params.n_embed, + encoder_layers=bert_params.n_layer, + encoder_ffn_dim=bert_params.n_embed * 4, + ) + return config + + +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False, controlnet=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + if controlnet: + unet_key = "control_model." + else: + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + if not controlnet: + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + if controlnet: + # conditioning embedding + + orig_index = 0 + + new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + orig_index += 2 + + diffusers_index = 0 + + while diffusers_index < 6: + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + diffusers_index += 1 + orig_index += 2 + + new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + # down blocks + for i in range(num_input_blocks): + new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") + new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") + + # mid block + new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") + new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") + + return new_checkpoint + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +def convert_ldm_bert_checkpoint(checkpoint, config): + def _copy_attn_layer(hf_attn_layer, pt_attn_layer): + hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight + hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight + hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight + + hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight + hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias + + def _copy_linear(hf_linear, pt_linear): + hf_linear.weight = pt_linear.weight + hf_linear.bias = pt_linear.bias + + def _copy_layer(hf_layer, pt_layer): + # copy layer norms + _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0]) + _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0]) + + # copy attn + _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1]) + + # copy MLP + pt_mlp = pt_layer[1][1] + _copy_linear(hf_layer.fc1, pt_mlp.net[0][0]) + _copy_linear(hf_layer.fc2, pt_mlp.net[2]) + + def _copy_layers(hf_layers, pt_layers): + for i, hf_layer in enumerate(hf_layers): + if i != 0: + i += i + pt_layer = pt_layers[i : i + 2] + _copy_layer(hf_layer, pt_layer) + + hf_model = LDMBertModel(config).eval() + + # copy embeds + hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight + hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight + + # copy layer norm + _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm) + + # copy hidden layers + _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers) + + _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits) + + return hf_model + + +def convert_ldm_clip_checkpoint(checkpoint): + text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") + keys = list(checkpoint.keys()) + + text_model_dict = {} + + for key in keys: + if key.startswith("cond_stage_model.transformer"): + text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] + + text_model.load_state_dict(text_model_dict) + + return text_model + + +textenc_conversion_lst = [ + ("cond_stage_model.model.positional_embedding", "text_model.embeddings.position_embedding.weight"), + ("cond_stage_model.model.token_embedding.weight", "text_model.embeddings.token_embedding.weight"), + ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"), + ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"), +] +textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst} + +textenc_transformer_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + + +def convert_paint_by_example_checkpoint(checkpoint): + config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14") + model = PaintByExampleImageEncoder(config) + + keys = list(checkpoint.keys()) + + text_model_dict = {} + + for key in keys: + if key.startswith("cond_stage_model.transformer"): + text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] + + # load clip vision + model.model.load_state_dict(text_model_dict) + + # load mapper + keys_mapper = { + k[len("cond_stage_model.mapper.res") :]: v + for k, v in checkpoint.items() + if k.startswith("cond_stage_model.mapper") + } + + MAPPING = { + "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"], + "attn.c_proj": ["attn1.to_out.0"], + "ln_1": ["norm1"], + "ln_2": ["norm3"], + "mlp.c_fc": ["ff.net.0.proj"], + "mlp.c_proj": ["ff.net.2"], + } + + mapped_weights = {} + for key, value in keys_mapper.items(): + prefix = key[: len("blocks.i")] + suffix = key.split(prefix)[-1].split(".")[-1] + name = key.split(prefix)[-1].split(suffix)[0][1:-1] + mapped_names = MAPPING[name] + + num_splits = len(mapped_names) + for i, mapped_name in enumerate(mapped_names): + new_name = ".".join([prefix, mapped_name, suffix]) + shape = value.shape[0] // num_splits + mapped_weights[new_name] = value[i * shape : (i + 1) * shape] + + model.mapper.load_state_dict(mapped_weights) + + # load final layer norm + model.final_layer_norm.load_state_dict( + { + "bias": checkpoint["cond_stage_model.final_ln.bias"], + "weight": checkpoint["cond_stage_model.final_ln.weight"], + } + ) + + # load final proj + model.proj_out.load_state_dict( + { + "bias": checkpoint["proj_out.bias"], + "weight": checkpoint["proj_out.weight"], + } + ) + + # load uncond vector + model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"]) + return model + + +def convert_open_clip_checkpoint(checkpoint): + text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder") + + keys = list(checkpoint.keys()) + + text_model_dict = {} + + if "cond_stage_model.model.text_projection" in checkpoint: + d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) + else: + d_model = 1024 + + text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids") + + for key in keys: + if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer + continue + if key in textenc_conversion_map: + text_model_dict[textenc_conversion_map[key]] = checkpoint[key] + if key.startswith("cond_stage_model.model.transformer."): + new_key = key[len("cond_stage_model.model.transformer.") :] + if new_key.endswith(".in_proj_weight"): + new_key = new_key[: -len(".in_proj_weight")] + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] + text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] + text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] + elif new_key.endswith(".in_proj_bias"): + new_key = new_key[: -len(".in_proj_bias")] + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] + text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] + text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] + else: + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + + text_model_dict[new_key] = checkpoint[key] + + text_model.load_state_dict(text_model_dict) + + return text_model + + +def stable_unclip_image_encoder(original_config): + """ + Returns the image processor and clip image encoder for the img2img unclip pipeline. + + We currently know of two types of stable unclip models which separately use the clip and the openclip image + encoders. + """ + + image_embedder_config = original_config.model.params.embedder_config + + sd_clip_image_embedder_class = image_embedder_config.target + sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1] + + if sd_clip_image_embedder_class == "ClipImageEmbedder": + clip_model_name = image_embedder_config.params.model + + if clip_model_name == "ViT-L/14": + feature_extractor = CLIPImageProcessor() + image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + else: + raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}") + + elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder": + feature_extractor = CLIPImageProcessor() + image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + else: + raise NotImplementedError( + f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}" + ) + + return feature_extractor, image_encoder + + +def stable_unclip_image_noising_components( + original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None +): + """ + Returns the noising components for the img2img and txt2img unclip pipelines. + + Converts the stability noise augmentor into + 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats + 2. a `DDPMScheduler` for holding the noise schedule + + If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided. + """ + noise_aug_config = original_config.model.params.noise_aug_config + noise_aug_class = noise_aug_config.target + noise_aug_class = noise_aug_class.split(".")[-1] + + if noise_aug_class == "CLIPEmbeddingNoiseAugmentation": + noise_aug_config = noise_aug_config.params + embedding_dim = noise_aug_config.timestep_dim + max_noise_level = noise_aug_config.noise_schedule_config.timesteps + beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule + + image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim) + image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule) + + if "clip_stats_path" in noise_aug_config: + if clip_stats_path is None: + raise ValueError("This stable unclip config requires a `clip_stats_path`") + + clip_mean, clip_std = torch.load(clip_stats_path, map_location=device) + clip_mean = clip_mean[None, :] + clip_std = clip_std[None, :] + + clip_stats_state_dict = { + "mean": clip_mean, + "std": clip_std, + } + + image_normalizer.load_state_dict(clip_stats_state_dict) + else: + raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}") + + return image_normalizer, image_noising_scheduler + + +def convert_controlnet_checkpoint( + checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema +): + ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True) + ctrlnet_config["upcast_attention"] = upcast_attention + + ctrlnet_config.pop("sample_size") + + controlnet_model = ControlNetModel(**ctrlnet_config) + + converted_ctrl_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, ctrlnet_config, path=checkpoint_path, extract_ema=extract_ema, controlnet=True + ) + + controlnet_model.load_state_dict(converted_ctrl_checkpoint) + + return controlnet_model diff --git a/animatediff/utils/convert_lora_safetensor_to_diffusers.py b/animatediff/utils/convert_lora_safetensor_to_diffusers.py new file mode 100644 index 0000000000000000000000000000000000000000..7490e38ecfc2a00d90bb97205f32546384443aee --- /dev/null +++ b/animatediff/utils/convert_lora_safetensor_to_diffusers.py @@ -0,0 +1,154 @@ +# coding=utf-8 +# Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" Conversion script for the LoRA's safetensors checkpoints. """ + +import argparse + +import torch +from safetensors.torch import load_file + +from diffusers import StableDiffusionPipeline +import pdb + + + +def convert_motion_lora_ckpt_to_diffusers(pipeline, state_dict, alpha=1.0): + # directly update weight in diffusers model + for key in state_dict: + # only process lora down key + if "up." in key: continue + + up_key = key.replace(".down.", ".up.") + model_key = key.replace("processor.", "").replace("_lora", "").replace("down.", "").replace("up.", "") + model_key = model_key.replace("to_out.", "to_out.0.") + layer_infos = model_key.split(".")[:-1] + + curr_layer = pipeline.unet + while len(layer_infos) > 0: + temp_name = layer_infos.pop(0) + curr_layer = curr_layer.__getattr__(temp_name) + + weight_down = state_dict[key] + weight_up = state_dict[up_key] + curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) + + return pipeline + + + +def convert_lora(pipeline, state_dict, LORA_PREFIX_UNET="lora_unet", LORA_PREFIX_TEXT_ENCODER="lora_te", alpha=0.6): + # load base model + # pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32) + + # load LoRA weight from .safetensors + # state_dict = load_file(checkpoint_path) + + visited = [] + + # directly update weight in diffusers model + for key in state_dict: + # it is suggested to print out the key, it usually will be something like below + # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" + + # as we have set the alpha beforehand, so just skip + if ".alpha" in key or key in visited: + continue + + if "text" in key: + layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") + curr_layer = pipeline.text_encoder + else: + layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_") + curr_layer = pipeline.unet + + # find the target layer + temp_name = layer_infos.pop(0) + while len(layer_infos) > -1: + try: + curr_layer = curr_layer.__getattr__(temp_name) + if len(layer_infos) > 0: + temp_name = layer_infos.pop(0) + elif len(layer_infos) == 0: + break + except Exception: + if len(temp_name) > 0: + temp_name += "_" + layer_infos.pop(0) + else: + temp_name = layer_infos.pop(0) + + pair_keys = [] + if "lora_down" in key: + pair_keys.append(key.replace("lora_down", "lora_up")) + pair_keys.append(key) + else: + pair_keys.append(key) + pair_keys.append(key.replace("lora_up", "lora_down")) + + # update weight + if len(state_dict[pair_keys[0]].shape) == 4: + weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) + weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) + curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(curr_layer.weight.data.device) + else: + weight_up = state_dict[pair_keys[0]].to(torch.float32) + weight_down = state_dict[pair_keys[1]].to(torch.float32) + curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) + + # update visited list + for item in pair_keys: + visited.append(item) + + return pipeline + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format." + ) + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument( + "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors" + ) + parser.add_argument( + "--lora_prefix_text_encoder", + default="lora_te", + type=str, + help="The prefix of text encoder weight in safetensors", + ) + parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW") + parser.add_argument( + "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not." + ) + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + + args = parser.parse_args() + + base_model_path = args.base_model_path + checkpoint_path = args.checkpoint_path + dump_path = args.dump_path + lora_prefix_unet = args.lora_prefix_unet + lora_prefix_text_encoder = args.lora_prefix_text_encoder + alpha = args.alpha + + pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) + + pipe = pipe.to(args.device) + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/animatediff/utils/util.py b/animatediff/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..539338520aca337f7bedd28cc2870dd2aec12678 --- /dev/null +++ b/animatediff/utils/util.py @@ -0,0 +1,157 @@ +import os +import imageio +import numpy as np +from typing import Union + +import torch +import torchvision +import torch.distributed as dist + +from safetensors import safe_open +from tqdm import tqdm +from einops import rearrange +from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint +from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora, convert_motion_lora_ckpt_to_diffusers + + +def zero_rank_print(s): + if (not dist.is_initialized()) and (dist.is_initialized() and dist.get_rank() == 0): print("### " + s) + + +def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8): + videos = rearrange(videos, "b c t h w -> t b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.mimsave(path, outputs, fps=fps) + + +# DDIM Inversion +@torch.no_grad() +def init_prompt(prompt, pipeline): + uncond_input = pipeline.tokenizer( + [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, + return_tensors="pt" + ) + uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0] + text_input = pipeline.tokenizer( + [prompt], + padding="max_length", + max_length=pipeline.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] + context = torch.cat([uncond_embeddings, text_embeddings]) + + return context + + +def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, + sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler): + timestep, next_timestep = min( + timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep + alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod + alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep] + beta_prod_t = 1 - alpha_prod_t + next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 + next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output + next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction + return next_sample + + +def get_noise_pred_single(latents, t, context, unet): + noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"] + return noise_pred + + +@torch.no_grad() +def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt): + context = init_prompt(prompt, pipeline) + uncond_embeddings, cond_embeddings = context.chunk(2) + all_latent = [latent] + latent = latent.clone().detach() + for i in tqdm(range(num_inv_steps)): + t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1] + noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet) + latent = next_step(noise_pred, t, latent, ddim_scheduler) + all_latent.append(latent) + return all_latent + + +@torch.no_grad() +def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""): + ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt) + return ddim_latents + +def load_weights( + animation_pipeline, + # motion module + motion_module_path = "", + motion_module_lora_configs = [], + # image layers + dreambooth_model_path = "", + lora_model_path = "", + lora_alpha = 0.8, +): + # 1.1 motion module + unet_state_dict = {} + if motion_module_path != "": + print(f"load motion module from {motion_module_path}") + motion_module_state_dict = torch.load(motion_module_path, map_location="cpu") + motion_module_state_dict = motion_module_state_dict["state_dict"] if "state_dict" in motion_module_state_dict else motion_module_state_dict + unet_state_dict.update({name: param for name, param in motion_module_state_dict.items() if "motion_modules." in name}) + + missing, unexpected = animation_pipeline.unet.load_state_dict(unet_state_dict, strict=False) + assert len(unexpected) == 0 + del unet_state_dict + + if dreambooth_model_path != "": + print(f"load dreambooth model from {dreambooth_model_path}") + if dreambooth_model_path.endswith(".safetensors"): + dreambooth_state_dict = {} + with safe_open(dreambooth_model_path, framework="pt", device="cpu") as f: + for key in f.keys(): + dreambooth_state_dict[key] = f.get_tensor(key) + elif dreambooth_model_path.endswith(".ckpt"): + dreambooth_state_dict = torch.load(dreambooth_model_path, map_location="cpu") + + # 1. vae + converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, animation_pipeline.vae.config) + animation_pipeline.vae.load_state_dict(converted_vae_checkpoint) + # 2. unet + converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, animation_pipeline.unet.config) + animation_pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False) + # 3. text_model + animation_pipeline.text_encoder = convert_ldm_clip_checkpoint(dreambooth_state_dict) + del dreambooth_state_dict + + if lora_model_path != "": + print(f"load lora model from {lora_model_path}") + assert lora_model_path.endswith(".safetensors") + lora_state_dict = {} + with safe_open(lora_model_path, framework="pt", device="cpu") as f: + for key in f.keys(): + lora_state_dict[key] = f.get_tensor(key) + + animation_pipeline = convert_lora(animation_pipeline, lora_state_dict, alpha=lora_alpha) + del lora_state_dict + + + for motion_module_lora_config in motion_module_lora_configs: + path, alpha = motion_module_lora_config["path"], motion_module_lora_config["alpha"] + print(f"load motion LoRA from {path}") + + motion_lora_state_dict = torch.load(path, map_location="cpu") + motion_lora_state_dict = motion_lora_state_dict["state_dict"] if "state_dict" in motion_lora_state_dict else motion_lora_state_dict + + animation_pipeline = convert_motion_lora_ckpt_to_diffusers(animation_pipeline, motion_lora_state_dict, alpha) + + return animation_pipeline diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..d4df5db942a0d53597ce961cc8f00c6e29e8a188 --- /dev/null +++ b/app.py @@ -0,0 +1,328 @@ + +import os +import json +import torch +import random + +import gradio as gr +from glob import glob +from omegaconf import OmegaConf +from datetime import datetime +from safetensors import safe_open + +from diffusers import AutoencoderKL +from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler +from diffusers.utils.import_utils import is_xformers_available +from transformers import CLIPTextModel, CLIPTokenizer + +from animatediff.models.unet import UNet3DConditionModel +from animatediff.pipelines.pipeline_animation import AnimationPipeline +from animatediff.utils.util import save_videos_grid +from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint +from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora + + +sample_idx = 0 +scheduler_dict = { + "Euler": EulerDiscreteScheduler, + "PNDM": PNDMScheduler, + "DDIM": DDIMScheduler, +} + +css = """ +.toolbutton { + margin-buttom: 0em 0em 0em 0em; + max-width: 2.5em; + min-width: 2.5em !important; + height: 2.5em; +} +""" + +class AnimateController: + def __init__(self): + + # config dirs + self.basedir = os.getcwd() + self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion") + self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module") + self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA") + self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S")) + self.savedir_sample = os.path.join(self.savedir, "sample") + os.makedirs(self.savedir, exist_ok=True) + + self.stable_diffusion_list = [] + self.motion_module_list = [] + self.personalized_model_list = [] + + self.refresh_stable_diffusion() + self.refresh_motion_module() + self.refresh_personalized_model() + + # config models + self.tokenizer = None + self.text_encoder = None + self.vae = None + self.unet = None + self.pipeline = None + self.lora_model_state_dict = {} + + self.inference_config = OmegaConf.load("configs/inference/inference.yaml") + + def refresh_stable_diffusion(self): + self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/")) + + def refresh_motion_module(self): + motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt")) + self.motion_module_list = [os.path.basename(p) for p in motion_module_list] + + def refresh_personalized_model(self): + personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors")) + self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list] + + def update_stable_diffusion(self, stable_diffusion_dropdown): + self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer") + self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda() + self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda() + self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda() + return gr.Dropdown.update() + + def update_motion_module(self, motion_module_dropdown): + if self.unet is None: + gr.Info(f"Please select a pretrained model path.") + return gr.Dropdown.update(value=None) + else: + motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown) + motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu") + missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False) + assert len(unexpected) == 0 + return gr.Dropdown.update() + + def update_base_model(self, base_model_dropdown): + if self.unet is None: + gr.Info(f"Please select a pretrained model path.") + return gr.Dropdown.update(value=None) + else: + base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown) + base_model_state_dict = {} + with safe_open(base_model_dropdown, framework="pt", device="cpu") as f: + for key in f.keys(): + base_model_state_dict[key] = f.get_tensor(key) + + converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config) + self.vae.load_state_dict(converted_vae_checkpoint) + + converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config) + self.unet.load_state_dict(converted_unet_checkpoint, strict=False) + + self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict) + return gr.Dropdown.update() + + def update_lora_model(self, lora_model_dropdown): + lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown) + self.lora_model_state_dict = {} + if lora_model_dropdown == "none": pass + else: + with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f: + for key in f.keys(): + self.lora_model_state_dict[key] = f.get_tensor(key) + return gr.Dropdown.update() + + def animate( + self, + stable_diffusion_dropdown, + motion_module_dropdown, + base_model_dropdown, + lora_alpha_slider, + prompt_textbox, + negative_prompt_textbox, + sampler_dropdown, + sample_step_slider, + width_slider, + length_slider, + height_slider, + cfg_scale_slider, + seed_textbox + ): + if self.unet is None: + raise gr.Error(f"Please select a pretrained model path.") + if motion_module_dropdown == "": + raise gr.Error(f"Please select a motion module.") + if base_model_dropdown == "": + raise gr.Error(f"Please select a base DreamBooth model.") + + if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention() + + pipeline = AnimationPipeline( + vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet, + scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs)) + ).to("cuda") + + if self.lora_model_state_dict != {}: + pipeline = convert_lora(pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider) + + pipeline.to("cuda") + + if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox)) + else: torch.seed() + seed = torch.initial_seed() + + sample = pipeline( + prompt_textbox, + negative_prompt = negative_prompt_textbox, + num_inference_steps = sample_step_slider, + guidance_scale = cfg_scale_slider, + width = width_slider, + height = height_slider, + video_length = length_slider, + ).videos + + save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4") + save_videos_grid(sample, save_sample_path) + + sample_config = { + "prompt": prompt_textbox, + "n_prompt": negative_prompt_textbox, + "sampler": sampler_dropdown, + "num_inference_steps": sample_step_slider, + "guidance_scale": cfg_scale_slider, + "width": width_slider, + "height": height_slider, + "video_length": length_slider, + "seed": seed + } + json_str = json.dumps(sample_config, indent=4) + with open(os.path.join(self.savedir, "logs.json"), "a") as f: + f.write(json_str) + f.write("\n\n") + + return gr.Video.update(value=save_sample_path) + + +controller = AnimateController() + + +def ui(): + with gr.Blocks(css=css) as demo: + gr.Markdown( + """ + # [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725) + Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)
+ [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/) + """ + ) + with gr.Column(variant="panel"): + gr.Markdown( + """ + ### 1. Model checkpoints (select pretrained model path first). + """ + ) + with gr.Row(): + stable_diffusion_dropdown = gr.Dropdown( + label="Pretrained Model Path", + choices=controller.stable_diffusion_list, + interactive=True, + ) + stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown]) + + stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") + def update_stable_diffusion(): + controller.refresh_stable_diffusion() + return gr.Dropdown.update(choices=controller.stable_diffusion_list) + stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown]) + + with gr.Row(): + motion_module_dropdown = gr.Dropdown( + label="Select motion module", + choices=controller.motion_module_list, + interactive=True, + ) + motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown]) + + motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") + def update_motion_module(): + controller.refresh_motion_module() + return gr.Dropdown.update(choices=controller.motion_module_list) + motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown]) + + base_model_dropdown = gr.Dropdown( + label="Select base Dreambooth model (required)", + choices=controller.personalized_model_list, + interactive=True, + ) + base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown]) + + lora_model_dropdown = gr.Dropdown( + label="Select LoRA model (optional)", + choices=["none"] + controller.personalized_model_list, + value="none", + interactive=True, + ) + lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown]) + + lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.8, minimum=0, maximum=2, interactive=True) + + personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") + def update_personalized_model(): + controller.refresh_personalized_model() + return [ + gr.Dropdown.update(choices=controller.personalized_model_list), + gr.Dropdown.update(choices=["none"] + controller.personalized_model_list) + ] + personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown]) + + with gr.Column(variant="panel"): + gr.Markdown( + """ + ### 2. Configs for AnimateDiff. + """ + ) + + prompt_textbox = gr.Textbox(label="Prompt", lines=2) + negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2) + + with gr.Row().style(equal_height=False): + with gr.Column(): + with gr.Row(): + sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0]) + sample_step_slider = gr.Slider(label="Sampling steps", value=25, minimum=10, maximum=100, step=1) + + width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64) + height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64) + length_slider = gr.Slider(label="Animation length", value=16, minimum=8, maximum=24, step=1) + cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20) + + with gr.Row(): + seed_textbox = gr.Textbox(label="Seed", value=-1) + seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton") + seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox]) + + generate_button = gr.Button(value="Generate", variant='primary') + + result_video = gr.Video(label="Generated Animation", interactive=False) + + generate_button.click( + fn=controller.animate, + inputs=[ + stable_diffusion_dropdown, + motion_module_dropdown, + base_model_dropdown, + lora_alpha_slider, + prompt_textbox, + negative_prompt_textbox, + sampler_dropdown, + sample_step_slider, + width_slider, + length_slider, + height_slider, + cfg_scale_slider, + seed_textbox, + ], + outputs=[result_video] + ) + + return demo + + +if __name__ == "__main__": + demo = ui() + demo.launch(share=True) diff --git a/configs/inference/inference-v1.yaml b/configs/inference/inference-v1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86f377721c0637372229c024e2b5127bc6ca1604 --- /dev/null +++ b/configs/inference/inference-v1.yaml @@ -0,0 +1,26 @@ +unet_additional_kwargs: + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + motion_module_resolutions: + - 1 + - 2 + - 4 + - 8 + motion_module_mid_block: false + motion_module_decoder_only: false + motion_module_type: Vanilla + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: + - Temporal_Self + - Temporal_Self + temporal_position_encoding: true + temporal_position_encoding_max_len: 24 + temporal_attention_dim_div: 1 + +noise_scheduler_kwargs: + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" diff --git a/configs/inference/inference-v2.yaml b/configs/inference/inference-v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a33bc124f75130b1d64633c09bf5e87a63cf2b87 --- /dev/null +++ b/configs/inference/inference-v2.yaml @@ -0,0 +1,27 @@ +unet_additional_kwargs: + use_inflated_groupnorm: true + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + motion_module_resolutions: + - 1 + - 2 + - 4 + - 8 + motion_module_mid_block: true + motion_module_decoder_only: false + motion_module_type: Vanilla + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: + - Temporal_Self + - Temporal_Self + temporal_position_encoding: true + temporal_position_encoding_max_len: 32 + temporal_attention_dim_div: 1 + +noise_scheduler_kwargs: + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" diff --git a/configs/inference/inference-v3.yaml b/configs/inference/inference-v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0931f6493839755dee64ecadd799275f0a87e805 --- /dev/null +++ b/configs/inference/inference-v3.yaml @@ -0,0 +1,42 @@ +unet_additional_kwargs: + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + motion_module_resolutions: + - 1 + - 2 + - 4 + - 8 + motion_module_mid_block: false + motion_module_decoder_only: false + motion_module_type: Vanilla + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: + - Temporal_Self + - Temporal_Self + temporal_position_encoding: true + temporal_position_encoding_max_len: 24 + temporal_attention_dim_div: 1 + +noise_scheduler_kwargs: + DDIMScheduler: + num_train_timesteps: 1000 + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" + steps_offset: 1 + clip_sample: false + + EulerAncestralDiscreteScheduler: + num_train_timesteps: 1000 + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" + + KDPM2AncestralDiscreteScheduler: + num_train_timesteps: 1000 + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" diff --git a/configs/prompts/1-ToonYou.yaml b/configs/prompts/1-ToonYou.yaml new file mode 100644 index 0000000000000000000000000000000000000000..925e933607c6acde19a3f2819c9eab4835f37a87 --- /dev/null +++ b/configs/prompts/1-ToonYou.yaml @@ -0,0 +1,23 @@ +ToonYou: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/toonyou_beta3.safetensors" + lora_model_path: "" + + seed: [10788741199826055526, 6520604954829636163, 6519455744612555650, 16372571278361863751] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress" + - "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes," + - "best quality, masterpiece, 1boy, formal, abstract, looking at viewer, masculine, marble pattern" + - "best quality, masterpiece, 1girl, cloudy sky, dandelion, contrapposto, alternate hairstyle," + + n_prompt: + - "" + - "badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth" + - "" + - "" diff --git a/configs/prompts/2-Lyriel.yaml b/configs/prompts/2-Lyriel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..583b0ce34f833b6fa63d52a1ac7fec754bf97662 --- /dev/null +++ b/configs/prompts/2-Lyriel.yaml @@ -0,0 +1,23 @@ +Lyriel: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/lyriel_v16.safetensors" + lora_model_path: "" + + seed: [10917152860782582783, 6399018107401806238, 15875751942533906793, 6653196880059936551] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "dark shot, epic realistic, portrait of halo, sunglasses, blue eyes, tartan scarf, white hair by atey ghailan, by greg rutkowski, by greg tocchini, by james gilleard, by joe fenton, by kaethe butcher, gradient yellow, black, brown and magenta color scheme, grunge aesthetic!!! graffiti tag wall background, art by greg rutkowski and artgerm, soft cinematic light, adobe lightroom, photolab, hdr, intricate, highly detailed, depth of field, faded, neutral colors, hdr, muted colors, hyperdetailed, artstation, cinematic, warm lights, dramatic light, intricate details, complex background, rutkowski, teal and orange" + - "A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal" + - "dark theme, medieval portrait of a man sharp features, grim, cold stare, dark colors, Volumetric lighting, baroque oil painting by Greg Rutkowski, Artgerm, WLOP, Alphonse Mucha dynamic lighting hyperdetailed intricately detailed, hdr, muted colors, complex background, hyperrealism, hyperdetailed, amandine van ray" + - "As I have gone alone in there and with my treasures bold, I can keep my secret where and hint of riches new and old. Begin it where warm waters halt and take it in a canyon down, not far but too far to walk, put in below the home of brown." + + n_prompt: + - "3d, cartoon, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, young, loli, elf, 3d, illustration" + - "3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular" + - "dof, grayscale, black and white, bw, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular,badhandsv5-neg, By bad artist -neg 1, monochrome" + - "holding an item, cowboy, hat, cartoon, 3d, disfigured, bad art, deformed,extra limbs,close up,b&w, wierd colors, blurry, duplicate, morbid, mutilated, [out of frame], extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, out of frame, ugly, extra limbs, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, Photoshop, video game, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, mutation, mutated, extra limbs, extra legs, extra arms, disfigured, deformed, cross-eye, body out of frame, blurry, bad art, bad anatomy, 3d render" diff --git a/configs/prompts/3-RcnzCartoon.yaml b/configs/prompts/3-RcnzCartoon.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd76a2b3bc5d42d7f66e2637fa9193ea8ad67689 --- /dev/null +++ b/configs/prompts/3-RcnzCartoon.yaml @@ -0,0 +1,23 @@ +RcnzCartoon: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/rcnzCartoon3d_v10.safetensors" + lora_model_path: "" + + seed: [16931037867122267877, 2094308009433392066, 4292543217695451092, 15572665120852309890] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded" + - "close up Portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal [rust], elegant, sharp focus, photo by greg rutkowski, soft lighting, vibrant colors, masterpiece, streets, detailed face" + - "absurdres, photorealistic, masterpiece, a 30 year old man with gold framed, aviator reading glasses and a black hooded jacket and a beard, professional photo, a character portrait, altermodern, detailed eyes, detailed lips, detailed face, grey eyes" + - "a golden labrador, warm vibrant colours, natural lighting, dappled lighting, diffused lighting, absurdres, highres,k, uhd, hdr, rtx, unreal, octane render, RAW photo, photorealistic, global illumination, subsurface scattering" + + n_prompt: + - "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation" + - "nude, cross eyed, tongue, open mouth, inside, 3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, red eyes, muscular" + - "easynegative, cartoon, anime, sketches, necklace, earrings worst quality, low quality, normal quality, bad anatomy, bad hands, shiny skin, error, missing fingers, extra digit, fewer digits, jpeg artifacts, signature, watermark, username, blurry, chubby, anorectic, bad eyes, old, wrinkled skin, red skin, photograph By bad artist -neg, big eyes, muscular face," + - "beard, EasyNegative, lowres, chromatic aberration, depth of field, motion blur, blurry, bokeh, bad quality, worst quality, multiple arms, badhand" diff --git a/configs/prompts/4-MajicMix.yaml b/configs/prompts/4-MajicMix.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6680bd5cbe5ef9b025fc6d9debbb5185cb744b11 --- /dev/null +++ b/configs/prompts/4-MajicMix.yaml @@ -0,0 +1,23 @@ +MajicMix: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/majicmixRealistic_v5Preview.safetensors" + lora_model_path: "" + + seed: [1572448948722921032, 1099474677988590681, 6488833139725635347, 18339859844376517918] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic" + - "best quality, masterpiece, photorealistic, 1boy, 50 years old beard, dramatic lighting" + - "best quality, masterpiece, photorealistic, 1girl, light smile, shirt with collars, waist up, dramatic lighting, from below" + - "male, man, beard, bodybuilder, skinhead,cold face, tough guy, cowboyshot, tattoo, french windows, luxury hotel masterpiece, best quality, photorealistic" + + n_prompt: + - "ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles" + - "nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome" + - "nsfw, ng_deepnegative_v1_75t,badhandv4, worst quality, low quality, normal quality, lowres,watermark, monochrome" + - "nude, nsfw, ng_deepnegative_v1_75t, badhandv4, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, monochrome, grayscale watermark, moles, people" diff --git a/configs/prompts/5-RealisticVision.yaml b/configs/prompts/5-RealisticVision.yaml new file mode 100644 index 0000000000000000000000000000000000000000..520619cf0a52bba260b4d7a13cd3f53c38ec10e6 --- /dev/null +++ b/configs/prompts/5-RealisticVision.yaml @@ -0,0 +1,23 @@ +RealisticVision: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: [5658137986800322009, 12099779162349365895, 10499524853910852697, 16768009035333711932] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + - "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot" + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + - "night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain" + + n_prompt: + - "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + - "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, art, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" diff --git a/configs/prompts/6-Tusun.yaml b/configs/prompts/6-Tusun.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1275a2066644f51ca29eeef2b0485b2726e53611 --- /dev/null +++ b/configs/prompts/6-Tusun.yaml @@ -0,0 +1,21 @@ +Tusun: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/moonfilm_reality20.safetensors" + lora_model_path: "models/DreamBooth_LoRA/TUSUN.safetensors" + lora_alpha: 0.6 + + seed: [10154078483724687116, 2664393535095473805, 4231566096207622938, 1713349740448094493] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "tusuncub with its mouth open, blurry, open mouth, fangs, photo background, looking at viewer, tongue, full body, solo, cute and lovely, Beautiful and realistic eye details, perfect anatomy, Nonsense, pure background, Centered-Shot, realistic photo, photograph, 4k, hyper detailed, DSLR, 24 Megapixels, 8mm Lens, Full Frame, film grain, Global Illumination, studio Lighting, Award Winning Photography, diffuse reflection, ray tracing" + - "cute tusun with a blurry background, black background, simple background, signature, face, solo, cute and lovely, Beautiful and realistic eye details, perfect anatomy, Nonsense, pure background, Centered-Shot, realistic photo, photograph, 4k, hyper detailed, DSLR, 24 Megapixels, 8mm Lens, Full Frame, film grain, Global Illumination, studio Lighting, Award Winning Photography, diffuse reflection, ray tracing" + - "cut tusuncub walking in the snow, blurry, looking at viewer, depth of field, blurry background, full body, solo, cute and lovely, Beautiful and realistic eye details, perfect anatomy, Nonsense, pure background, Centered-Shot, realistic photo, photograph, 4k, hyper detailed, DSLR, 24 Megapixels, 8mm Lens, Full Frame, film grain, Global Illumination, studio Lighting, Award Winning Photography, diffuse reflection, ray tracing" + - "character design, cyberpunk tusun kitten wearing astronaut suit, sci-fic, realistic eye color and details, fluffy, big head, science fiction, communist ideology, Cyborg, fantasy, intense angle, soft lighting, photograph, 4k, hyper detailed, portrait wallpaper, realistic, photo-realistic, DSLR, 24 Megapixels, Full Frame, vibrant details, octane render, finely detail, best quality, incredibly absurdres, robotic parts, rim light, vibrant details, luxurious cyberpunk, hyperrealistic, cable electric wires, microchip, full body" + + n_prompt: + - "worst quality, low quality, deformed, distorted, disfigured, bad eyes, bad anatomy, disconnected limbs, wrong body proportions, low quality, worst quality, text, watermark, signatre, logo, illustration, painting, cartoons, ugly, easy_negative" diff --git a/configs/prompts/7-FilmVelvia.yaml b/configs/prompts/7-FilmVelvia.yaml new file mode 100644 index 0000000000000000000000000000000000000000..46cc1634c5b425fc8c13038c131480e854a3e013 --- /dev/null +++ b/configs/prompts/7-FilmVelvia.yaml @@ -0,0 +1,24 @@ +FilmVelvia: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/majicmixRealistic_v4.safetensors" + lora_model_path: "models/DreamBooth_LoRA/FilmVelvia2.safetensors" + lora_alpha: 0.6 + + seed: [358675358833372813, 3519455280971923743, 11684545350557985081, 8696855302100399877] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "a woman standing on the side of a road at night,girl, long hair, motor vehicle, car, looking at viewer, ground vehicle, night, hands in pockets, blurry background, coat, black hair, parted lips, bokeh, jacket, brown hair, outdoors, red lips, upper body, artist name" + - ", dark shot,0mm, portrait quality of a arab man worker,boy, wasteland that stands out vividly against the background of the desert, barren landscape, closeup, moles skin, soft light, sharp, exposure blend, medium shot, bokeh, hdr, high contrast, cinematic, teal and orange5, muted colors, dim colors, soothing tones, low saturation, hyperdetailed, noir" + - "fashion photography portrait of 1girl, offshoulder, fluffy short hair, soft light, rim light, beautiful shadow, low key, photorealistic, raw photo, natural skin texture, realistic eye and face details, hyperrealism, ultra high res, 4K, Best quality, masterpiece, necklace, cleavage, in the dark" + - "In this lighthearted portrait, a woman is dressed as a fierce warrior, armed with an arsenal of paintbrushes and palette knives. Her war paint is composed of thick, vibrant strokes of color, and her armor is made of paint tubes and paint-splattered canvases. She stands victoriously atop a mountain of conquered blank canvases, with a beautiful, colorful landscape behind her, symbolizing the power of art and creativity. bust Portrait, close-up, Bright and transparent scene lighting, " + + n_prompt: + - "cartoon, anime, sketches,worst quality, low quality, deformed, distorted, disfigured, bad eyes, wrong lips, weird mouth, bad teeth, mutated hands and fingers, bad anatomy, wrong anatomy, amputation, extra limb, missing limb, floating limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg" + - "cartoon, anime, sketches,worst quality, low quality, deformed, distorted, disfigured, bad eyes, wrong lips, weird mouth, bad teeth, mutated hands and fingers, bad anatomy, wrong anatomy, amputation, extra limb, missing limb, floating limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg" + - "wrong white balance, dark, cartoon, anime, sketches,worst quality, low quality, deformed, distorted, disfigured, bad eyes, wrong lips, weird mouth, bad teeth, mutated hands and fingers, bad anatomy, wrong anatomy, amputation, extra limb, missing limb, floating limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg" + - "wrong white balance, dark, cartoon, anime, sketches,worst quality, low quality, deformed, distorted, disfigured, bad eyes, wrong lips, weird mouth, bad teeth, mutated hands and fingers, bad anatomy, wrong anatomy, amputation, extra limb, missing limb, floating limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg" diff --git a/configs/prompts/8-GhibliBackground.yaml b/configs/prompts/8-GhibliBackground.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45145dbcdc1e31f061f3fdf8fa035d4d962a4f25 --- /dev/null +++ b/configs/prompts/8-GhibliBackground.yaml @@ -0,0 +1,21 @@ +GhibliBackground: + motion_module: + - "models/Motion_Module/mm_sd_v14.ckpt" + - "models/Motion_Module/mm_sd_v15.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/CounterfeitV30_25.safetensors" + lora_model_path: "models/DreamBooth_LoRA/lora_Ghibli_n3.safetensors" + lora_alpha: 1.0 + + seed: [8775748474469046618, 5893874876080607656, 11911465742147695752, 12437784838692000640] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "best quality,single build,architecture, blue_sky, building,cloudy_sky, day, fantasy, fence, field, house, build,architecture,landscape, moss, outdoors, overgrown, path, river, road, rock, scenery, sky, sword, tower, tree, waterfall" + - "black_border, building, city, day, fantasy, ice, landscape, letterboxed, mountain, ocean, outdoors, planet, scenery, ship, snow, snowing, water, watercraft, waterfall, winter" + - ",mysterious sea area, fantasy,build,concept" + - "Tomb Raider,Scenography,Old building" + + n_prompt: + - "easynegative,bad_construction,bad_structure,bad_wail,bad_windows,blurry,cloned_window,cropped,deformed,disfigured,error,extra_windows,extra_chimney,extra_door,extra_structure,extra_frame,fewer_digits,fused_structure,gross_proportions,jpeg_artifacts,long_roof,low_quality,structure_limbs,missing_windows,missing_doors,missing_roofs,mutated_structure,mutation,normal_quality,out_of_frame,owres,poorly_drawn_structure,poorly_drawn_house,signature,text,too_many_windows,ugly,username,uta,watermark,worst_quality" diff --git a/configs/prompts/v2/5-RealisticVision-MotionLoRA.yaml b/configs/prompts/v2/5-RealisticVision-MotionLoRA.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37d1aedd91365b06fb97b615117bd399095f7bc5 --- /dev/null +++ b/configs/prompts/v2/5-RealisticVision-MotionLoRA.yaml @@ -0,0 +1,189 @@ +ZoomIn: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_ZoomIn.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +ZoomOut: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_ZoomOut.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +PanLeft: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_PanLeft.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +PanRight: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_PanRight.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +TiltUp: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_TiltUp.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +TiltDown: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_TiltDown.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +RollingAnticlockwise: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_RollingAnticlockwise.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + + + +RollingClockwise: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + motion_module_lora_configs: + - path: "models/MotionLoRA/v2_lora_RollingClockwise.ckpt" + alpha: 1.0 + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: 45987230 + steps: 25 + guidance_scale: 7.5 + + prompt: + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + + n_prompt: + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" diff --git a/configs/prompts/v2/5-RealisticVision.yaml b/configs/prompts/v2/5-RealisticVision.yaml new file mode 100644 index 0000000000000000000000000000000000000000..423b64f250e8ba32566b22338dacfa2a15e239d8 --- /dev/null +++ b/configs/prompts/v2/5-RealisticVision.yaml @@ -0,0 +1,23 @@ +RealisticVision: + inference_config: "configs/inference/inference-v2.yaml" + motion_module: + - "models/Motion_Module/mm_sd_v15_v2.ckpt" + + dreambooth_path: "models/DreamBooth_LoRA/realisticVisionV20_v20.safetensors" + lora_model_path: "" + + seed: [13100322578370451493, 14752961627088720670, 9329399085567825781, 16987697414827649302] + steps: 25 + guidance_scale: 7.5 + + prompt: + - "b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + - "close up photo of a rabbit, forest, haze, halation, bloom, dramatic atmosphere, centred, rule of thirds, 200mm 1.4f macro shot" + - "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + - "night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain" + + n_prompt: + - "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + - "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" + - "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, art, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation" diff --git a/configs/test.yaml b/configs/test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..edfb37bea3f35c8ece7eec5176e308a12e60671a --- /dev/null +++ b/configs/test.yaml @@ -0,0 +1,33 @@ +pretrained_model_path: /content/AnimateDiff-SceneFusion/models/StableDiffusion/ +motion_module: /content/AnimateDiff-SceneFusion/models/Motion_Module/mm_sd_v15.ckpt +output_dir: /content/AnimateDiff-SceneFusion/models/Motion_Module/test +train_data: + video_folder: data/output + csv_path: data/output.csv + sample_n_frames: 5 + sample_stride: 100 + sample_size: 256 +validation_data: + prompts: + - camera panning right to left, a bird's eye view of a row of buildings in a city + with trees in the foreground, masterpiece, best quality + video_length: 5 + width: 256 + height: 256 + num_inference_steps: 20 + guidance_scale: 12.5 + temporal_context: 24 + use_inv_latent: true + num_inv_steps: 50 +learning_rate: 0.0003 +train_batch_size: 1 +max_train_steps: 1 +checkpointing_steps: 100 +validation_steps: 1 +trainable_modules: +- to_q +seed: 33 +mixed_precision: fp16 +use_8bit_adam: false +gradient_checkpointing: true +enable_xformers_memory_efficient_attention: true diff --git a/configs/training/image_finetune.yaml b/configs/training/image_finetune.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ea05fd143b533e033246133140d70d7dc008c041 --- /dev/null +++ b/configs/training/image_finetune.yaml @@ -0,0 +1,48 @@ +image_finetune: true + +output_dir: "outputs" +pretrained_model_path: "models/StableDiffusion/stable-diffusion-v1-5" + +noise_scheduler_kwargs: + num_train_timesteps: 1000 + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "scaled_linear" + steps_offset: 1 + clip_sample: false + +train_data: + csv_path: "/mnt/petrelfs/guoyuwei/projects/datasets/webvid/results_2M_val.csv" + video_folder: "/mnt/petrelfs/guoyuwei/projects/datasets/webvid/2M_val" + sample_size: 256 + +validation_data: + prompts: + - "Snow rocky mountains peaks canyon. Snow blanketed rocky mountains surround and shadow deep canyons." + - "A drone view of celebration with Christma tree and fireworks, starry sky - background." + - "Robot dancing in times square." + - "Pacific coast, carmel by the sea ocean and waves." + num_inference_steps: 25 + guidance_scale: 8. + +trainable_modules: + - "." + +unet_checkpoint_path: "" + +learning_rate: 1.e-5 +train_batch_size: 50 + +max_train_epoch: -1 +max_train_steps: 100 +checkpointing_epochs: -1 +checkpointing_steps: 60 + +validation_steps: 5000 +validation_steps_tuple: [2, 50] + +global_seed: 42 +mixed_precision_training: true +enable_xformers_memory_efficient_attention: True + +is_debug: False diff --git a/configs/training/training copy.yaml b/configs/training/training copy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18d0231b30a88c633c3761e0d4e8c9920a77354e --- /dev/null +++ b/configs/training/training copy.yaml @@ -0,0 +1,69 @@ +image_finetune: false + +output_dir: "outputs" +pretrained_model_path: "runwayml/stable-diffusion-v1-5" + +unet_additional_kwargs: + use_motion_module : true + motion_module_resolutions : [ 1,2,4,8 ] + unet_use_cross_frame_attention : false + unet_use_temporal_attention : false + + motion_module_type: Vanilla + motion_module_kwargs: + num_attention_heads : 8 + num_transformer_block : 1 + attention_block_types : [ "Temporal_Self", "Temporal_Self" ] + temporal_position_encoding : true + temporal_position_encoding_max_len : 24 + temporal_attention_dim_div : 1 + zero_initialize : true + +noise_scheduler_kwargs: + num_train_timesteps: 1000 + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" + steps_offset: 1 + clip_sample: false + +train_data: + csv_path: "data/output.csv" + video_folder: "data/output" + sample_size: 256 + sample_stride: 4 + sample_n_frames: 5 + +validation_data: + prompts: + - "Snow rocky mountains peaks canyon. Snow blanketed rocky mountains surround and shadow deep canyons." + - "A drone view of celebration with Christmas tree and fireworks, starry sky - background." + - "Robot dancing in times square." + - "Pacific coast, carmel by the sea ocean and waves." + num_inference_steps: 20 + guidance_scale: 12.5 + temporal_context": 24 + use_inv_latent": True + num_inv_steps: 50 + +trainable_modules: + - "motion_modules." + +unet_checkpoint_path: "" + +learning_rate: 3.e-5 +train_batch_size: 1 + +max_train_epoch: -1 +max_train_steps: 300 +checkpointing_epochs: -1 +checkpointing_steps: 1000 + +validation_steps: 100 +# validation_steps_tuple: [2, 50] + +global_seed: 42 +mixed_precision_training: true +enable_xformers_memory_efficient_attention: True + +is_debug: True diff --git a/configs/training/training.yaml b/configs/training/training.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b8ba114729b3d58b43ec91ff19f5a8623cc2bc6a --- /dev/null +++ b/configs/training/training.yaml @@ -0,0 +1,31 @@ +pretrained_model_path: /content/AnimateDiff-SceneFusion/models/StableDiffusion/ +motion_module: /content/AnimateDiff-SceneFusion/models/Motion_Module/mm_sd_v15.ckpt +output_dir: /content/AnimateDiff-SceneFusion/models/Motion_Module/test +train_data: + csv_path: "data/output.csv" + video_folder: "data/output" + sample_size: 256 + sample_stride: 100 + sample_n_frames: 5 +validation_data: + prompts: + - camera panning right to left, a bird's eye view of a row of buildings in a city with trees in the foreground, masterpiece, best quality + video_length: 5 + width: 256 + height: 256 + num_inference_steps: 20 + guidance_scale: 12.5 + use_inv_latent: true + num_inv_steps: 50 +learning_rate: 3.0e-04 +train_batch_size: 1 +max_train_steps: 36 +checkpointing_steps: 100 +validation_steps: 1 +trainable_modules: +- to_q +seed: 33 +mixed_precision: fp16 +use_8bit_adam: false +gradient_checkpointing: true +enable_xformers_memory_efficient_attention: true \ No newline at end of file diff --git a/data/data.ipynb b/data/data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b73e1a5ec9558038c13761bba79ebd6c5e69192f --- /dev/null +++ b/data/data.ipynb @@ -0,0 +1,2504 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\data\\EveryDream\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "fatal: destination path 'EveryDream' already exists and is not an empty directory.\n" + ] + } + ], + "source": [ + "#download repo\n", + "!git clone https://github.com/victorchall/EveryDream.git\n", + "# Set working directory\n", + "%cd EveryDream" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "fatal: destination path 'scripts/BLIP' already exists and is not an empty directory.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: timm in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (0.9.10)\n", + "Requirement already satisfied: torch>=1.7 in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from timm) (1.13.1)\n", + "Requirement already satisfied: torchvision in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from timm) (0.14.1)\n", + "Requirement already satisfied: pyyaml in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from timm) (6.0.1)\n", + "Requirement already satisfied: huggingface-hub in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from timm) (0.19.3)\n", + "Requirement already satisfied: safetensors in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from timm) (0.4.0)\n", + "Requirement already 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(1.3.1)\n", + "Requirement already satisfied: idna>=2.0 in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from yarl<2.0,>=1.0->aiohttp) (3.4)\n", + "Requirement already satisfied: opencv-python in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (4.8.1.78)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages (from opencv-python) (1.26.0)\n" + ] + } + ], + "source": [ + "\n", + "# install requirements\n", + "# !pip install torch=='1.12.1+cu113' 'torchvision==0.13.1+cu113' --extra-index-url https://download.pytorch.org/whl/cu113\n", + "!pip install pandas>='1.3.5'\n", + "!git clone https://github.com/salesforce/BLIP scripts/BLIP\n", + "!pip install timm\n", + "!pip install fairscale\n", + "# !pip install transformers=='4.19.2'\n", + "!pip install timm\n", + "!pip install aiofiles\n", + "!pip install aiohttp\n", + "!pip install opencv-python" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip uninstall pillow\n", + "# !pip install pillow" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\data\n" + ] + } + ], + "source": [ + "%cd .." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Volume in drive C is OS\n", + " Volume Serial Number is 9C50-ADC8\n", + "\n", + " Directory of c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\data\n", + "\n", + "11/16/2023 02:50 PM .\n", + "11/16/2023 02:30 PM ..\n", + "11/15/2023 12:32 PM .cache\n", + "11/16/2023 02:52 PM 31,383 data.ipynb\n", + "11/15/2023 12:27 PM EveryDream\n", + "11/15/2023 01:23 PM output\n", + "11/15/2023 01:57 PM 262 output.csv\n", + "11/15/2023 11:56 AM raw\n", + " 2 File(s) 31,645 bytes\n", + " 6 Dir(s) 170,972,418,048 bytes free\n" + ] + } + ], + "source": [ + "%ls" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# !python EveryDream/scripts/auto_caption.py --img_dir raw/1 --out_dir output " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision import transforms\n", + "SIZE = 256" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def load_image(raw_image, device):\n", + " transform = transforms.Compose([\n", + " #transforms.CenterCrop(SIZE),\n", + " transforms.Resize((SIZE, SIZE), interpolation=InterpolationMode.BICUBIC),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n", + " ])\n", + " image = transform(raw_image).unsqueeze(0).to(device)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "reshape position embedding from 576 to 256\n", + "load checkpoint from .cache/model_base_caption_capfilt_large.pth\n", + "loading model to cuda\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import torch\n", + "import aiofiles\n", + "import io\n", + "\n", + "if not os.path.exists(\"EveryDream/scripts/BLIP\"):\n", + " print(\"BLIP not found, cloning BLIP repo\")\n", + " subprocess.run([\"git\", \"clone\", \"https://github.com/salesforce/BLIP\", \"EveryDream/scripts/BLIP\"])\n", + "blip_path = \"EveryDream/scripts/BLIP/\"\n", + "sys.path.append(blip_path)\n", + "\n", + "import models.blip\n", + "\n", + "config_path = \"EveryDream/scripts/BLIP/configs/med_config.json\"\n", + "\n", + "cache_folder = \".cache\"\n", + "model_cache_path = \".cache/model_base_caption_capfilt_large.pth\"\n", + "\n", + "blip_decoder = models.blip.blip_decoder(pretrained=model_cache_path, image_size=SIZE, vit='base', med_config=config_path)\n", + "blip_decoder.eval()\n", + "\n", + "print(f\"loading model to cuda\")\n", + "\n", + "blip_decoder = blip_decoder.to(torch.device(\"cuda\"))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['3dDS10', '3dDS11', '3dDS12', '3dDS13', '3dDS14RL', '3dDS15', '3dDS16', '3dDS17', '3dDS18', '3dDS19', '3dDS1RL', '3dDS20', '3dDS21', '3dDS22', '3dDS23', '3dDS24', '3dDS25', '3dDS26', '3dDS27', '3dDS28', '3dDS29', '3dDS2RL', '3dDS30', '3dDS31', '3dDS32', '3dDS33', '3dDS34', '3dDS35', '3dDS36', '3dDS3LR', '3dDS4', '3dDS5', '3dDS6', '3dDS7RL', '3dDS8RL', '3dDS9']\n", + "working image: raw/3dDS10/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000029c21a42480] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000029c21a42480] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000029c21a42480] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/0.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 107kB time=00:00:02.00 bitrate= 439.1kbits/s speed=48.4x \n", + "video:106kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.813598%\n", + "[libx264 @ 0000029c21a42480] frame I:1 Avg QP:18.36 size: 22667\n", + "[libx264 @ 0000029c21a42480] frame P:4 Avg QP:18.82 size: 21390\n", + "[libx264 @ 0000029c21a42480] mb I I16..4: 0.4% 25.0% 74.6%\n", + "[libx264 @ 0000029c21a42480] mb P I16..4: 0.4% 12.0% 36.4% P16..4: 6.2% 26.1% 18.8% 0.0% 0.0% skip: 0.1%\n", + "[libx264 @ 0000029c21a42480] 8x8 transform intra:24.7% inter:19.7%\n", + "[libx264 @ 0000029c21a42480] coded y,u,v intra: 98.5% 81.5% 71.9% inter: 95.8% 29.7% 20.0%\n", + "[libx264 @ 0000029c21a42480] i16 v,h,dc,p: 0% 40% 0% 60%\n", + "[libx264 @ 0000029c21a42480] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 26% 16% 4% 4% 4% 9% 4% 12%\n", + "[libx264 @ 0000029c21a42480] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 21% 10% 6% 6% 5% 9% 6% 12%\n", + "[libx264 @ 0000029c21a42480] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000029c21a42480] ref P L0: 75.8% 22.8% 1.1% 0.4%\n", + "[libx264 @ 0000029c21a42480] kb/s:173.16\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS11/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 00000238af013bc0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 00000238af013bc0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 00000238af013bc0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/1.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 114kB time=00:00:02.00 bitrate= 467.7kbits/s speed= 49x \n", + "video:113kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.763497%\n", + "[libx264 @ 00000238af013bc0] frame I:1 Avg QP:18.44 size: 21300\n", + "[libx264 @ 00000238af013bc0] frame P:4 Avg QP:18.90 size: 23518\n", + "[libx264 @ 00000238af013bc0] mb I I16..4: 0.8% 21.1% 78.1%\n", + "[libx264 @ 00000238af013bc0] mb P I16..4: 1.1% 16.1% 70.9% P16..4: 2.2% 5.2% 4.5% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 00000238af013bc0] 8x8 transform intra:18.9% inter:22.1%\n", + "[libx264 @ 00000238af013bc0] coded y,u,v intra: 91.6% 75.4% 68.2% inter: 76.6% 39.8% 31.6%\n", + "[libx264 @ 00000238af013bc0] i16 v,h,dc,p: 31% 8% 54% 8%\n", + "[libx264 @ 00000238af013bc0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20% 12% 16% 7% 9% 7% 13% 8% 7%\n", + "[libx264 @ 00000238af013bc0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 12% 16% 10% 8% 6% 15% 10% 9%\n", + "[libx264 @ 00000238af013bc0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 00000238af013bc0] ref P L0: 66.4% 22.5% 7.6% 3.5%\n", + "[libx264 @ 00000238af013bc0] kb/s:184.60\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS12/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 00000246afbcc9c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 00000246afbcc9c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 00000246afbcc9c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/2.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 99kB time=00:00:02.00 bitrate= 406.3kbits/s speed=53.1x \n", + "video:98kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.900086%\n", + "[libx264 @ 00000246afbcc9c0] frame I:2 Avg QP:17.99 size: 22694\n", + "[libx264 @ 00000246afbcc9c0] frame P:2 Avg QP:18.18 size: 19100\n", + "[libx264 @ 00000246afbcc9c0] frame B:1 Avg QP:18.56 size: 16394\n", + "[libx264 @ 00000246afbcc9c0] consecutive B-frames: 60.0% 40.0% 0.0% 0.0%\n", + "[libx264 @ 00000246afbcc9c0] mb I I16..4: 0.4% 26.8% 72.9%\n", + "[libx264 @ 00000246afbcc9c0] mb P I16..4: 0.8% 28.1% 47.7% P16..4: 3.1% 11.7% 8.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 00000246afbcc9c0] mb B I16..4: 0.8% 7.0% 25.4% B16..8: 10.5% 24.6% 29.3% direct: 2.3% skip: 0.0% L0:54.1% L1:27.0% BI:18.9%\n", + "[libx264 @ 00000246afbcc9c0] 8x8 transform intra:30.2% inter:34.3%\n", + "[libx264 @ 00000246afbcc9c0] coded y,u,v intra: 96.0% 61.5% 72.1% inter: 85.3% 27.7% 35.1%\n", + "[libx264 @ 00000246afbcc9c0] i16 v,h,dc,p: 0% 25% 12% 62%\n", + "[libx264 @ 00000246afbcc9c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 18% 24% 16% 5% 6% 8% 9% 6% 8%\n", + "[libx264 @ 00000246afbcc9c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 32% 10% 5% 6% 8% 6% 6% 10%\n", + "[libx264 @ 00000246afbcc9c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 00000246afbcc9c0] kb/s:159.97\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS13/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000002ca46100f00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000002ca46100f00] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000002ca46100f00] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/3.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 86kB time=00:00:02.00 bitrate= 350.3kbits/s speed=45.2x \n", + "video:85kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.021997%\n", + "[libx264 @ 000002ca46100f00] frame I:1 Avg QP:17.98 size: 20223\n", + "[libx264 @ 000002ca46100f00] frame P:4 Avg QP:16.86 size: 16449\n", + "[libx264 @ 000002ca46100f00] mb I I16..4: 2.3% 28.5% 69.1%\n", + "[libx264 @ 000002ca46100f00] mb P I16..4: 0.9% 32.7% 47.9% P16..4: 2.1% 8.7% 7.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000002ca46100f00] 8x8 transform intra:37.4% inter:37.6%\n", + "[libx264 @ 000002ca46100f00] coded y,u,v intra: 88.0% 57.2% 58.8% inter: 90.1% 33.2% 42.6%\n", + "[libx264 @ 000002ca46100f00] i16 v,h,dc,p: 7% 27% 47% 20%\n", + "[libx264 @ 000002ca46100f00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 19% 19% 6% 6% 4% 8% 5% 16%\n", + "[libx264 @ 000002ca46100f00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 24% 11% 6% 6% 4% 14% 5% 14%\n", + "[libx264 @ 000002ca46100f00] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000002ca46100f00] ref P L0: 78.7% 14.2% 7.1%\n", + "[libx264 @ 000002ca46100f00] kb/s:137.63\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS14RL/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001bae88ad100] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001bae88ad100] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001bae88ad100] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/4.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 191kB time=00:00:02.00 bitrate= 782.9kbits/s speed=41.5x \n", + "video:190kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.456818%\n", + "[libx264 @ 000001bae88ad100] frame I:2 Avg QP:18.58 size: 40314\n", + "[libx264 @ 000001bae88ad100] frame P:3 Avg QP:19.80 size: 37842\n", + "[libx264 @ 000001bae88ad100] mb I I16..4: 0.0% 29.9% 70.1%\n", + "[libx264 @ 000001bae88ad100] mb P I16..4: 0.0% 31.0% 58.7% P16..4: 1.6% 5.2% 3.5% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001bae88ad100] 8x8 transform intra:32.6% inter:21.5%\n", + "[libx264 @ 000001bae88ad100] coded y,u,v intra: 100.0% 89.9% 79.3% inter: 98.1% 26.3% 13.0%\n", + "[libx264 @ 000001bae88ad100] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 20% 26% 7% 5% 5% 8% 7% 15%\n", + "[libx264 @ 000001bae88ad100] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 8% 11% 18% 10% 8% 8% 11% 11% 15%\n", + "[libx264 @ 000001bae88ad100] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001bae88ad100] ref P L0: 91.8% 5.7% 2.5%\n", + "[libx264 @ 000001bae88ad100] kb/s:310.64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS15/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 00000192edc34980] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 00000192edc34980] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 00000192edc34980] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/5.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 144kB time=00:00:02.00 bitrate= 590.8kbits/s speed=52.6x \n", + "video:143kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.625387%\n", + "[libx264 @ 00000192edc34980] frame I:1 Avg QP:18.39 size: 34490\n", + "[libx264 @ 00000192edc34980] frame P:1 Avg QP:18.27 size: 28103\n", + "[libx264 @ 00000192edc34980] frame B:3 Avg QP:18.39 size: 27841\n", + "[libx264 @ 00000192edc34980] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 00000192edc34980] mb I I16..4: 12.9% 26.2% 60.9%\n", + "[libx264 @ 00000192edc34980] mb P I16..4: 0.0% 37.5% 53.9% P16..4: 1.2% 4.3% 3.1% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 00000192edc34980] mb B I16..4: 0.5% 21.7% 49.0% B16..8: 9.6% 10.5% 5.3% direct: 3.4% skip: 0.0% L0:30.8% L1:17.8% BI:51.4%\n", + "[libx264 @ 00000192edc34980] 8x8 transform intra:31.8% inter:21.8%\n", + "[libx264 @ 00000192edc34980] coded y,u,v intra: 99.9% 75.4% 62.2% inter: 99.7% 18.0% 9.3%\n", + "[libx264 @ 00000192edc34980] i16 v,h,dc,p: 3% 3% 78% 16%\n", + "[libx264 @ 00000192edc34980] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 6% 21% 24% 6% 7% 5% 11% 6% 15%\n", + "[libx264 @ 00000192edc34980] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 14% 19% 9% 9% 9% 12% 9% 13%\n", + "[libx264 @ 00000192edc34980] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 00000192edc34980] ref B L0: 95.7% 4.3%\n", + "[libx264 @ 00000192edc34980] ref B L1: 94.3% 5.7%\n", + "[libx264 @ 00000192edc34980] kb/s:233.79\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS16/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001bfb6088040] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001bfb6088040] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001bfb6088040] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/6.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 168kB time=00:00:02.00 bitrate= 689.7kbits/s speed=41.1x \n", + "video:167kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.535227%\n", + "[libx264 @ 000001bfb6088040] frame I:1 Avg QP:19.52 size: 39562\n", + "[libx264 @ 000001bfb6088040] frame P:1 Avg QP:18.64 size: 31744\n", + "[libx264 @ 000001bfb6088040] frame B:3 Avg QP:19.26 size: 33179\n", + "[libx264 @ 000001bfb6088040] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 000001bfb6088040] mb I I16..4: 18.4% 23.8% 57.8%\n", + "[libx264 @ 000001bfb6088040] mb P I16..4: 0.4% 41.8% 52.3% P16..4: 2.7% 2.3% 0.4% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001bfb6088040] mb B I16..4: 0.0% 18.0% 49.0% B16..8: 8.7% 10.7% 9.1% direct: 4.6% skip: 0.0% L0:22.7% L1:23.4% BI:53.9%\n", + "[libx264 @ 000001bfb6088040] 8x8 transform intra:30.2% inter:31.3%\n", + "[libx264 @ 000001bfb6088040] coded y,u,v intra: 99.9% 84.4% 82.6% inter: 99.5% 46.3% 37.8%\n", + "[libx264 @ 000001bfb6088040] i16 v,h,dc,p: 0% 2% 81% 17%\n", + "[libx264 @ 000001bfb6088040] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 28% 18% 3% 8% 6% 17% 4% 10%\n", + "[libx264 @ 000001bfb6088040] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 7% 19% 8% 12% 15% 11% 9% 10%\n", + "[libx264 @ 000001bfb6088040] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001bfb6088040] ref B L0: 96.2% 3.8%\n", + "[libx264 @ 000001bfb6088040] ref B L1: 96.2% 3.8%\n", + "[libx264 @ 000001bfb6088040] kb/s:273.35\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS17/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000002b618334f40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000002b618334f40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000002b618334f40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/7.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 199kB time=00:00:02.00 bitrate= 813.1kbits/s speed=41.9x \n", + "video:198kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.439769%\n", + "[libx264 @ 000002b618334f40] frame I:2 Avg QP:19.20 size: 42154\n", + "[libx264 @ 000002b618334f40] frame P:3 Avg QP:20.27 size: 39133\n", + "[libx264 @ 000002b618334f40] mb I I16..4: 0.2% 5.5% 94.3%\n", + "[libx264 @ 000002b618334f40] mb P I16..4: 0.0% 13.3% 83.3% P16..4: 0.8% 0.8% 1.8% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000002b618334f40] 8x8 transform intra:10.4% inter:0.0%\n", + "[libx264 @ 000002b618334f40] coded y,u,v intra: 99.9% 92.9% 74.8% inter: 100.0% 80.8% 33.7%\n", + "[libx264 @ 000002b618334f40] i16 v,h,dc,p: 0% 100% 0% 0%\n", + "[libx264 @ 000002b618334f40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 21% 19% 6% 8% 7% 8% 7% 14%\n", + "[libx264 @ 000002b618334f40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 11% 18% 9% 9% 10% 10% 10% 14%\n", + "[libx264 @ 000002b618334f40] Weighted P-Frames: Y:33.3% UV:0.0%\n", + "[libx264 @ 000002b618334f40] ref P L0: 83.7% 6.7% 9.6%\n", + "[libx264 @ 000002b618334f40] kb/s:322.73\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS18/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001ebeaeac540] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001ebeaeac540] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001ebeaeac540] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/8.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 177kB time=00:00:02.00 bitrate= 726.6kbits/s speed=42.9x \n", + "video:177kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.503475%\n", + "[libx264 @ 000001ebeaeac540] frame I:1 Avg QP:19.30 size: 37476\n", + "[libx264 @ 000001ebeaeac540] frame P:2 Avg QP:19.51 size: 37468\n", + "[libx264 @ 000001ebeaeac540] frame B:2 Avg QP:19.55 size: 33829\n", + "[libx264 @ 000001ebeaeac540] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 000001ebeaeac540] mb I I16..4: 0.0% 19.1% 80.9%\n", + "[libx264 @ 000001ebeaeac540] mb P I16..4: 0.0% 16.2% 81.4% P16..4: 0.0% 1.6% 0.8% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001ebeaeac540] mb B I16..4: 0.0% 19.5% 42.6% B16..8: 12.1% 12.5% 9.0% direct: 4.1% skip: 0.2% L0:54.4% L1: 3.1% BI:42.6%\n", + "[libx264 @ 000001ebeaeac540] 8x8 transform intra:21.6% inter:6.4%\n", + "[libx264 @ 000001ebeaeac540] coded y,u,v intra: 99.8% 97.3% 84.2% inter: 92.0% 75.5% 16.0%\n", + "[libx264 @ 000001ebeaeac540] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 34% 15% 13% 5% 3% 6% 4% 9% 10%\n", + "[libx264 @ 000001ebeaeac540] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20% 8% 15% 8% 7% 11% 8% 11% 11%\n", + "[libx264 @ 000001ebeaeac540] Weighted P-Frames: Y:50.0% UV:50.0%\n", + "[libx264 @ 000001ebeaeac540] ref P L0: 35.4% 39.6% 25.0%\n", + "[libx264 @ 000001ebeaeac540] ref B L0: 93.3% 6.7%\n", + "[libx264 @ 000001ebeaeac540] kb/s:288.11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS19/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000018c9e800b40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000018c9e800b40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000018c9e800b40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/9.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 123kB time=00:00:02.00 bitrate= 505.1kbits/s speed=59.4x \n", + "video:122kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.722707%\n", + "[libx264 @ 0000018c9e800b40] frame I:2 Avg QP:18.28 size: 27908\n", + "[libx264 @ 0000018c9e800b40] frame P:2 Avg QP:19.29 size: 24479\n", + "[libx264 @ 0000018c9e800b40] frame B:1 Avg QP:19.48 size: 19915\n", + "[libx264 @ 0000018c9e800b40] consecutive B-frames: 60.0% 40.0% 0.0% 0.0%\n", + "[libx264 @ 0000018c9e800b40] mb I I16..4: 1.4% 7.6% 91.0%\n", + "[libx264 @ 0000018c9e800b40] mb P I16..4: 0.6% 12.5% 70.9% P16..4: 1.6% 5.9% 8.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000018c9e800b40] mb B I16..4: 0.0% 4.7% 29.3% B16..8: 8.2% 22.3% 30.2% direct: 4.7% skip: 0.8% L0:69.7% L1: 7.1% BI:23.2%\n", + "[libx264 @ 0000018c9e800b40] 8x8 transform intra:11.2% inter:18.1%\n", + "[libx264 @ 0000018c9e800b40] coded y,u,v intra: 99.9% 87.0% 60.4% inter: 93.5% 42.1% 18.7%\n", + "[libx264 @ 0000018c9e800b40] i16 v,h,dc,p: 0% 10% 30% 60%\n", + "[libx264 @ 0000018c9e800b40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 20% 12% 5% 9% 6% 16% 6% 21%\n", + "[libx264 @ 0000018c9e800b40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 8% 18% 11% 7% 11% 8% 16% 6% 16%\n", + "[libx264 @ 0000018c9e800b40] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000018c9e800b40] kb/s:199.50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS1RL/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.24, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 00000293fdb15b00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 00000293fdb15b00] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 00000293fdb15b00] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/10.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 6 fps=0.0 q=-1.0 Lsize= 124kB time=00:00:03.00 bitrate= 338.9kbits/s speed=69.9x \n", + "video:123kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.705157%\n", + "[libx264 @ 00000293fdb15b00] frame I:1 Avg QP:19.27 size: 22800\n", + "[libx264 @ 00000293fdb15b00] frame P:5 Avg QP:19.07 size: 20548\n", + "[libx264 @ 00000293fdb15b00] mb I I16..4: 2.3% 21.9% 75.8%\n", + "[libx264 @ 00000293fdb15b00] mb P I16..4: 0.4% 36.6% 57.8% P16..4: 1.4% 1.7% 2.0% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 00000293fdb15b00] 8x8 transform intra:35.7% inter:12.1%\n", + "[libx264 @ 00000293fdb15b00] coded y,u,v intra: 98.8% 68.7% 65.9% inter: 83.7% 23.1% 28.0%\n", + "[libx264 @ 00000293fdb15b00] i16 v,h,dc,p: 36% 27% 18% 18%\n", + "[libx264 @ 00000293fdb15b00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 16% 17% 5% 5% 14% 10% 7% 10%\n", + "[libx264 @ 00000293fdb15b00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 15% 12% 7% 7% 11% 10% 7% 9%\n", + "[libx264 @ 00000293fdb15b00] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 00000293fdb15b00] ref P L0: 63.6% 30.3% 4.2% 1.9%\n", + "[libx264 @ 00000293fdb15b00] kb/s:167.39\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS20/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001ea856615c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001ea856615c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001ea856615c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/11.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 130kB time=00:00:02.00 bitrate= 531.2kbits/s speed=48.3x \n", + "video:129kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.689995%\n", + "[libx264 @ 000001ea856615c0] frame I:1 Avg QP:19.19 size: 28502\n", + "[libx264 @ 000001ea856615c0] frame P:3 Avg QP:19.44 size: 26301\n", + "[libx264 @ 000001ea856615c0] frame B:1 Avg QP:19.63 size: 23808\n", + "[libx264 @ 000001ea856615c0] consecutive B-frames: 60.0% 40.0% 0.0% 0.0%\n", + "[libx264 @ 000001ea856615c0] mb I I16..4: 0.0% 10.5% 89.5%\n", + "[libx264 @ 000001ea856615c0] mb P I16..4: 0.1% 11.7% 68.2% P16..4: 1.2% 8.5% 10.3% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001ea856615c0] mb B I16..4: 0.0% 5.5% 46.5% B16..8: 3.5% 24.6% 17.9% direct: 2.0% skip: 0.0% L0:56.9% L1:23.4% BI:19.7%\n", + "[libx264 @ 000001ea856615c0] 8x8 transform intra:13.0% inter:10.2%\n", + "[libx264 @ 000001ea856615c0] coded y,u,v intra: 99.8% 90.3% 62.7% inter: 96.5% 48.9% 20.1%\n", + "[libx264 @ 000001ea856615c0] i16 v,h,dc,p: 0% 0% 0% 100%\n", + "[libx264 @ 000001ea856615c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 10% 31% 10% 7% 6% 6% 11% 6% 13%\n", + "[libx264 @ 000001ea856615c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 23% 11% 7% 8% 10% 10% 8% 13%\n", + "[libx264 @ 000001ea856615c0] Weighted P-Frames: Y:33.3% UV:33.3%\n", + "[libx264 @ 000001ea856615c0] ref P L0: 46.6% 28.1% 15.2% 10.1%\n", + "[libx264 @ 000001ea856615c0] kb/s:209.94\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS21/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001f13793eac0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001f13793eac0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001f13793eac0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/12.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 97kB time=00:00:02.00 bitrate= 395.3kbits/s speed=52.3x \n", + "video:96kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.937529%\n", + "[libx264 @ 000001f13793eac0] frame I:1 Avg QP:18.25 size: 24399\n", + "[libx264 @ 000001f13793eac0] frame P:2 Avg QP:18.55 size: 21118\n", + "[libx264 @ 000001f13793eac0] frame B:2 Avg QP:18.75 size: 15304\n", + "[libx264 @ 000001f13793eac0] consecutive B-frames: 20.0% 80.0% 0.0% 0.0%\n", + "[libx264 @ 000001f13793eac0] mb I I16..4: 0.4% 16.8% 82.8%\n", + "[libx264 @ 000001f13793eac0] mb P I16..4: 0.0% 14.6% 64.3% P16..4: 1.0% 7.6% 12.5% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001f13793eac0] mb B I16..4: 0.0% 2.7% 18.0% B16..8: 2.5% 37.5% 36.8% direct: 2.3% skip: 0.2% L0:28.5% L1:33.2% BI:38.2%\n", + "[libx264 @ 000001f13793eac0] 8x8 transform intra:17.2% inter:21.5%\n", + "[libx264 @ 000001f13793eac0] coded y,u,v intra: 99.8% 86.6% 54.9% inter: 87.5% 29.3% 9.0%\n", + "[libx264 @ 000001f13793eac0] i16 v,h,dc,p: 0% 0% 0% 100%\n", + "[libx264 @ 000001f13793eac0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 5% 19% 13% 6% 9% 8% 12% 8% 20%\n", + "[libx264 @ 000001f13793eac0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 8% 15% 11% 8% 13% 10% 11% 7% 18%\n", + "[libx264 @ 000001f13793eac0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001f13793eac0] ref P L0: 85.6% 12.3% 2.1%\n", + "[libx264 @ 000001f13793eac0] ref B L0: 97.4% 2.6%\n", + "[libx264 @ 000001f13793eac0] kb/s:155.59\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS22/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000029b1dbdf5c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000029b1dbdf5c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000029b1dbdf5c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/13.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 109kB time=00:00:02.00 bitrate= 448.2kbits/s speed=55.1x \n", + "video:109kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.826104%\n", + "[libx264 @ 0000029b1dbdf5c0] frame I:1 Avg QP:18.81 size: 29421\n", + "[libx264 @ 0000029b1dbdf5c0] frame P:1 Avg QP:19.88 size: 25065\n", + "[libx264 @ 0000029b1dbdf5c0] frame B:3 Avg QP:19.72 size: 18655\n", + "[libx264 @ 0000029b1dbdf5c0] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 0000029b1dbdf5c0] mb I I16..4: 1.6% 8.6% 89.8%\n", + "[libx264 @ 0000029b1dbdf5c0] mb P I16..4: 6.2% 3.1% 67.2% P16..4: 6.2% 8.2% 9.0% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000029b1dbdf5c0] mb B I16..4: 3.4% 2.3% 20.2% B16..8: 11.7% 29.8% 28.5% direct: 3.8% skip: 0.3% L0:35.8% L1:34.2% BI:30.0%\n", + "[libx264 @ 0000029b1dbdf5c0] 8x8 transform intra:7.4% inter:15.7%\n", + "[libx264 @ 0000029b1dbdf5c0] coded y,u,v intra: 91.1% 77.8% 74.9% inter: 89.9% 30.7% 29.5%\n", + "[libx264 @ 0000029b1dbdf5c0] i16 v,h,dc,p: 2% 83% 11% 4%\n", + "[libx264 @ 0000029b1dbdf5c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 29% 15% 3% 3% 9% 3% 7% 5%\n", + "[libx264 @ 0000029b1dbdf5c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 25% 11% 5% 6% 10% 8% 9% 9%\n", + "[libx264 @ 0000029b1dbdf5c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000029b1dbdf5c0] ref B L0: 90.7% 9.3%\n", + "[libx264 @ 0000029b1dbdf5c0] ref B L1: 92.8% 7.2%\n", + "[libx264 @ 0000029b1dbdf5c0] kb/s:176.72\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS23/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001c647105280] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001c647105280] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001c647105280] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/14.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 128kB time=00:00:02.00 bitrate= 523.3kbits/s speed=46.4x \n", + "video:127kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.694199%\n", + "[libx264 @ 000001c647105280] frame I:1 Avg QP:19.43 size: 35427\n", + "[libx264 @ 000001c647105280] frame P:2 Avg QP:19.87 size: 26292\n", + "[libx264 @ 000001c647105280] frame B:2 Avg QP:20.56 size: 20624\n", + "[libx264 @ 000001c647105280] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 000001c647105280] mb I I16..4: 0.0% 7.0% 93.0%\n", + "[libx264 @ 000001c647105280] mb P I16..4: 1.0% 7.8% 40.6% P16..4: 2.9% 19.3% 28.3% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001c647105280] mb B I16..4: 0.2% 0.8% 5.5% B16..8: 4.7% 36.9% 49.5% direct: 2.1% skip: 0.4% L0:46.1% L1:20.9% BI:33.0%\n", + "[libx264 @ 000001c647105280] 8x8 transform intra:11.4% inter:14.0%\n", + "[libx264 @ 000001c647105280] coded y,u,v intra: 96.3% 88.0% 86.2% inter: 92.8% 33.7% 33.5%\n", + "[libx264 @ 000001c647105280] i16 v,h,dc,p: 33% 50% 17% 0%\n", + "[libx264 @ 000001c647105280] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 6% 18% 18% 5% 8% 4% 11% 6% 23%\n", + "[libx264 @ 000001c647105280] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 19% 12% 6% 12% 8% 12% 5% 20%\n", + "[libx264 @ 000001c647105280] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001c647105280] ref P L0: 89.6% 6.6% 3.9%\n", + "[libx264 @ 000001c647105280] ref B L0: 96.4% 3.6%\n", + "[libx264 @ 000001c647105280] kb/s:206.82\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS24/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000026abe8100c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000026abe8100c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000026abe8100c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/15.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 103kB time=00:00:02.00 bitrate= 421.4kbits/s speed=47.3x \n", + "video:102kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.848196%\n", + "[libx264 @ 0000026abe8100c0] frame I:1 Avg QP:18.40 size: 26155\n", + "[libx264 @ 0000026abe8100c0] frame P:4 Avg QP:18.84 size: 19407\n", + "[libx264 @ 0000026abe8100c0] mb I I16..4: 4.7% 13.3% 82.0%\n", + "[libx264 @ 0000026abe8100c0] mb P I16..4: 1.0% 7.0% 41.6% P16..4: 5.5% 17.3% 27.2% 0.0% 0.0% skip: 0.4%\n", + "[libx264 @ 0000026abe8100c0] 8x8 transform intra:13.9% inter:23.4%\n", + "[libx264 @ 0000026abe8100c0] coded y,u,v intra: 93.8% 77.7% 75.3% inter: 82.8% 35.7% 38.1%\n", + "[libx264 @ 0000026abe8100c0] i16 v,h,dc,p: 18% 9% 18% 55%\n", + "[libx264 @ 0000026abe8100c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 5% 24% 14% 4% 7% 2% 24% 1% 19%\n", + "[libx264 @ 0000026abe8100c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 16% 10% 7% 13% 6% 20% 5% 15%\n", + "[libx264 @ 0000026abe8100c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000026abe8100c0] ref P L0: 73.3% 20.0% 5.1% 1.6%\n", + "[libx264 @ 0000026abe8100c0] kb/s:166.05\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS25/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000002017ba1c840] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000002017ba1c840] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000002017ba1c840] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/16.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 45kB time=00:00:02.00 bitrate= 182.5kbits/s speed=52.4x \n", + "video:44kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.980464%\n", + "[libx264 @ 000002017ba1c840] frame I:1 Avg QP:16.35 size: 13002\n", + "[libx264 @ 000002017ba1c840] frame P:4 Avg QP:17.24 size: 7766\n", + "[libx264 @ 000002017ba1c840] mb I I16..4: 1.6% 53.9% 44.5%\n", + "[libx264 @ 000002017ba1c840] mb P I16..4: 1.3% 12.1% 15.2% P16..4: 7.0% 28.6% 35.3% 0.0% 0.0% skip: 0.5%\n", + "[libx264 @ 000002017ba1c840] 8x8 transform intra:47.7% inter:40.6%\n", + "[libx264 @ 000002017ba1c840] coded y,u,v intra: 96.4% 75.6% 78.9% inter: 60.0% 16.3% 16.1%\n", + "[libx264 @ 000002017ba1c840] i16 v,h,dc,p: 0% 0% 12% 88%\n", + "[libx264 @ 000002017ba1c840] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 42% 8% 5% 4% 4% 12% 3% 15%\n", + "[libx264 @ 000002017ba1c840] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 27% 9% 7% 10% 8% 13% 6% 13%\n", + "[libx264 @ 000002017ba1c840] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000002017ba1c840] ref P L0: 86.0% 12.6% 1.2% 0.2%\n", + "[libx264 @ 000002017ba1c840] kb/s:70.50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS26/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000021b061559c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000021b061559c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000021b061559c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/17.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 84kB time=00:00:02.00 bitrate= 345.8kbits/s speed= 54x \n", + "video:84kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.054514%\n", + "[libx264 @ 0000021b061559c0] frame I:1 Avg QP:18.09 size: 19906\n", + "[libx264 @ 0000021b061559c0] frame P:2 Avg QP:18.81 size: 18136\n", + "[libx264 @ 0000021b061559c0] frame B:2 Avg QP:19.53 size: 14343\n", + "[libx264 @ 0000021b061559c0] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 0000021b061559c0] mb I I16..4: 0.8% 21.1% 78.1%\n", + "[libx264 @ 0000021b061559c0] mb P I16..4: 0.6% 10.9% 42.6% P16..4: 4.5% 18.2% 23.2% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000021b061559c0] mb B I16..4: 0.2% 3.3% 12.1% B16..8: 7.4% 28.9% 44.4% direct: 2.7% skip: 1.0% L0:51.2% L1:25.0% BI:23.7%\n", + "[libx264 @ 0000021b061559c0] 8x8 transform intra:20.7% inter:17.7%\n", + "[libx264 @ 0000021b061559c0] coded y,u,v intra: 91.2% 74.6% 73.1% inter: 83.6% 47.8% 46.7%\n", + "[libx264 @ 0000021b061559c0] i16 v,h,dc,p: 17% 0% 33% 50%\n", + "[libx264 @ 0000021b061559c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 48% 11% 12% 2% 2% 10% 3% 8% 4%\n", + "[libx264 @ 0000021b061559c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 14% 14% 6% 7% 10% 8% 8% 7%\n", + "[libx264 @ 0000021b061559c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000021b061559c0] ref P L0: 77.6% 6.8% 15.6%\n", + "[libx264 @ 0000021b061559c0] ref B L0: 85.0% 15.0%\n", + "[libx264 @ 0000021b061559c0] kb/s:135.78\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS27/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.16, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000014df2433b40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000014df2433b40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000014df2433b40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/18.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 4 fps=0.0 q=-1.0 Lsize= 58kB time=00:00:01.00 bitrate= 476.5kbits/s speed=35.1x \n", + "video:57kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.530699%\n", + "[libx264 @ 0000014df2433b40] frame I:1 Avg QP:18.11 size: 16718\n", + "[libx264 @ 0000014df2433b40] frame P:1 Avg QP:18.70 size: 14584\n", + "[libx264 @ 0000014df2433b40] frame B:2 Avg QP:18.79 size: 13346\n", + "[libx264 @ 0000014df2433b40] consecutive B-frames: 25.0% 0.0% 75.0% 0.0%\n", + "[libx264 @ 0000014df2433b40] mb I I16..4: 7.4% 42.2% 50.4%\n", + "[libx264 @ 0000014df2433b40] mb P I16..4: 0.8% 42.2% 49.2% P16..4: 1.6% 4.7% 1.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000014df2433b40] mb B I16..4: 0.4% 18.6% 21.3% B16..8: 14.6% 24.8% 13.2% direct: 7.2% skip: 0.0% L0:53.9% L1:30.0% BI:16.1%\n", + "[libx264 @ 0000014df2433b40] 8x8 transform intra:44.6% inter:39.0%\n", + "[libx264 @ 0000014df2433b40] coded y,u,v intra: 99.1% 49.5% 41.4% inter: 94.3% 22.2% 20.0%\n", + "[libx264 @ 0000014df2433b40] i16 v,h,dc,p: 0% 13% 43% 43%\n", + "[libx264 @ 0000014df2433b40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 31% 14% 3% 5% 5% 8% 5% 10%\n", + "[libx264 @ 0000014df2433b40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 26% 12% 4% 6% 6% 8% 6% 8%\n", + "[libx264 @ 0000014df2433b40] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000014df2433b40] ref B L0: 84.4% 15.6%\n", + "[libx264 @ 0000014df2433b40] kb/s:115.99\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS28/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000021ad998af80] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000021ad998af80] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000021ad998af80] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/19.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 140kB time=00:00:02.00 bitrate= 575.2kbits/s speed=41.9x \n", + "video:140kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.619984%\n", + "[libx264 @ 0000021ad998af80] frame I:1 Avg QP:19.64 size: 31201\n", + "[libx264 @ 0000021ad998af80] frame P:4 Avg QP:18.86 size: 27758\n", + "[libx264 @ 0000021ad998af80] mb I I16..4: 0.4% 47.3% 52.3%\n", + "[libx264 @ 0000021ad998af80] mb P I16..4: 0.0% 36.0% 57.1% P16..4: 0.1% 3.4% 3.3% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000021ad998af80] 8x8 transform intra:40.5% inter:20.0%\n", + "[libx264 @ 0000021ad998af80] coded y,u,v intra: 100.0% 34.9% 19.4% inter: 100.0% 0.4% 0.0%\n", + "[libx264 @ 0000021ad998af80] i16 v,h,dc,p: 0% 100% 0% 0%\n", + "[libx264 @ 0000021ad998af80] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 13% 16% 27% 7% 5% 4% 7% 7% 14%\n", + "[libx264 @ 0000021ad998af80] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 10% 11% 23% 10% 7% 7% 9% 10% 13%\n", + "[libx264 @ 0000021ad998af80] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000021ad998af80] ref P L0: 77.5% 21.8% 0.0% 0.7%\n", + "[libx264 @ 0000021ad998af80] kb/s:227.57\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS29/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000002d6b4a04f40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000002d6b4a04f40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000002d6b4a04f40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/20.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 128kB time=00:00:02.00 bitrate= 523.5kbits/s speed= 48x \n", + "video:127kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.700189%\n", + "[libx264 @ 000002d6b4a04f40] frame I:1 Avg QP:19.31 size: 30720\n", + "[libx264 @ 000002d6b4a04f40] frame P:2 Avg QP:17.42 size: 22201\n", + "[libx264 @ 000002d6b4a04f40] frame B:2 Avg QP:19.87 size: 27085\n", + "[libx264 @ 000002d6b4a04f40] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 000002d6b4a04f40] mb I I16..4: 0.0% 3.5% 96.5%\n", + "[libx264 @ 000002d6b4a04f40] mb P I16..4: 1.0% 22.3% 57.0% P16..4: 6.6% 4.3% 4.3% 0.0% 0.0% skip: 4.5%\n", + "[libx264 @ 000002d6b4a04f40] mb B I16..4: 0.6% 4.3% 45.1% B16..8: 9.2% 15.4% 21.2% direct: 1.6% skip: 2.5% L0:35.6% L1:19.8% BI:44.6%\n", + "[libx264 @ 000002d6b4a04f40] 8x8 transform intra:15.7% inter:14.9%\n", + "[libx264 @ 000002d6b4a04f40] coded y,u,v intra: 94.8% 63.9% 57.7% inter: 66.6% 30.7% 17.3%\n", + "[libx264 @ 000002d6b4a04f40] i16 v,h,dc,p: 0% 38% 0% 62%\n", + "[libx264 @ 000002d6b4a04f40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 2% 42% 26% 3% 2% 1% 4% 1% 18%\n", + "[libx264 @ 000002d6b4a04f40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 24% 16% 6% 10% 6% 13% 6% 14%\n", + "[libx264 @ 000002d6b4a04f40] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000002d6b4a04f40] ref P L0: 75.3% 16.0% 7.1% 1.6%\n", + "[libx264 @ 000002d6b4a04f40] ref B L0: 88.5% 11.5%\n", + "[libx264 @ 000002d6b4a04f40] kb/s:206.87\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS2RL/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000014e61625000] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000014e61625000] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000014e61625000] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/21.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 96kB time=00:00:02.00 bitrate= 395.1kbits/s speed= 51x \n", + "video:96kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.905014%\n", + "[libx264 @ 0000014e61625000] frame I:1 Avg QP:19.25 size: 24737\n", + "[libx264 @ 0000014e61625000] frame P:4 Avg QP:19.00 size: 18122\n", + "[libx264 @ 0000014e61625000] mb I I16..4: 1.2% 22.7% 76.2%\n", + "[libx264 @ 0000014e61625000] mb P I16..4: 1.5% 31.3% 59.1% P16..4: 2.4% 3.8% 1.9% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000014e61625000] 8x8 transform intra:31.7% inter:27.7%\n", + "[libx264 @ 0000014e61625000] coded y,u,v intra: 98.3% 63.5% 58.5% inter: 91.9% 20.2% 23.2%\n", + "[libx264 @ 0000014e61625000] i16 v,h,dc,p: 6% 28% 11% 56%\n", + "[libx264 @ 0000014e61625000] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 23% 14% 5% 5% 7% 7% 8% 11%\n", + "[libx264 @ 0000014e61625000] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 22% 12% 6% 6% 7% 7% 7% 9%\n", + "[libx264 @ 0000014e61625000] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000014e61625000] ref P L0: 85.5% 12.7% 1.2% 0.6%\n", + "[libx264 @ 0000014e61625000] kb/s:155.56\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS30/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000029f2e9e7fc0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000029f2e9e7fc0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000029f2e9e7fc0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/22.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 163kB time=00:00:02.00 bitrate= 669.0kbits/s speed=55.9x \n", + "video:162kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.544648%\n", + "[libx264 @ 0000029f2e9e7fc0] frame I:2 Avg QP:18.44 size: 38284\n", + "[libx264 @ 0000029f2e9e7fc0] frame P:2 Avg QP:19.15 size: 32164\n", + "[libx264 @ 0000029f2e9e7fc0] frame B:1 Avg QP:19.20 size: 24776\n", + "[libx264 @ 0000029f2e9e7fc0] consecutive B-frames: 60.0% 40.0% 0.0% 0.0%\n", + "[libx264 @ 0000029f2e9e7fc0] mb I I16..4: 0.0% 23.4% 76.6%\n", + "[libx264 @ 0000029f2e9e7fc0] mb P I16..4: 0.0% 29.7% 49.4% P16..4: 2.1% 8.4% 10.4% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000029f2e9e7fc0] mb B I16..4: 0.0% 0.0% 9.0% B16..8: 5.5% 41.4% 42.1% direct: 2.0% skip: 0.0% L0:31.5% L1:16.7% BI:51.8%\n", + "[libx264 @ 0000029f2e9e7fc0] 8x8 transform intra:28.9% inter:20.9%\n", + "[libx264 @ 0000029f2e9e7fc0] coded y,u,v intra: 100.0% 87.6% 78.0% inter: 99.2% 31.7% 14.6%\n", + "[libx264 @ 0000029f2e9e7fc0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 9% 23% 11% 6% 6% 10% 6% 20%\n", + "[libx264 @ 0000029f2e9e7fc0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 6% 12% 19% 11% 8% 8% 11% 8% 17%\n", + "[libx264 @ 0000029f2e9e7fc0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000029f2e9e7fc0] kb/s:265.08\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS31/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001ca25090140] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001ca25090140] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001ca25090140] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/23.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 171kB time=00:00:02.00 bitrate= 700.9kbits/s speed=44.3x \n", + "video:170kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.526654%\n", + "[libx264 @ 000001ca25090140] frame I:1 Avg QP:19.84 size: 33538\n", + "[libx264 @ 000001ca25090140] frame P:1 Avg QP:20.46 size: 37540\n", + "[libx264 @ 000001ca25090140] frame B:3 Avg QP:20.26 size: 34186\n", + "[libx264 @ 000001ca25090140] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 000001ca25090140] mb I I16..4: 0.4% 12.5% 87.1%\n", + "[libx264 @ 000001ca25090140] mb P I16..4: 0.4% 23.4% 75.4% P16..4: 0.0% 0.4% 0.4% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001ca25090140] mb B I16..4: 0.1% 15.0% 47.0% B16..8: 2.0% 16.8% 18.0% direct: 1.2% skip: 0.0% L0:34.5% L1:14.5% BI:51.0%\n", + "[libx264 @ 000001ca25090140] 8x8 transform intra:21.0% inter:8.2%\n", + "[libx264 @ 000001ca25090140] coded y,u,v intra: 99.2% 80.4% 70.0% inter: 96.9% 29.6% 19.1%\n", + "[libx264 @ 000001ca25090140] i16 v,h,dc,p: 33% 0% 67% 0%\n", + "[libx264 @ 000001ca25090140] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 7% 16% 26% 8% 6% 3% 8% 6% 20%\n", + "[libx264 @ 000001ca25090140] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 13% 19% 9% 7% 7% 12% 6% 19%\n", + "[libx264 @ 000001ca25090140] Weighted P-Frames: Y:100.0% UV:100.0%\n", + "[libx264 @ 000001ca25090140] ref B L0: 98.2% 1.8%\n", + "[libx264 @ 000001ca25090140] ref B L1: 96.3% 3.7%\n", + "[libx264 @ 000001ca25090140] kb/s:277.82\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS32/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000020da08711c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000020da08711c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000020da08711c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/24.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 117kB time=00:00:02.00 bitrate= 477.2kbits/s speed= 48x \n", + "video:116kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.761773%\n", + "[libx264 @ 0000020da08711c0] frame I:1 Avg QP:17.66 size: 25303\n", + "[libx264 @ 0000020da08711c0] frame P:2 Avg QP:17.94 size: 24466\n", + "[libx264 @ 0000020da08711c0] frame B:2 Avg QP:18.32 size: 21750\n", + "[libx264 @ 0000020da08711c0] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 0000020da08711c0] mb I I16..4: 0.4% 30.1% 69.5%\n", + "[libx264 @ 0000020da08711c0] mb P I16..4: 0.4% 30.7% 56.2% P16..4: 4.7% 4.9% 2.7% 0.0% 0.0% skip: 0.4%\n", + "[libx264 @ 0000020da08711c0] mb B I16..4: 0.6% 20.9% 46.7% B16..8: 6.1% 12.3% 10.3% direct: 0.8% skip: 2.5% L0:38.2% L1:21.7% BI:40.1%\n", + "[libx264 @ 0000020da08711c0] 8x8 transform intra:32.4% inter:16.7%\n", + "[libx264 @ 0000020da08711c0] coded y,u,v intra: 98.2% 46.2% 40.1% inter: 77.0% 40.9% 14.7%\n", + "[libx264 @ 0000020da08711c0] i16 v,h,dc,p: 0% 33% 17% 50%\n", + "[libx264 @ 0000020da08711c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 9% 23% 8% 10% 11% 5% 13% 11%\n", + "[libx264 @ 0000020da08711c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 13% 15% 10% 11% 10% 11% 8% 12%\n", + "[libx264 @ 0000020da08711c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000020da08711c0] ref P L0: 88.1% 5.6% 6.3%\n", + "[libx264 @ 0000020da08711c0] ref B L0: 81.7% 18.3%\n", + "[libx264 @ 0000020da08711c0] kb/s:188.38\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS33/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000027504ce4f40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000027504ce4f40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000027504ce4f40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/25.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 69kB time=00:00:02.00 bitrate= 282.6kbits/s speed=56.3x \n", + "video:68kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.304978%\n", + "[libx264 @ 0000027504ce4f40] frame I:1 Avg QP:17.62 size: 16818\n", + "[libx264 @ 0000027504ce4f40] frame P:2 Avg QP:16.82 size: 14090\n", + "[libx264 @ 0000027504ce4f40] frame B:2 Avg QP:16.57 size: 12030\n", + "[libx264 @ 0000027504ce4f40] consecutive B-frames: 40.0% 0.0% 60.0% 0.0%\n", + "[libx264 @ 0000027504ce4f40] mb I I16..4: 1.6% 28.5% 69.9%\n", + "[libx264 @ 0000027504ce4f40] mb P I16..4: 3.7% 23.6% 42.6% P16..4: 4.7% 18.4% 7.0% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000027504ce4f40] mb B I16..4: 0.0% 11.7% 25.4% B16..8: 7.0% 33.6% 18.3% direct: 3.7% skip: 0.2% L0:44.5% L1:23.5% BI:32.0%\n", + "[libx264 @ 0000027504ce4f40] 8x8 transform intra:31.6% inter:47.3%\n", + "[libx264 @ 0000027504ce4f40] coded y,u,v intra: 97.9% 66.5% 61.1% inter: 82.3% 24.2% 17.2%\n", + "[libx264 @ 0000027504ce4f40] i16 v,h,dc,p: 9% 61% 9% 22%\n", + "[libx264 @ 0000027504ce4f40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 21% 16% 7% 4% 6% 6% 6% 10%\n", + "[libx264 @ 0000027504ce4f40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 39% 12% 10% 5% 6% 12% 4% 7% 5%\n", + "[libx264 @ 0000027504ce4f40] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000027504ce4f40] ref P L0: 73.5% 24.4% 0.6% 1.5%\n", + "[libx264 @ 0000027504ce4f40] ref B L0: 98.3% 1.7%\n", + "[libx264 @ 0000027504ce4f40] kb/s:110.50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS34/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000023a5bda4840] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000023a5bda4840] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000023a5bda4840] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/26.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 84kB time=00:00:02.00 bitrate= 342.2kbits/s speed=48.4x \n", + "video:83kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.046453%\n", + "[libx264 @ 0000023a5bda4840] frame I:1 Avg QP:16.90 size: 19126\n", + "[libx264 @ 0000023a5bda4840] frame P:4 Avg QP:17.69 size: 16217\n", + "[libx264 @ 0000023a5bda4840] mb I I16..4: 3.9% 48.4% 47.7%\n", + "[libx264 @ 0000023a5bda4840] mb P I16..4: 0.7% 13.5% 22.8% P16..4: 8.9% 32.9% 21.3% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000023a5bda4840] 8x8 transform intra:41.3% inter:36.5%\n", + "[libx264 @ 0000023a5bda4840] coded y,u,v intra: 97.3% 73.9% 74.1% inter: 91.8% 27.4% 27.4%\n", + "[libx264 @ 0000023a5bda4840] i16 v,h,dc,p: 76% 0% 6% 18%\n", + "[libx264 @ 0000023a5bda4840] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 23% 13% 6% 7% 4% 10% 4% 17%\n", + "[libx264 @ 0000023a5bda4840] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 17% 11% 7% 7% 6% 15% 6% 12%\n", + "[libx264 @ 0000023a5bda4840] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000023a5bda4840] ref P L0: 72.9% 22.1% 3.7% 1.2%\n", + "[libx264 @ 0000023a5bda4840] kb/s:134.39\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS35/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001d381ec1080] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001d381ec1080] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001d381ec1080] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/27.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 72kB time=00:00:02.00 bitrate= 295.1kbits/s speed=55.3x \n", + "video:71kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.259916%\n", + "[libx264 @ 000001d381ec1080] frame I:1 Avg QP:17.63 size: 15326\n", + "[libx264 @ 000001d381ec1080] frame P:1 Avg QP:18.37 size: 14402\n", + "[libx264 @ 000001d381ec1080] frame B:3 Avg QP:18.22 size: 14154\n", + "[libx264 @ 000001d381ec1080] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 000001d381ec1080] mb I I16..4: 3.1% 50.8% 46.1%\n", + "[libx264 @ 000001d381ec1080] mb P I16..4: 2.7% 41.8% 52.7% P16..4: 0.8% 0.4% 1.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001d381ec1080] mb B I16..4: 0.7% 20.3% 32.4% B16..8: 5.9% 21.4% 11.9% direct: 6.9% skip: 0.7% L0:26.6% L1:44.7% BI:28.7%\n", + "[libx264 @ 000001d381ec1080] 8x8 transform intra:43.0% inter:49.2%\n", + "[libx264 @ 000001d381ec1080] coded y,u,v intra: 97.9% 54.9% 55.1% inter: 93.6% 35.7% 31.9%\n", + "[libx264 @ 000001d381ec1080] i16 v,h,dc,p: 15% 65% 5% 15%\n", + "[libx264 @ 000001d381ec1080] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 35% 7% 4% 6% 5% 11% 6% 12%\n", + "[libx264 @ 000001d381ec1080] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 28% 28% 8% 4% 6% 5% 8% 5% 8%\n", + "[libx264 @ 000001d381ec1080] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001d381ec1080] ref B L0: 97.5% 2.5%\n", + "[libx264 @ 000001d381ec1080] ref B L1: 95.9% 4.1%\n", + "[libx264 @ 000001d381ec1080] kb/s:115.50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS36/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 00000157d49d6840] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 00000157d49d6840] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 00000157d49d6840] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/28.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 65kB time=00:00:02.00 bitrate= 266.1kbits/s speed=52.4x \n", + "video:64kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.399284%\n", + "[libx264 @ 00000157d49d6840] frame I:1 Avg QP:18.00 size: 15274\n", + "[libx264 @ 00000157d49d6840] frame P:1 Avg QP:15.78 size: 10023\n", + "[libx264 @ 00000157d49d6840] frame B:3 Avg QP:17.52 size: 13212\n", + "[libx264 @ 00000157d49d6840] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%\n", + "[libx264 @ 00000157d49d6840] mb I I16..4: 0.8% 38.7% 60.5%\n", + "[libx264 @ 00000157d49d6840] mb P I16..4: 9.8% 47.3% 43.0% P16..4: 0.0% 0.0% 0.0% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 00000157d49d6840] mb B I16..4: 0.5% 18.1% 29.8% B16..8: 5.7% 27.7% 13.2% direct: 4.7% skip: 0.3% L0:28.4% L1:35.0% BI:36.6%\n", + "[libx264 @ 00000157d49d6840] 8x8 transform intra:40.6% inter:45.3%\n", + "[libx264 @ 00000157d49d6840] coded y,u,v intra: 91.7% 44.5% 40.3% inter: 94.4% 16.2% 11.0%\n", + "[libx264 @ 00000157d49d6840] i16 v,h,dc,p: 77% 3% 13% 6%\n", + "[libx264 @ 00000157d49d6840] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 24% 12% 3% 8% 5% 13% 3% 15%\n", + "[libx264 @ 00000157d49d6840] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 38% 17% 10% 4% 5% 4% 8% 4% 9%\n", + "[libx264 @ 00000157d49d6840] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 00000157d49d6840] ref B L0: 98.8% 1.2%\n", + "[libx264 @ 00000157d49d6840] ref B L1: 99.5% 0.5%\n", + "[libx264 @ 00000157d49d6840] kb/s:103.89\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS3LR/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001c9199dfa40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001c9199dfa40] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001c9199dfa40] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/29.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 90kB time=00:00:02.00 bitrate= 367.9kbits/s speed=48.3x \n", + "video:89kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.972643%\n", + "[libx264 @ 000001c9199dfa40] frame I:1 Avg QP:18.10 size: 15391\n", + "[libx264 @ 000001c9199dfa40] frame P:4 Avg QP:18.81 size: 18757\n", + "[libx264 @ 000001c9199dfa40] mb I I16..4: 2.3% 51.2% 46.5%\n", + "[libx264 @ 000001c9199dfa40] mb P I16..4: 0.6% 37.5% 56.4% P16..4: 1.2% 2.1% 2.1% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001c9199dfa40] 8x8 transform intra:42.1% inter:28.6%\n", + "[libx264 @ 000001c9199dfa40] coded y,u,v intra: 99.7% 66.8% 71.0% inter: 98.7% 28.1% 40.6%\n", + "[libx264 @ 000001c9199dfa40] i16 v,h,dc,p: 0% 75% 17% 8%\n", + "[libx264 @ 000001c9199dfa40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 15% 31% 9% 5% 7% 8% 6% 8% 10%\n", + "[libx264 @ 000001c9199dfa40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 21% 10% 7% 12% 9% 10% 7% 10%\n", + "[libx264 @ 000001c9199dfa40] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001c9199dfa40] ref P L0: 61.2% 16.1% 11.2% 11.6%\n", + "[libx264 @ 000001c9199dfa40] kb/s:144.67\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS4/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001b214983280] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001b214983280] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001b214983280] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/30.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 114kB time=00:00:02.00 bitrate= 467.2kbits/s speed=43.4x \n", + "video:113kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.764426%\n", + "[libx264 @ 000001b214983280] frame I:1 Avg QP:19.70 size: 23596\n", + "[libx264 @ 000001b214983280] frame P:4 Avg QP:19.62 size: 22909\n", + "[libx264 @ 000001b214983280] mb I I16..4: 0.0% 31.2% 68.8%\n", + "[libx264 @ 000001b214983280] mb P I16..4: 0.1% 29.5% 69.0% P16..4: 0.4% 0.5% 0.5% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 000001b214983280] 8x8 transform intra:30.2% inter:57.1%\n", + "[libx264 @ 000001b214983280] coded y,u,v intra: 99.7% 75.1% 74.1% inter: 98.2% 57.1% 57.1%\n", + "[libx264 @ 000001b214983280] i16 v,h,dc,p: 0% 0% 0% 100%\n", + "[libx264 @ 000001b214983280] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 36% 10% 5% 5% 7% 7% 6% 8%\n", + "[libx264 @ 000001b214983280] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 13% 21% 12% 8% 9% 8% 10% 8% 11%\n", + "[libx264 @ 000001b214983280] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001b214983280] ref P L0: 44.6% 28.6% 19.6% 7.1%\n", + "[libx264 @ 000001b214983280] kb/s:184.37\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS5/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000017742ce9380] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000017742ce9380] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000017742ce9380] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/31.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 137kB time=00:00:02.00 bitrate= 561.2kbits/s speed=44.3x \n", + "video:136kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.635513%\n", + "[libx264 @ 0000017742ce9380] frame I:1 Avg QP:20.05 size: 27908\n", + "[libx264 @ 0000017742ce9380] frame P:4 Avg QP:20.07 size: 27708\n", + "[libx264 @ 0000017742ce9380] mb I I16..4: 0.8% 7.4% 91.8%\n", + "[libx264 @ 0000017742ce9380] mb P I16..4: 1.0% 13.1% 70.2% P16..4: 0.5% 4.6% 10.6% 0.0% 0.0% skip: 0.0%\n", + "[libx264 @ 0000017742ce9380] 8x8 transform intra:13.7% inter:6.2%\n", + "[libx264 @ 0000017742ce9380] coded y,u,v intra: 99.8% 80.3% 78.2% inter: 99.7% 30.1% 37.3%\n", + "[libx264 @ 0000017742ce9380] i16 v,h,dc,p: 0% 0% 25% 75%\n", + "[libx264 @ 0000017742ce9380] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 6% 9% 16% 12% 5% 5% 16% 8% 22%\n", + "[libx264 @ 0000017742ce9380] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 10% 14% 10% 9% 8% 17% 8% 16%\n", + "[libx264 @ 0000017742ce9380] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000017742ce9380] ref P L0: 74.1% 19.9% 4.0% 2.0%\n", + "[libx264 @ 0000017742ce9380] kb/s:221.99\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS6/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000024dc0b848c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000024dc0b848c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000024dc0b848c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/32.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 48kB time=00:00:02.00 bitrate= 197.4kbits/s speed=61.9x \n", + "video:47kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.836604%\n", + "[libx264 @ 0000024dc0b848c0] frame I:2 Avg QP:15.01 size: 10311\n", + "[libx264 @ 0000024dc0b848c0] frame P:3 Avg QP:16.74 size: 9055\n", + "[libx264 @ 0000024dc0b848c0] mb I I16..4: 23.4% 9.2% 67.4%\n", + "[libx264 @ 0000024dc0b848c0] mb P I16..4: 7.2% 5.3% 47.3% P16..4: 12.2% 14.3% 9.4% 0.0% 0.0% skip: 4.3%\n", + "[libx264 @ 0000024dc0b848c0] 8x8 transform intra:9.1% inter:19.6%\n", + "[libx264 @ 0000024dc0b848c0] coded y,u,v intra: 66.0% 37.0% 33.6% inter: 44.7% 11.5% 7.2%\n", + "[libx264 @ 0000024dc0b848c0] i16 v,h,dc,p: 53% 38% 8% 1%\n", + "[libx264 @ 0000024dc0b848c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 18% 47% 3% 8% 1% 2% 1% 4%\n", + "[libx264 @ 0000024dc0b848c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 36% 15% 4% 5% 5% 4% 4% 4%\n", + "[libx264 @ 0000024dc0b848c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000024dc0b848c0] ref P L0: 73.0% 6.4% 14.5% 6.1%\n", + "[libx264 @ 0000024dc0b848c0] kb/s:76.46\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS7RL/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000001bbbf4747c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000001bbbf4747c0] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000001bbbf4747c0] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/33.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 55kB time=00:00:02.00 bitrate= 224.5kbits/s speed= 58x \n", + "video:54kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.603620%\n", + "[libx264 @ 000001bbbf4747c0] frame I:1 Avg QP:15.90 size: 9993\n", + "[libx264 @ 000001bbbf4747c0] frame P:4 Avg QP:16.64 size: 11146\n", + "[libx264 @ 000001bbbf4747c0] mb I I16..4: 7.8% 23.4% 68.8%\n", + "[libx264 @ 000001bbbf4747c0] mb P I16..4: 8.1% 16.3% 63.2% P16..4: 3.8% 5.3% 3.1% 0.0% 0.0% skip: 0.2%\n", + "[libx264 @ 000001bbbf4747c0] 8x8 transform intra:19.7% inter:29.7%\n", + "[libx264 @ 000001bbbf4747c0] coded y,u,v intra: 65.4% 44.2% 39.8% inter: 54.9% 25.8% 20.1%\n", + "[libx264 @ 000001bbbf4747c0] i16 v,h,dc,p: 53% 34% 10% 3%\n", + "[libx264 @ 000001bbbf4747c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 30% 36% 18% 2% 1% 3% 2% 3% 4%\n", + "[libx264 @ 000001bbbf4747c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 35% 11% 4% 5% 7% 5% 4% 4%\n", + "[libx264 @ 000001bbbf4747c0] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000001bbbf4747c0] ref P L0: 73.4% 9.0% 13.6% 4.0%\n", + "[libx264 @ 000001bbbf4747c0] kb/s:87.32\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS8RL/0.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 0000022c36817e00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0000022c36817e00] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 0000022c36817e00] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/34.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 60kB time=00:00:02.00 bitrate= 245.6kbits/s speed=59.5x \n", + "video:59kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.464221%\n", + "[libx264 @ 0000022c36817e00] frame I:1 Avg QP:16.23 size: 10754\n", + "[libx264 @ 0000022c36817e00] frame P:4 Avg QP:17.58 size: 12271\n", + "[libx264 @ 0000022c36817e00] mb I I16..4: 8.6% 18.0% 73.4%\n", + "[libx264 @ 0000022c36817e00] mb P I16..4: 8.4% 10.7% 66.8% P16..4: 6.2% 4.8% 2.6% 0.0% 0.0% skip: 0.4%\n", + "[libx264 @ 0000022c36817e00] 8x8 transform intra:13.7% inter:11.1%\n", + "[libx264 @ 0000022c36817e00] coded y,u,v intra: 72.6% 46.8% 41.0% inter: 55.4% 12.5% 9.0%\n", + "[libx264 @ 0000022c36817e00] i16 v,h,dc,p: 44% 43% 8% 5%\n", + "[libx264 @ 0000022c36817e00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 35% 27% 20% 3% 2% 3% 3% 2% 5%\n", + "[libx264 @ 0000022c36817e00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 38% 11% 4% 4% 6% 4% 5% 5%\n", + "[libx264 @ 0000022c36817e00] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0000022c36817e00] ref P L0: 59.8% 3.0% 23.9% 13.2%\n", + "[libx264 @ 0000022c36817e00] kb/s:95.74\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "working image: raw/3dDS9/0.png\n", + "minFileCount: 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, image2, from 'output/tmp/%d.png':\n", + " Duration: 00:00:00.20, start: 0.000000, bitrate: N/A\n", + " Stream #0:0: Video: png, rgb24(pc, gbr/unknown/unknown), 256x256, 25 fps, 25 tbr, 25 tbn\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", + "Press [q] to stop, [?] for help\n", + "[libx264 @ 000002b72e2c3440] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 000002b72e2c3440] profile High 4:4:4 Predictive, level 1.2, 4:4:4, 8-bit\n", + "[libx264 @ 000002b72e2c3440] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=8 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "Output #0, mp4, to 'output/35.mp4':\n", + " Metadata:\n", + " encoder : Lavf60.4.101\n", + " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 256x256, q=2-31, 1 fps, 16384 tbn\n", + " Metadata:\n", + " encoder : Lavc60.7.100 libx264\n", + " Side data:\n", + " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", + "frame= 0 fps=0.0 q=0.0 size= 0kB time=-577014:32:22.77 bitrate= -0.0kbits/s speed=N/A \n", + "frame= 5 fps=0.0 q=-1.0 Lsize= 92kB time=00:00:02.00 bitrate= 378.2kbits/s speed=49.5x \n", + "video:91kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.946046%\n", + "[libx264 @ 000002b72e2c3440] frame I:1 Avg QP:17.01 size: 16001\n", + "[libx264 @ 000002b72e2c3440] frame P:4 Avg QP:17.26 size: 19245\n", + "[libx264 @ 000002b72e2c3440] mb I I16..4: 7.8% 33.6% 58.6%\n", + "[libx264 @ 000002b72e2c3440] mb P I16..4: 2.2% 37.3% 46.1% P16..4: 5.2% 5.2% 3.4% 0.0% 0.0% skip: 0.6%\n", + "[libx264 @ 000002b72e2c3440] 8x8 transform intra:41.3% inter:30.7%\n", + "[libx264 @ 000002b72e2c3440] coded y,u,v intra: 97.3% 63.6% 57.4% inter: 78.7% 24.1% 19.0%\n", + "[libx264 @ 000002b72e2c3440] i16 v,h,dc,p: 23% 14% 28% 35%\n", + "[libx264 @ 000002b72e2c3440] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 10% 23% 28% 5% 4% 3% 10% 5% 12%\n", + "[libx264 @ 000002b72e2c3440] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 27% 20% 12% 5% 6% 6% 9% 5% 9%\n", + "[libx264 @ 000002b72e2c3440] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 000002b72e2c3440] ref P L0: 74.5% 19.7% 3.4% 2.5%\n", + "[libx264 @ 000002b72e2c3440] kb/s:148.77\n" + ] + } + ], + "source": [ + "from PIL import Image\n", + "from torchvision.transforms.functional import InterpolationMode\n", + "import cv2\n", + "\n", + "testing = False\n", + "\n", + "count = os.listdir(f'raw')\n", + "print(count)\n", + "minFileCount = 1000\n", + "folder = 1\n", + "\n", + "# create csv file\n", + "import pandas as pd\n", + "df = pd.DataFrame(columns=['videoid', 'name', 'page_dir'])\n", + "\n", + "!rm -rf output\n", + "\n", + "for i,dirname in enumerate(count):\n", + " # get list of images from raw/{i} folder\n", + " images = os.listdir(f'raw/{dirname}')\n", + " \n", + " if len(images) < minFileCount:\n", + " minFileCount = len(images)\n", + " folder = dirname\n", + " \n", + " # rename images to 1.jpg, 2.jpg, 3.jpg, etc\n", + " for j in range(len(images)):\n", + " os.rename(f'raw/{dirname}/{images[j]}', f'raw/{dirname}/{j}.png')\n", + " images[j] = f'raw/{dirname}/{j}.png'\n", + " \n", + " img_file_name = images[0]\n", + " \n", + " async with aiofiles.open(img_file_name, \"rb\") as input_file:\n", + " print(\"working image: \", img_file_name)\n", + "\n", + " image_bin = await input_file.read()\n", + " image = Image.open(io.BytesIO(image_bin))\n", + "\n", + " if not image.mode == \"RGB\":\n", + " image = image.convert(\"RGB\")\n", + "\n", + " image = load_image(image, device=torch.device(\"cuda\"))\n", + " # caption the image\n", + " captions = blip_decoder.generate(image, sample=False, num_beams=16, min_length=22, max_length=48, repetition_penalty=1.0)\n", + " \n", + " isRightToLeft = dirname.endswith(\"RL\")\n", + " \n", + " if isRightToLeft:\n", + " caption = \"camera panning right to left, \" + captions[0]\n", + " else:\n", + " caption = \"camera panning left to right, \" + captions[0]\n", + " # save caption as name to csv file\n", + " df.loc[len(df)] = [i, caption, f'raw/{dirname}']\n", + " \n", + " # print(caption)\n", + " \n", + " # turn images into video\n", + " # Get the images\n", + " images = [cv2.imread(image_path) for image_path in images]\n", + " # crop image to center square shape using the shortest side\n", + " images = [image[:, int((image.shape[1] - image.shape[0]) / 2):int((image.shape[1] + image.shape[0]) / 2)] for image in images]\n", + " # resize images to 256x256\n", + " images = [cv2.resize(image, (256, 256)) for image in images]\n", + " \n", + " !rm -rf output\\tmp\n", + " !mkdir output\\tmp\n", + " \n", + " # save frames to output/tmp folder for debugging\n", + " for j,image in enumerate(images):\n", + " cv2.imwrite(f'output/tmp/{j}.png', image)\n", + " \n", + " \n", + " \n", + " !ffmpeg -r 1 -i output/tmp/%d.png -c:v libx264 -vf fps=1 output/{i}.mp4\n", + " \n", + "\n", + " # # Create a video writer\n", + " # video_writer = cv2.VideoWriter(f'output/{i}.mp4', cv2.VideoWriter_fourcc(*'MP4V'), 1, (256, 256))\n", + " \n", + " # # fix moov atom not found error\n", + " # # https://stackoverflow.com/questions/30140432/how-to-fix-moov-atom-not-found\n", + "\n", + " # # Write the images to the video writer\n", + " # for image in images:\n", + " # video_writer.write(image)\n", + " \n", + " \n", + " # # Close the writer\n", + " # video_writer.release()\n", + " \n", + " \n", + " if testing and i > 11:\n", + " break\n", + " \n", + "print(\"minFileCount: \", minFileCount)\n", + " \n", + "# save csv file\n", + "df.to_csv('output.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ffmpeg version N-110126-g4d216654ca-20230330 Copyright (c) 2000-2023 the FFmpeg developers\n", + " built with gcc 12.2.0 (crosstool-NG 1.25.0.152_89671bf)\n", + " configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --disable-libmfx --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --disable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20230330\n", + " libavutil 58. 5.100 / 58. 5.100\n", + " libavcodec 60. 7.100 / 60. 7.100\n", + " libavformat 60. 4.101 / 60. 4.101\n", + " libavdevice 60. 2.100 / 60. 2.100\n", + " libavfilter 9. 5.100 / 9. 5.100\n", + " libswscale 7. 2.100 / 7. 2.100\n", + " libswresample 4. 11.100 / 4. 11.100\n", + " libpostproc 57. 2.100 / 57. 2.100\n", + "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'output/0.mp4':\n", + " Metadata:\n", + " major_brand : isom\n", + " minor_version : 512\n", + " compatible_brands: isomiso2avc1mp41\n", + " encoder : Lavf60.4.101\n", + " Duration: 00:00:06.00, start: 0.000000, bitrate: 169 kb/s\n", + " Stream #0:0[0x1](und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv444p(progressive), 256x256, 168 kb/s, 1 fps, 1 tbr, 16384 tbn (default)\n", + " Metadata:\n", + " handler_name : VideoHandler\n", + " vendor_id : [0][0][0][0]\n", + " encoder : Lavc60.7.100 libx264\n", + "At least one output file must be specified\n" + ] + } + ], + "source": [ + "!ffmpeg -i output/0.mp4" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "animatediff", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data/output.csv b/data/output.csv new file mode 100644 index 0000000000000000000000000000000000000000..17321c150e219af895617de400f81b330d130a1a --- /dev/null +++ b/data/output.csv @@ -0,0 +1,37 @@ +videoid,name,page_dir +0,"camera panning left to right, a group of buildings sitting next to each other on top of a grass covered field in front of a tall building",raw/3dDS10 +1,"camera panning left to right, a computer generated image of a city with a bus on the road and trees on the side of the road",raw/3dDS11 +2,"camera panning left to right, a screenshot of a city street with a yellow taxi cab driving down the middle of the road",raw/3dDS12 +3,"camera panning left to right, a yellow car driving down a street next to a tall orange building with a clock on it's side",raw/3dDS13 +4,"camera panning right to left, a computer generated image of a mountain with trees in the foreground and mountains in the background",raw/3dDS14RL +5,"camera panning left to right, a group of rocks sitting on top of a grass covered field next to a body of water",raw/3dDS15 +6,"camera panning left to right, a painting of a mountain with trees in the foreground and a dark sky in the background",raw/3dDS16 +7,"camera panning left to right, a painting of a pond in the middle of a park with trees on both sides of it",raw/3dDS17 +8,"camera panning left to right, a painting of a pond in the middle of a grassy area with trees on either side of it",raw/3dDS18 +9,"camera panning left to right, a computer generated image of a park with a lot of trees and a lot of rocks on the ground",raw/3dDS19 +10,"camera panning right to left, a bird's - eye view of a row of buildings in a city with trees in the foreground",raw/3dDS1RL +11,"camera panning left to right, a computer generated image of a blue diamond in the middle of a green and leafy area",raw/3dDS20 +12,"camera panning left to right, a computer generated image of a landscape with trees, rocks, and a path through a forest",raw/3dDS21 +13,"camera panning left to right, a computer generated image of a building with a blue roof and a blue roof on top of it",raw/3dDS22 +14,"camera panning left to right, a map of a small town with a lot of trees and bushes in the middle of it",raw/3dDS23 +15,"camera panning left to right, a computer generated image of a building with a blue roof and a green lawn in front of it",raw/3dDS24 +16,"camera panning left to right, an artist's rendering of a floating island in the middle of a large body of water",raw/3dDS25 +17,"camera panning left to right, a couple of white birds floating on top of a lake filled with waterlily green leaves",raw/3dDS26 +18,"camera panning left to right, a bunch of poles that are standing in front of a building with a sign that says shop",raw/3dDS27 +19,"camera panning left to right, a 3d rendering of a jail cell with bars and bars on each side of the cell wall",raw/3dDS28 +20,"camera panning left to right, an artist's rendering of a bridge over a body of water with a building in the background",raw/3dDS29 +21,"camera panning right to left, a bird's eye view of a building with a lot of trees in front of it",raw/3dDS2RL +22,"camera panning left to right, an artist's rendering of a highway with a bridge in the middle and trees on both sides",raw/3dDS30 +23,"camera panning left to right, an aerial view of a freeway with multiple lanes and a bridge in the foreground and a cityscape in the background",raw/3dDS31 +24,"camera panning left to right, an artist's rendering of a bridge over a body of water with a clock tower in the background",raw/3dDS32 +25,"camera panning left to right, a set of stairs leading up to the top of a set of stairs in a dimly lit area",raw/3dDS33 +26,"camera panning left to right, a 3d rendering of a subway station with red and yellow striped barriers and a red and white sign",raw/3dDS34 +27,"camera panning left to right, a screenshot of a subway station with a man in a suit and a woman in a dress",raw/3dDS35 +28,"camera panning left to right, a screenshot of a hallway in a building with a gate and a sign on the wall",raw/3dDS36 +29,"camera panning left to right, a bird's - eye view of a row of houses in the suburbs of a city",raw/3dDS3LR +30,"camera panning left to right, a bird's eye view of a house with solar panels on the top of the roof",raw/3dDS4 +31,"camera panning left to right, a bird's eye view of a row of houses with solar panels on top of them",raw/3dDS5 +32,"camera panning left to right, a screenshot of a city street with a bench on one side and a building on the other",raw/3dDS6 +33,"camera panning right to left, a computer generated image of a computer generated image of a construction site with a truck in the background",raw/3dDS7RL +34,"camera panning right to left, a screenshot of a city with a bunch of green objects in the middle of the street",raw/3dDS8RL +35,"camera panning left to right, a computer generated image of a city street with tall buildings and a clock tower in the distance",raw/3dDS9 diff --git a/data/output/0.mp4 b/data/output/0.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..05d3ba9aca2927f9e8b2cfb9525f5ea7809f7f0f Binary files /dev/null and b/data/output/0.mp4 differ diff 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--no-check-certificate \ No newline at end of file diff --git a/download_bashscripts/2-Lyriel.sh b/download_bashscripts/2-Lyriel.sh new file mode 100644 index 0000000000000000000000000000000000000000..f4f02152cc627d0bdb4c4b45a24ecfc5bbc82b08 --- /dev/null +++ b/download_bashscripts/2-Lyriel.sh @@ -0,0 +1,2 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/72396 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate \ No newline at end of file diff --git a/download_bashscripts/3-RcnzCartoon.sh b/download_bashscripts/3-RcnzCartoon.sh new file mode 100644 index 0000000000000000000000000000000000000000..07f4f69d399e10b0a618501d7f72bcf7da571dd0 --- /dev/null +++ b/download_bashscripts/3-RcnzCartoon.sh @@ -0,0 +1,2 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/71009 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate \ No newline at end of file diff --git a/download_bashscripts/4-MajicMix.sh b/download_bashscripts/4-MajicMix.sh new file mode 100644 index 0000000000000000000000000000000000000000..b287167c5ba8e594d6f183017aa9a231d4ae63b6 --- /dev/null +++ b/download_bashscripts/4-MajicMix.sh @@ -0,0 +1,2 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/79068 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate \ No newline at end of file diff --git a/download_bashscripts/5-RealisticVision.sh b/download_bashscripts/5-RealisticVision.sh new file mode 100644 index 0000000000000000000000000000000000000000..bd7f6f24a9c786bbddf1674a67d30f9939762fb3 --- /dev/null +++ b/download_bashscripts/5-RealisticVision.sh @@ -0,0 +1,2 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/29460 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate \ No newline at end of file diff --git a/download_bashscripts/6-Tusun.sh b/download_bashscripts/6-Tusun.sh new file mode 100644 index 0000000000000000000000000000000000000000..9fc18b3ad1c59139896b68a444276b2d5e52a1ce --- /dev/null +++ b/download_bashscripts/6-Tusun.sh @@ -0,0 +1,3 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/97261 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate +wget https://civitai.com/api/download/models/50705 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate diff --git a/download_bashscripts/7-FilmVelvia.sh b/download_bashscripts/7-FilmVelvia.sh new file mode 100644 index 0000000000000000000000000000000000000000..53aa688a0d5b2ac1bfa5a080cc4151e1a92ed9f0 --- /dev/null +++ b/download_bashscripts/7-FilmVelvia.sh @@ -0,0 +1,3 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/90115 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate +wget https://civitai.com/api/download/models/92475 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate diff --git a/download_bashscripts/8-GhibliBackground.sh b/download_bashscripts/8-GhibliBackground.sh new file mode 100644 index 0000000000000000000000000000000000000000..39b9e76ddf77a842e4f41acbee9e73f62c49eec0 --- /dev/null +++ b/download_bashscripts/8-GhibliBackground.sh @@ -0,0 +1,3 @@ +#!/bin/bash +wget https://civitai.com/api/download/models/102828 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate +wget https://civitai.com/api/download/models/57618 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate diff --git a/environment.yaml b/environment.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ffbf94eaf30eb56ff5f60b72841664daff9c1153 --- /dev/null +++ b/environment.yaml @@ -0,0 +1,224 @@ +name: animatediff +channels: + - pytorch + - nvidia + - conda-forge + - defaults +dependencies: + - asttokens=2.4.1=pyhd8ed1ab_0 + - backports=1.1=pyhd3eb1b0_0 + - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0 + - blas=2.119=mkl + - blas-devel=3.9.0=19_win64_mkl + - brotli-python=1.1.0=py310h00ffb61_1 + - bzip2=1.0.8=hcfcfb64_5 + - ca-certificates=2023.08.22=haa95532_0 + - certifi=2023.7.22=pyhd8ed1ab_0 + - charset-normalizer=3.3.2=pyhd8ed1ab_0 + - colorama=0.4.6=pyhd8ed1ab_0 + - comm=0.1.4=pyhd8ed1ab_0 + - cuda-cccl=12.3.52=0 + - cuda-cudart=11.7.99=0 + - cuda-cudart-dev=11.7.99=0 + - cuda-cupti=11.7.101=0 + - cuda-libraries=11.7.1=0 + - cuda-libraries-dev=11.7.1=0 + - cuda-nvrtc=11.7.99=0 + - cuda-nvrtc-dev=11.7.99=0 + - cuda-nvtx=11.7.91=0 + - cuda-runtime=11.7.1=0 + - debugpy=1.8.0=py310h00ffb61_1 + - decorator=5.1.1=pyhd8ed1ab_0 + - exceptiongroup=1.1.3=pyhd8ed1ab_0 + - executing=2.0.1=pyhd8ed1ab_0 + - freetype=2.12.1=hdaf720e_2 + - idna=3.4=pyhd8ed1ab_0 + - importlib-metadata=6.8.0=pyha770c72_0 + - importlib_metadata=6.8.0=hd8ed1ab_0 + - intel-openmp=2023.2.0=h57928b3_50497 + - ipykernel=6.26.0=pyha63f2e9_0 + - ipython=8.17.2=pyh5737063_0 + - jedi=0.19.1=pyhd8ed1ab_0 + - jpeg=9e=hcfcfb64_3 + - jupyter_client=8.6.0=pyhd8ed1ab_0 + - jupyter_core=5.5.0=py310h5588dad_0 + - lerc=4.0.0=h63175ca_0 + - libblas=3.9.0=19_win64_mkl + - libcblas=3.9.0=19_win64_mkl + - libcublas=11.10.3.66=0 + - libcublas-dev=11.10.3.66=0 + - libcufft=10.7.2.124=0 + - libcufft-dev=10.7.2.124=0 + - libcurand=10.3.4.52=0 + - libcurand-dev=10.3.4.52=0 + - libcusolver=11.4.0.1=0 + - libcusolver-dev=11.4.0.1=0 + - libcusparse=11.7.4.91=0 + - libcusparse-dev=11.7.4.91=0 + - libdeflate=1.19=hcfcfb64_0 + - libffi=3.4.4=hd77b12b_0 + - libhwloc=2.9.3=default_haede6df_1009 + - libiconv=1.17=h8ffe710_0 + - liblapack=3.9.0=19_win64_mkl + - liblapacke=3.9.0=19_win64_mkl + - libnpp=11.7.4.75=0 + - libnpp-dev=11.7.4.75=0 + - libnvjpeg=11.8.0.2=0 + - libnvjpeg-dev=11.8.0.2=0 + - libpng=1.6.39=h19919ed_0 + - libsodium=1.0.18=h8d14728_1 + - libsqlite=3.44.0=hcfcfb64_0 + - libtiff=4.5.0=h8a3f274_0 + - libuv=1.44.2=hcfcfb64_1 + - libwebp=1.3.2=hcfcfb64_1 + - libwebp-base=1.3.2=hcfcfb64_0 + - libxml2=2.11.5=hc3477c8_1 + - libzlib=1.2.13=hcfcfb64_5 + - m2w64-gcc-libgfortran=5.3.0=6 + - m2w64-gcc-libs=5.3.0=7 + - m2w64-gcc-libs-core=5.3.0=7 + - m2w64-gmp=6.1.0=2 + - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 + - matplotlib-inline=0.1.6=pyhd8ed1ab_0 + - mkl=2023.2.0=h6a75c08_50496 + - mkl-devel=2023.2.0=h57928b3_50496 + - mkl-include=2023.2.0=h6a75c08_50496 + - msys2-conda-epoch=20160418=1 + - nest-asyncio=1.5.8=pyhd8ed1ab_0 + - numpy=1.26.0=py310hf667824_0 + - openjpeg=2.5.0=ha2aaf27_2 + - openssl=3.1.4=hcfcfb64_0 + - packaging=23.2=pyhd8ed1ab_0 + - parso=0.8.3=pyhd8ed1ab_0 + - pickleshare=0.7.5=py_1003 + - pip=23.3.1=pyhd8ed1ab_0 + - platformdirs=4.0.0=pyhd8ed1ab_0 + - prompt-toolkit=3.0.41=pyha770c72_0 + - prompt_toolkit=3.0.41=hd8ed1ab_0 + - pthreads-win32=2.9.1=hfa6e2cd_3 + - pure_eval=0.2.2=pyhd8ed1ab_0 + - pygments=2.16.1=pyhd8ed1ab_0 + - pysocks=1.7.1=pyh0701188_6 + - python=3.10.13=h4de0772_0_cpython + - python-dateutil=2.8.2=pyhd8ed1ab_0 + - python_abi=3.10=4_cp310 + - pytorch=1.13.1=py3.10_cuda11.7_cudnn8_0 + - pytorch-cuda=11.7=h16d0643_5 + - pytorch-mutex=1.0=cuda + - pywin32=306=py310h00ffb61_2 + - pyzmq=25.1.1=py310h2849c00_2 + - requests=2.31.0=pyhd8ed1ab_0 + - setuptools=68.2.2=pyhd8ed1ab_0 + - six=1.16.0=pyh6c4a22f_0 + - stack_data=0.6.2=pyhd8ed1ab_0 + - tbb=2021.10.0=h91493d7_2 + - tk=8.6.13=h5226925_1 + - tornado=6.3.3=py310h8d17308_1 + - traitlets=5.13.0=pyhd8ed1ab_0 + - typing_extensions=4.8.0=pyha770c72_0 + - ucrt=10.0.22621.0=h57928b3_0 + - urllib3=2.1.0=pyhd8ed1ab_0 + - vc=14.3=h64f974e_17 + - vc14_runtime=14.36.32532=hdcecf7f_17 + - vs2015_runtime=14.36.32532=h05e6639_17 + - wcwidth=0.2.10=pyhd8ed1ab_0 + - wheel=0.41.3=pyhd8ed1ab_0 + - win_inet_pton=1.1.0=pyhd8ed1ab_6 + - xz=5.4.2=h8cc25b3_0 + - zeromq=4.3.5=h63175ca_0 + - zipp=3.17.0=pyhd8ed1ab_0 + - zlib=1.2.13=hcfcfb64_5 + - zstd=1.5.5=h12be248_0 + - pip: + - aiofiles==23.2.1 + - aiohttp==3.8.6 + - aiosignal==1.3.1 + - altair==5.1.2 + - annotated-types==0.6.0 + - antlr4-python3-runtime==4.9.3 + - anyio==3.7.1 + - appdirs==1.4.4 + - async-timeout==4.0.3 + - attrs==23.1.0 + - beautifulsoup4==4.12.2 + - click==8.1.7 + - cmake==3.27.7 + - contourpy==1.2.0 + - cycler==0.12.1 + - decord==0.6.0 + - diffusers==0.11.1 + - docker-pycreds==0.4.0 + - einops==0.7.0 + - fairscale==0.4.13 + - fastapi==0.104.1 + - ffmpy==0.3.1 + - filelock==3.13.1 + - fonttools==4.44.3 + - frozenlist==1.4.0 + - fsspec==2023.10.0 + - gdown==4.7.1 + - gitdb==4.0.11 + - gitpython==3.1.40 + - gradio==4.3.0 + - gradio-client==0.7.0 + - h11==0.14.0 + - httpcore==1.0.2 + - httpx==0.25.1 + - huggingface-hub==0.19.3 + - imageio==2.27.0 + - importlib-resources==6.1.1 + - jinja2==3.1.2 + - jsonschema==4.19.2 + - jsonschema-specifications==2023.11.1 + - kiwisolver==1.4.5 + - markdown-it-py==3.0.0 + - markupsafe==2.1.3 + - matplotlib==3.8.1 + - mdurl==0.1.2 + - multidict==6.0.4 + - mypy-extensions==1.0.0 + - omegaconf==2.3.0 + - opencv-python==4.8.1.78 + - orjson==3.9.10 + - pandas==2.1.3 + - pillow==10.1.0 + - protobuf==4.25.0 + - psutil==5.9.6 + - pydantic==2.5.1 + - pydantic-core==2.14.3 + - pydub==0.25.1 + - pyparsing==3.1.1 + - pyre-extensions==0.0.23 + - python-multipart==0.0.6 + - pytz==2023.3.post1 + - pyyaml==6.0.1 + - referencing==0.31.0 + - regex==2023.10.3 + - rich==13.7.0 + - rpds-py==0.12.0 + - safetensors==0.4.0 + - semantic-version==2.10.0 + - sentry-sdk==1.35.0 + - setproctitle==1.3.3 + - shellingham==1.5.4 + - smmap==5.0.1 + - sniffio==1.3.0 + - soupsieve==2.5 + - starlette==0.27.0 + - timm==0.9.10 + - tokenizers==0.13.3 + - tomlkit==0.12.0 + - toolz==0.12.0 + - torchaudio==0.13.1 + - torchvision==0.14.1 + - tqdm==4.66.1 + - transformers==4.25.1 + - triton==2.0.0 + - typer==0.9.0 + - typing-inspect==0.9.0 + - tzdata==2023.3 + - uvicorn==0.24.0.post1 + - wandb==0.16.0 + - websockets==11.0.3 + - xformers==0.0.16 + - yarl==1.9.2 \ No newline at end of file diff --git a/models/DreamBooth_LoRA/Put personalized T2I checkpoints here.txt b/models/DreamBooth_LoRA/Put personalized T2I checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/DreamBooth_LoRA/toonyou_beta3.safetensors b/models/DreamBooth_LoRA/toonyou_beta3.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..c2114683322da31eb8d0fcb76df3ce0faba6dac1 --- /dev/null +++ b/models/DreamBooth_LoRA/toonyou_beta3.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52768d2bc4e73af920df3466d0806d9a40727dab0869fc7aa2f3bc82214bb071 +size 2132626252 diff --git a/models/MotionLoRA/Put MotionLoRA checkpoints here.txt b/models/MotionLoRA/Put MotionLoRA checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/Motion_Module/Put motion module checkpoints here.txt b/models/Motion_Module/Put motion module checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/Motion_Module/mm_sd_v14.ckpt b/models/Motion_Module/mm_sd_v14.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..6b4867d1500cd54e27826de22d5c59d3c526d890 --- /dev/null +++ b/models/Motion_Module/mm_sd_v14.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa7fd8a200a89031edd84487e2a757c5315460eca528fa70d4b3885c399bffd5 +size 1672133921 diff --git a/models/Motion_Module/mm_sd_v15.ckpt b/models/Motion_Module/mm_sd_v15.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..cb080b70a8a3058731e1eef275ff7ed7f72cfeae --- /dev/null +++ b/models/Motion_Module/mm_sd_v15.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf16ea656cb16124990c8e2c70a29c793f9841f3a2223073fac8bd89ebd9b69a +size 1672133921 diff --git a/models/Motion_Module/test/config.yaml b/models/Motion_Module/test/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..316822a04c2a53d5821a3598110bd1b1f1848e9c --- /dev/null +++ b/models/Motion_Module/test/config.yaml @@ -0,0 +1,66 @@ +pretrained_model_path: /content/AnimateDiff-SceneFusion/models/StableDiffusion/ +output_dir: /content/AnimateDiff-SceneFusion/models/Motion_Module/test +train_data: + video_folder: data/output + csv_path: data/output.csv + sample_n_frames: 5 + sample_stride: 100 + sample_size: 256 +validation_data: + prompts: + - camera panning right to left, a bird's eye view of a row of buildings in a city + with trees in the foreground, masterpiece, best quality + video_length: 5 + width: 256 + height: 256 + num_inference_steps: 20 + guidance_scale: 12.5 + temporal_context: 24 + use_inv_latent: true + num_inv_steps: 50 +validation_steps: 1 +train_whole_module: false +trainable_modules: +- to_q +train_batch_size: 1 +max_train_steps: 1 +learning_rate: 0.0003 +scale_lr: false +lr_scheduler: constant +lr_warmup_steps: 0 +adam_beta1: 0.9 +adam_beta2: 0.999 +adam_weight_decay: 0.01 +adam_epsilon: 1.0e-08 +max_grad_norm: 1.0 +gradient_accumulation_steps: 1 +gradient_checkpointing: true +checkpointing_steps: 100 +start_global_step: 0 +resume_from_checkpoint: null +mixed_precision: fp16 +use_8bit_adam: false +enable_xformers_memory_efficient_attention: true +seed: 33 +motion_module: /content/AnimateDiff-SceneFusion/models/Motion_Module/mm_sd_v15.ckpt +inference_config_path: configs/inference/inference-v3.yaml +motion_module_pe_multiplier: 1 +dataset_class: MultiTuneAVideoDataset +image_finetune: false +name: scenefusion +use_wandb: true +launcher: launcher +cfg_random_null_text: true +cfg_random_null_text_ratio: 0.1 +unet_checkpoint_path: '' +unet_additional_kwargs: {} +ema_decay: 0.9999 +noise_scheduler_kwargs: null +max_train_epoch: -1 +validation_steps_tuple: +- -1 +num_workers: 32 +checkpointing_epochs: 5 +mixed_precision_training: true +global_seed: 42 +is_debug: false diff --git 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/dev/null +++ b/models/StableDiffusion/README.md @@ -0,0 +1,203 @@ +--- +license: creativeml-openrail-m +tags: +- stable-diffusion +- stable-diffusion-diffusers +- text-to-image +inference: false +extra_gated_prompt: |- + This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. + 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content + 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license + 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) + Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license + + By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well. + +extra_gated_fields: + I have read the License and agree with its terms: checkbox +--- + +# Stable Diffusion v1-5 Model Card + +Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. +For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). + +The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) +checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). + +You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). + +### Diffusers +```py +from diffusers import StableDiffusionPipeline + +model_id = "runwayml/stable-diffusion-v1-5" +pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") +pipe = pipe.to(device) + +prompt = "a photo of an astronaut riding a horse on mars" +image = pipe(prompt).images[0] + +image.save("astronaut_rides_horse.png") +``` +For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) + +### Original GitHub Repository + +1. Download the weights [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) +2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). + +## Model Details +- **Developed by:** Robin Rombach, Patrick Esser +- **Model type:** Diffusion-based text-to-image generation model +- **Language(s):** English +- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. +- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). +- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). +- **Cite as:** + + @InProceedings{Rombach_2022_CVPR, + author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, + title = {High-Resolution Image Synthesis With Latent Diffusion Models}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2022}, + pages = {10684-10695} + } + +# Uses + +## Direct Use +The model is intended for research purposes only. Possible research areas and +tasks include + +- Safe deployment of models which have the potential to generate harmful content. +- Probing and understanding the limitations and biases of generative models. +- Generation of artworks and use in design and other artistic processes. +- Applications in educational or creative tools. +- Research on generative models. + +Excluded uses are described below. + + ### Misuse, Malicious Use, and Out-of-Scope Use +_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. + + +The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. + +#### Out-of-Scope Use +The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. + +#### Misuse and Malicious Use +Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: + +- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. +- Intentionally promoting or propagating discriminatory content or harmful stereotypes. +- Impersonating individuals without their consent. +- Sexual content without consent of the people who might see it. +- Mis- and disinformation +- Representations of egregious violence and gore +- Sharing of copyrighted or licensed material in violation of its terms of use. +- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. + +## Limitations and Bias + +### Limitations + +- The model does not achieve perfect photorealism +- The model cannot render legible text +- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” +- Faces and people in general may not be generated properly. +- The model was trained mainly with English captions and will not work as well in other languages. +- The autoencoding part of the model is lossy +- The model was trained on a large-scale dataset + [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material + and is not fit for product use without additional safety mechanisms and + considerations. +- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. + The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. + +### Bias + +While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. +Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), +which consists of images that are primarily limited to English descriptions. +Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. +This affects the overall output of the model, as white and western cultures are often set as the default. Further, the +ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. + +### Safety Module + +The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. +This checker works by checking model outputs against known hard-coded NSFW concepts. +The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. +Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. +The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. + + +## Training + +**Training Data** +The model developers used the following dataset for training the model: + +- LAION-2B (en) and subsets thereof (see next section) + +**Training Procedure** +Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, + +- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 +- Text prompts are encoded through a ViT-L/14 text-encoder. +- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. +- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. + +We currently provide four checkpoints, which were trained as follows. +- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). + 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). +- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. + 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, +filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). +- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). +- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). +- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). + +- **Hardware:** 32 x 8 x A100 GPUs +- **Optimizer:** AdamW +- **Gradient Accumulations**: 2 +- **Batch:** 32 x 8 x 2 x 4 = 2048 +- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant + +## Evaluation Results +Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, +5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling +steps show the relative improvements of the checkpoints: + +![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) + +Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. +## Environmental Impact + +**Stable Diffusion v1** **Estimated Emissions** +Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. + +- **Hardware Type:** A100 PCIe 40GB +- **Hours used:** 150000 +- **Cloud Provider:** AWS +- **Compute Region:** US-east +- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. + + +## Citation + +```bibtex + @InProceedings{Rombach_2022_CVPR, + author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, + title = {High-Resolution Image Synthesis With Latent Diffusion Models}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2022}, + pages = {10684-10695} + } +``` + +*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* \ No newline at end of file diff --git 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\u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.bin\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.fp16.bin\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.fp16.safetensors\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.non_ema.bin\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.non_ema.safetensors\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdiffusion_pytorch_model.safetensors\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m 5\u001b[0m \u001b[39mfor\u001b[39;00m file \u001b[39min\u001b[39;00m files:\n\u001b[1;32m----> 6\u001b[0m huggingface_hub\u001b[39m.\u001b[39;49mhf_hub_download(\u001b[39m\"\u001b[39;49m\u001b[39mrunwayml/stable-diffusion-v1-5\u001b[39;49m\u001b[39m\"\u001b[39;49m, file, subfolder\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39munet\u001b[39;49m\u001b[39m\"\u001b[39;49m, local_dir\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39munet\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m \u001b[39mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m 116\u001b[0m kwargs \u001b[39m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[39m=\u001b[39mfn\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, has_token\u001b[39m=\u001b[39mhas_token, kwargs\u001b[39m=\u001b[39mkwargs)\n\u001b[1;32m--> 118\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\huggingface_hub\\file_download.py:1461\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[1;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[0;32m 1458\u001b[0m \u001b[39mif\u001b[39;00m local_dir \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 1459\u001b[0m _check_disk_space(expected_size, local_dir)\n\u001b[1;32m-> 1461\u001b[0m http_get(\n\u001b[0;32m 1462\u001b[0m url_to_download,\n\u001b[0;32m 1463\u001b[0m temp_file,\n\u001b[0;32m 1464\u001b[0m proxies\u001b[39m=\u001b[39;49mproxies,\n\u001b[0;32m 1465\u001b[0m resume_size\u001b[39m=\u001b[39;49mresume_size,\n\u001b[0;32m 1466\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m 1467\u001b[0m expected_size\u001b[39m=\u001b[39;49mexpected_size,\n\u001b[0;32m 1468\u001b[0m )\n\u001b[0;32m 1470\u001b[0m \u001b[39mif\u001b[39;00m local_dir \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 1471\u001b[0m logger\u001b[39m.\u001b[39mdebug(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mStoring \u001b[39m\u001b[39m{\u001b[39;00murl\u001b[39m}\u001b[39;00m\u001b[39m in cache at \u001b[39m\u001b[39m{\u001b[39;00mblob_path\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\huggingface_hub\\file_download.py:541\u001b[0m, in \u001b[0;36mhttp_get\u001b[1;34m(url, temp_file, proxies, resume_size, headers, expected_size, _nb_retries)\u001b[0m\n\u001b[0;32m 539\u001b[0m new_resume_size \u001b[39m=\u001b[39m resume_size\n\u001b[0;32m 540\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 541\u001b[0m \u001b[39mfor\u001b[39;00m chunk \u001b[39min\u001b[39;00m r\u001b[39m.\u001b[39miter_content(chunk_size\u001b[39m=\u001b[39mDOWNLOAD_CHUNK_SIZE):\n\u001b[0;32m 542\u001b[0m \u001b[39mif\u001b[39;00m chunk: \u001b[39m# filter out keep-alive new chunks\u001b[39;00m\n\u001b[0;32m 543\u001b[0m progress\u001b[39m.\u001b[39mupdate(\u001b[39mlen\u001b[39m(chunk))\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\requests\\models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 814\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mraw, \u001b[39m\"\u001b[39m\u001b[39mstream\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m 815\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 816\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mraw\u001b[39m.\u001b[39mstream(chunk_size, decode_content\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[39mexcept\u001b[39;00m ProtocolError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 818\u001b[0m \u001b[39mraise\u001b[39;00m ChunkedEncodingError(e)\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\urllib3\\response.py:934\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[1;34m(self, amt, decode_content)\u001b[0m\n\u001b[0;32m 932\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 933\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mnot\u001b[39;00m is_fp_closed(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fp) \u001b[39mor\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_decoded_buffer) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 934\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(amt\u001b[39m=\u001b[39;49mamt, decode_content\u001b[39m=\u001b[39;49mdecode_content)\n\u001b[0;32m 936\u001b[0m \u001b[39mif\u001b[39;00m data:\n\u001b[0;32m 937\u001b[0m \u001b[39myield\u001b[39;00m data\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\urllib3\\response.py:877\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[0;32m 874\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_decoded_buffer) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m amt:\n\u001b[0;32m 875\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_decoded_buffer\u001b[39m.\u001b[39mget(amt)\n\u001b[1;32m--> 877\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raw_read(amt)\n\u001b[0;32m 879\u001b[0m flush_decoder \u001b[39m=\u001b[39m amt \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m (amt \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m data)\n\u001b[0;32m 881\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m data \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_decoded_buffer) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\urllib3\\response.py:812\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 809\u001b[0m fp_closed \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fp, \u001b[39m\"\u001b[39m\u001b[39mclosed\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m 811\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_error_catcher():\n\u001b[1;32m--> 812\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fp_read(amt) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m fp_closed \u001b[39melse\u001b[39;00m \u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 813\u001b[0m \u001b[39mif\u001b[39;00m amt \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m amt \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m data:\n\u001b[0;32m 814\u001b[0m \u001b[39m# Platform-specific: Buggy versions of Python.\u001b[39;00m\n\u001b[0;32m 815\u001b[0m \u001b[39m# Close the connection when no data is returned\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 820\u001b[0m \u001b[39m# not properly close the connection in all cases. There is\u001b[39;00m\n\u001b[0;32m 821\u001b[0m \u001b[39m# no harm in redundantly calling close.\u001b[39;00m\n\u001b[0;32m 822\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fp\u001b[39m.\u001b[39mclose()\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\urllib3\\response.py:797\u001b[0m, in \u001b[0;36mHTTPResponse._fp_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 794\u001b[0m \u001b[39mreturn\u001b[39;00m buffer\u001b[39m.\u001b[39mgetvalue()\n\u001b[0;32m 795\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 796\u001b[0m \u001b[39m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[1;32m--> 797\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fp\u001b[39m.\u001b[39;49mread(amt) \u001b[39mif\u001b[39;00m amt \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fp\u001b[39m.\u001b[39mread()\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\http\\client.py:466\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 463\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlength \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m amt \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlength:\n\u001b[0;32m 464\u001b[0m \u001b[39m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[0;32m 465\u001b[0m amt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlength\n\u001b[1;32m--> 466\u001b[0m s \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mread(amt)\n\u001b[0;32m 467\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m s \u001b[39mand\u001b[39;00m amt:\n\u001b[0;32m 468\u001b[0m \u001b[39m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[0;32m 469\u001b[0m \u001b[39m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[0;32m 470\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_close_conn()\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 703\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m 704\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 705\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[0;32m 706\u001b[0m \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[0;32m 707\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\ssl.py:1307\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1303\u001b[0m \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 1304\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 1305\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[0;32m 1306\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[1;32m-> 1307\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[0;32m 1308\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1309\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", + "File \u001b[1;32mc:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\ssl.py:1163\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 1161\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1162\u001b[0m \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1163\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[0;32m 1164\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1165\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import huggingface_hub\n", + "\n", + "files = [\"config.json\", \"diffusion_pytorch_model.bin\", \"diffusion_pytorch_model.fp16.bin\", \"diffusion_pytorch_model.fp16.safetensors\", \"diffusion_pytorch_model.non_ema.bin\", \"diffusion_pytorch_model.non_ema.safetensors\", \"diffusion_pytorch_model.safetensors\"]\n", + "\n", + "for file in files:\n", + " huggingface_hub.hf_hub_download(\"runwayml/stable-diffusion-v1-5\", file, subfolder=\"unet\", local_dir=\"unet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading...\n", + "From (uriginal): https://drive.google.com/uc?id=1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq\n", + "From (redirected): https://drive.google.com/uc?id=1RqkQuGPaCO5sGZ6V6KZ-jUWmsRu48Kdq&confirm=t&uuid=462a3e11-53f4-4fa7-814e-39c97b936081\n", + "To: c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\models\\Motion_Module\n", + "\n", + " 0%| | 0.00/1.67G [00:00triton==2.0.0) (4.8.0)\n", + "triton is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n" + ] + } + ], + "source": [ + "!python -m pip install triton-2.0.0-cp310-cp310-win_amd64.whl" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import triton" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# download the unet model from the runwayml model hub\n", + "unet = " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HI\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NOTE: Redirects are currently not supported in Windows or MacOs.\n", + "[W C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\distributed\\c10d\\socket.cpp:601] [c10d] The client socket has failed to connect to [Bryant-Laptop]:29500 (system error: 10049 - The requested address is not valid in its context.).\n", + "[W C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\distributed\\c10d\\socket.cpp:601] [c10d] The client socket has failed to connect to [Bryant-Laptop]:29500 (system error: 10049 - The requested address is not valid in its context.).\n", + "[W C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\distributed\\c10d\\socket.cpp:601] [c10d] The client socket has failed to connect to [Bryant-Laptop]:29500 (system error: 10049 - The requested address is not valid in its context.).\n", + "[W C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\distributed\\c10d\\socket.cpp:601] [c10d] The client socket has failed to connect to [Bryant-Laptop]:29500 (system error: 10049 - The requested address is not valid in its context.).\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\train.py\", line 494, in \n", + " main(name=name, launcher=args.launcher, use_wandb=args.wandb, **config)\n", + " File \"c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\train.py\", line 129, in main\n", + " local_rank = init_dist(launcher=launcher)\n", + " File \"c:\\Users\\texas\\Desktop\\School\\SceneFusion\\AnimateDiff-SceneFusion\\train.py\", line 51, in init_dist\n", + " dist.init_process_group(backend=backend, **kwargs)\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\distributed_c10d.py\", line 761, in init_process_group\n", + " default_pg = _new_process_group_helper(\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\distributed_c10d.py\", line 886, in _new_process_group_helper\n", + " raise RuntimeError(\"Distributed package doesn't have NCCL \" \"built in\")\n", + "RuntimeError: Distributed package doesn't have NCCL built in\n", + "ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 11848) of binary: c:\\Users\\texas\\anaconda3\\envs\\animatediff\\python.exe\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\runpy.py\", line 196, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\runpy.py\", line 86, in _run_code\n", + " exec(code, run_globals)\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\run.py\", line 766, in \n", + " main()\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\elastic\\multiprocessing\\errors\\__init__.py\", line 346, in wrapper\n", + " return f(*args, **kwargs)\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\run.py\", line 762, in main\n", + " run(args)\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\run.py\", line 753, in run\n", + " elastic_launch(\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\launcher\\api.py\", line 132, in __call__\n", + " return launch_agent(self._config, self._entrypoint, list(args))\n", + " File \"c:\\Users\\texas\\anaconda3\\envs\\animatediff\\lib\\site-packages\\torch\\distributed\\launcher\\api.py\", line 246, in launch_agent\n", + " raise ChildFailedError(\n", + "torch.distributed.elastic.multiprocessing.errors.ChildFailedError: \n", + "============================================================\n", + "train.py FAILED\n", + "------------------------------------------------------------\n", + "Failures:\n", + " \n", + "------------------------------------------------------------\n", + "Root Cause (first observed failure):\n", + "[0]:\n", + " time : 2023-11-20_17:37:46\n", + " host : Bryant-Laptop.lan\n", + " rank : 0 (local_rank: 0)\n", + " exitcode : 1 (pid: 11848)\n", + " error_file: \n", + " traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n", + "============================================================\n", + "c:\\Users\\texas\\anaconda3\\envs\\animatediff\\python.exe: Error while finding module specification for 'train.py' (ModuleNotFoundError: __path__ attribute not found on 'train' while trying to find 'train.py'). Try using 'train' instead of 'train.py' as the module name.\n" + ] + } + ], + "source": [ + "!python -m torch.distributed.run --nnodes=1 --nproc_per_node=1 train.py --config configs/training/training.yaml\n", + "!python -m train.py --config configs/training/training.yaml" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "animatediff", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5f8d7db837bb5799d6be834f179435210378dfaf Binary files /dev/null and b/requirements.txt differ diff --git a/scripts/animate.py b/scripts/animate.py new file mode 100644 index 0000000000000000000000000000000000000000..de41c469ba7543091c2ac52ce2d8f6cab8fc1c94 --- /dev/null +++ b/scripts/animate.py @@ -0,0 +1,123 @@ +import argparse +import datetime +import inspect +import os +from omegaconf import OmegaConf + +import torch + +import diffusers +from diffusers import AutoencoderKL, DDIMScheduler + +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer + +from animatediff.models.unet import UNet3DConditionModel +from animatediff.pipelines.pipeline_animation import AnimationPipeline +from animatediff.utils.util import save_videos_grid +from animatediff.utils.util import load_weights +from diffusers.utils.import_utils import is_xformers_available + +from einops import rearrange, repeat + +import csv, pdb, glob +import math +from pathlib import Path + + +def main(args): + *_, func_args = inspect.getargvalues(inspect.currentframe()) + func_args = dict(func_args) + + time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + savedir = f"samples/{Path(args.config).stem}-{time_str}" + os.makedirs(savedir) + + config = OmegaConf.load(args.config) + samples = [] + + sample_idx = 0 + for model_idx, (config_key, model_config) in enumerate(list(config.items())): + + motion_modules = model_config.motion_module + motion_modules = [motion_modules] if isinstance(motion_modules, str) else list(motion_modules) + for motion_module in motion_modules: + inference_config = OmegaConf.load(model_config.get("inference_config", args.inference_config)) + + ### >>> create validation pipeline >>> ### + tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer") + text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder") + vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae") + unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)) + + if is_xformers_available(): unet.enable_xformers_memory_efficient_attention() + else: assert False + + pipeline = AnimationPipeline( + vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, + scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)), + ).to("cuda") + + pipeline = load_weights( + pipeline, + # motion module + motion_module_path = motion_module, + motion_module_lora_configs = model_config.get("motion_module_lora_configs", []), + # image layers + dreambooth_model_path = model_config.get("dreambooth_path", ""), + lora_model_path = model_config.get("lora_model_path", ""), + lora_alpha = model_config.get("lora_alpha", 0.8), + ).to("cuda") + + prompts = model_config.prompt + n_prompts = list(model_config.n_prompt) * len(prompts) if len(model_config.n_prompt) == 1 else model_config.n_prompt + + random_seeds = model_config.get("seed", [-1]) + random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds) + random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds + + config[config_key].random_seed = [] + for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)): + + # manually set random seed for reproduction + if random_seed != -1: torch.manual_seed(random_seed) + else: torch.seed() + config[config_key].random_seed.append(torch.initial_seed()) + + print(f"current seed: {torch.initial_seed()}") + print(f"sampling {prompt} ...") + sample = pipeline( + prompt, + negative_prompt = n_prompt, + num_inference_steps = model_config.steps, + guidance_scale = model_config.guidance_scale, + width = args.W, + height = args.H, + video_length = args.L, + ).videos + samples.append(sample) + + prompt = "-".join((prompt.replace("/", "").split(" ")[:10])) + save_videos_grid(sample, f"{savedir}/sample/{sample_idx}-{prompt}.gif") + print(f"save to {savedir}/sample/{prompt}.gif") + + sample_idx += 1 + + samples = torch.concat(samples) + save_videos_grid(samples, f"{savedir}/sample.gif", n_rows=4) + + OmegaConf.save(config, f"{savedir}/config.yaml") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--pretrained_model_path", type=str, default="models/StableDiffusion/stable-diffusion-v1-5",) + parser.add_argument("--inference_config", type=str, default="configs/inference/inference-v1.yaml") + parser.add_argument("--config", type=str, required=True) + + parser.add_argument("--L", type=int, default=16 ) + parser.add_argument("--W", type=int, default=512) + parser.add_argument("--H", type=int, default=512) + + args = parser.parse_args() + main(args) diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd2aeae9828bf23a257a5e2b218e0d037f32d8b --- /dev/null +++ b/train.py @@ -0,0 +1,457 @@ +import argparse +import datetime +import logging +import inspect +import math +import os +from typing import Dict, Optional, Tuple +from omegaconf import OmegaConf +from collections import OrderedDict + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint + +import diffusers +import transformers +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import set_seed +from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler +from diffusers.optimization import get_scheduler +from diffusers.utils import check_min_version +from diffusers.utils.import_utils import is_xformers_available +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer + +from animatediff.models.unet import UNet3DConditionModel +from tuneavideo.data.frames_dataset import FramesDataset +from animatediff.data.dataset import ImgSeqDataset +from tuneavideo.data.multi_dataset import MultiTuneAVideoDataset +from animatediff.pipelines.pipeline_animation import AnimationPipeline +from tuneavideo.util import save_videos_grid, ddim_inversion +from einops import rearrange, repeat + + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +check_min_version("0.10.0.dev0") + +logger = get_logger(__name__, log_level="INFO") + + +def main( + pretrained_model_path: str, + output_dir: str, + train_data: Dict, + validation_data: Dict, + validation_steps: int = 100, + train_whole_module: bool = False, + trainable_modules: Tuple[str] = ( + "to_q", + ), + train_batch_size: int = 1, + max_train_steps: int = 500, + learning_rate: float = 3e-5, + scale_lr: bool = False, + lr_scheduler: str = "constant", + lr_warmup_steps: int = 0, + adam_beta1: float = 0.9, + adam_beta2: float = 0.999, + adam_weight_decay: float = 1e-2, + adam_epsilon: float = 1e-08, + max_grad_norm: float = 1.0, + gradient_accumulation_steps: int = 1, + gradient_checkpointing: bool = True, + checkpointing_steps: int = 500, + start_global_step: int = 0, + resume_from_checkpoint: Optional[str] = None, + mixed_precision: Optional[str] = "fp16", + use_8bit_adam: bool = False, + enable_xformers_memory_efficient_attention: bool = True, + seed: Optional[int] = None, + + motion_module: str = "models/Motion_Module/mm_sd_v15.ckpt", + inference_config_path: str = "configs/inference/inference-v3.yaml", + motion_module_pe_multiplier: int = 1, + dataset_class: str = 'MultiTuneAVideoDataset', + + # extra args + + image_finetune: bool = False, + + name: str = "scenefusion", + use_wandb: bool = True, + launcher: str = "launcher", + + cfg_random_null_text: bool = True, + cfg_random_null_text_ratio: float = 0.1, + + unet_checkpoint_path: str = "", + unet_additional_kwargs: Dict = {}, + ema_decay: float = 0.9999, + noise_scheduler_kwargs = None, + + max_train_epoch: int = -1, + validation_steps_tuple: Tuple = (-1,), + + num_workers: int = 32, + checkpointing_epochs: int = 5, + + mixed_precision_training: bool = True, + + global_seed: int = 42, + is_debug: bool = False, +): + *_, config = inspect.getargvalues(inspect.currentframe()) + + inference_config = OmegaConf.load(inference_config_path) + + accelerator = Accelerator( + gradient_accumulation_steps=gradient_accumulation_steps, + mixed_precision=mixed_precision, + ) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if seed is not None: + set_seed(seed) + + # Handle the output folder creation + if accelerator.is_main_process: + # now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + # output_dir = os.path.join(output_dir, now) + os.makedirs(output_dir, exist_ok=True) + os.makedirs(f"{output_dir}/samples", exist_ok=True) + os.makedirs(f"{output_dir}/inv_latents", exist_ok=True) + OmegaConf.save(config, os.path.join(output_dir, 'config.yaml')) + + # Load scheduler, tokenizer and models. + noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler") + tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") + text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder") + vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae") + unet = UNet3DConditionModel.from_pretrained_2d( + pretrained_model_path, subfolder="unet", + unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs) + ) + + # unet_path = "unet" + # unet = UNet3DConditionModel.from_pretrained_2d( + # unet_path, subfolder="unet", + # unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs) + # ) + + + motion_module_state_dict = torch.load(motion_module, map_location="cpu") + + # Multiply pe weights by multiplier for training more than 24 frames + if motion_module_pe_multiplier > 1: + for key in motion_module_state_dict: + if 'pe' in key: + t = motion_module_state_dict[key] + t = repeat(t, "b f d -> b (f m) d", m=motion_module_pe_multiplier) + motion_module_state_dict[key] = t + + if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]}) + missing, unexpected = unet.load_state_dict(motion_module_state_dict, strict=False) + assert len(unexpected) == 0 + + # Freeze vae and text_encoder + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + + unet.requires_grad_(False) + for name, module in unet.named_modules(): + if "motion_modules" in name and (train_whole_module or name.endswith(tuple(trainable_modules))): + for params in module.parameters(): + params.requires_grad = True + + if enable_xformers_memory_efficient_attention: + if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + else: + raise ValueError("xformers is not available. Make sure it is installed correctly") + + if gradient_checkpointing: + unet.enable_gradient_checkpointing() + + if scale_lr: + learning_rate = ( + learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes + ) + + + print("optimizer values", learning_rate, adam_beta1, adam_beta2, adam_weight_decay, adam_epsilon) + # Initialize the optimizer + if use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" + ) + + optimizer_cls = bnb.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + optimizer = optimizer_cls( + unet.parameters(), + lr=learning_rate, + betas=(adam_beta1, adam_beta2), + weight_decay=adam_weight_decay, + eps=adam_epsilon, + ) + + # Get the training dataset + train_dataset = None + if dataset_class == 'MultiTuneAVideoDataset': + train_dataset = ImgSeqDataset(**train_data) + + # Preprocessing the dataset + train_dataset.prompt_ids = [None] * len(train_dataset.prompt) + for index, prompt in enumerate(train_dataset.prompt): + train_dataset.prompt_ids[index] = tokenizer( + prompt,max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" + ).input_ids[0] + else: + train_dataset = FramesDataset(tokenizer=tokenizer, **train_data) + train_dataset.load() + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, batch_size=train_batch_size + ) + + # Get the validation pipeline + validation_pipeline = AnimationPipeline( + vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, + scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs['DDIMScheduler'])), + ) + validation_pipeline.enable_vae_slicing() + ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler') + ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps) + + # Scheduler + lr_scheduler = get_scheduler( + lr_scheduler, + optimizer=optimizer, + num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps, + num_training_steps=max_train_steps * gradient_accumulation_steps, + ) + + # Prepare everything with our `accelerator`. + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + + # For mixed precision training we cast the text_encoder and vae weights to half-precision + # as these models are only used for inference, keeping weights in full precision is not required. + weight_dtype = torch.float32 + if accelerator.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + # Move text_encode and vae to gpu and cast to weight_dtype + text_encoder.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=weight_dtype) + + print("DATA LEN:", len(train_dataloader)) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps) + # Afterwards we recalculate our number of training epochs + num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + accelerator.init_trackers("text2video-fine-tune") + + # Train! + total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {max_train_steps}") + global_step = 0 + first_epoch = 0 + + if start_global_step > 0: + global_step = start_global_step + first_epoch = global_step // num_update_steps_per_epoch + resume_step = global_step % num_update_steps_per_epoch + + # Potentially load in the weights and states from a previous save + if resume_from_checkpoint: + if resume_from_checkpoint != "latest": + path = os.path.basename(resume_from_checkpoint) + else: + # Get the most recent checkpoint + dirs = os.listdir(output_dir) + dirs = [d for d in dirs if d.startswith("checkpoint")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] + accelerator.print(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(output_dir, path)) + global_step = int(path.split("-")[1]) + + first_epoch = global_step // num_update_steps_per_epoch + resume_step = global_step % num_update_steps_per_epoch + + # Only show the progress bar once on each machine. + progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process) + progress_bar.set_description("Steps") + + for epoch in range(first_epoch, num_train_epochs): + unet.train() + train_loss = 0.0 + for step, batch in enumerate(train_dataloader): + # Skip steps until we reach the resumed step + if resume_from_checkpoint and epoch == first_epoch and step < resume_step: + if step % gradient_accumulation_steps == 0: + progress_bar.update(1) + continue + + with accelerator.accumulate(unet): + # Convert videos to latent space + pixel_values = batch["pixel_values"].to(weight_dtype) + video_length = pixel_values.shape[1] + pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w") + latents = vae.encode(pixel_values).latent_dist.sample() + latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length) + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each video + timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(batch["prompt_ids"])[0] + + # Get the target for loss depending on the prediction type + if noise_scheduler.prediction_type == "epsilon": + target = noise + elif noise_scheduler.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}") + + # Predict the noise residual and compute loss + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + # Gather the losses across all processes for logging (if we use distributed training). + avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean() + train_loss += avg_loss.item() / gradient_accumulation_steps + + # Backpropagate + accelerator.backward(loss) + if accelerator.sync_gradients: + accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if global_step % checkpointing_steps == 0: + if accelerator.is_main_process: + save_path = os.path.join(output_dir, f"mm-{global_step}.pth") + save_checkpoint(unet, save_path) + logger.info(f"Saved state to {save_path}") + + if global_step % validation_steps == 0: + if accelerator.is_main_process: + samples = [] + generator = torch.Generator(device=latents.device) + generator.manual_seed(seed) + + ddim_inv_latent = None + if validation_data.use_inv_latent: + inv_latents_path = os.path.join(output_dir, f"inv_latents/ddim_latent-{global_step}.pt") + ddim_inv_latent = ddim_inversion( + validation_pipeline, ddim_inv_scheduler, video_latent=latents, + num_inv_steps=validation_data.num_inv_steps, prompt="")[-1].to(weight_dtype) + torch.save(ddim_inv_latent, inv_latents_path) + + for idx, prompt in enumerate(set(validation_data.prompts)): + sample = validation_pipeline(prompt, generator=generator, + latents=ddim_inv_latent, + fp16=True, + **validation_data).videos + save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{idx}.gif", fps=1) + samples.append(sample) + samples = torch.concat(samples) + save_path = f"{output_dir}/samples/sample-{global_step}.gif" + save_videos_grid(samples, save_path, fps=1) + logger.info(f"Saved samples to {save_path}") + + logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= max_train_steps: + break + + # Create the pipeline using the trained modules and save it. + accelerator.wait_for_everyone() + if accelerator.is_main_process: + unet = accelerator.unwrap_model(unet) + pipeline = AnimationPipeline.from_pretrained( + pretrained_model_path, + text_encoder=text_encoder, + vae=vae, + unet=unet, + ) + + mm_path = "%s/mm.pth" % output_dir + save_checkpoint(unet, mm_path) + + accelerator.end_training() + +def save_checkpoint(unet, mm_path): + mm_state_dict = OrderedDict() + state_dict = unet.state_dict() + for key in state_dict: + if "motion_module" in key: + mm_state_dict[key] = state_dict[key] + + torch.save(mm_state_dict, mm_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml") + args = parser.parse_args() + + main(**OmegaConf.load(args.config)) diff --git a/triton-2.0.0-cp310-cp310-win_amd64.whl b/triton-2.0.0-cp310-cp310-win_amd64.whl new file mode 100644 index 0000000000000000000000000000000000000000..e92974761fd4ade443e89067796b3780d7d0463b --- /dev/null +++ b/triton-2.0.0-cp310-cp310-win_amd64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91a6ec395022743269c942df7af01b210f642fb633d146a811be05a455adbae2 +size 12643861 diff --git a/tuneavideo/__pycache__/util.cpython-310.pyc b/tuneavideo/__pycache__/util.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ce5accd73fb59f177236d49722f1f1dd43797f6 Binary files /dev/null and b/tuneavideo/__pycache__/util.cpython-310.pyc differ diff --git a/tuneavideo/data/__pycache__/frames_dataset.cpython-310.pyc b/tuneavideo/data/__pycache__/frames_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..78137bdc8791ca553585999d8c0abe5420e29cde Binary files /dev/null and b/tuneavideo/data/__pycache__/frames_dataset.cpython-310.pyc differ diff --git a/tuneavideo/data/__pycache__/multi_dataset.cpython-310.pyc b/tuneavideo/data/__pycache__/multi_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3b4a6b4a0437f708f3b5580f666fea95747ec0ee Binary files /dev/null and b/tuneavideo/data/__pycache__/multi_dataset.cpython-310.pyc differ diff --git a/tuneavideo/data/dataset.py b/tuneavideo/data/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7c714aabd96bbe38497749096dcc936bb8e81b5e --- /dev/null +++ b/tuneavideo/data/dataset.py @@ -0,0 +1,44 @@ +import decord +decord.bridge.set_bridge('torch') + +from torch.utils.data import Dataset +from einops import rearrange + + +class TuneAVideoDataset(Dataset): + def __init__( + self, + video_path: str, + prompt: str, + width: int = 512, + height: int = 512, + n_sample_frames: int = 8, + sample_start_idx: int = 0, + sample_frame_rate: int = 1, + ): + self.video_path = video_path + self.prompt = prompt + self.prompt_ids = None + + self.width = width + self.height = height + self.n_sample_frames = n_sample_frames + self.sample_start_idx = sample_start_idx + self.sample_frame_rate = sample_frame_rate + + def __len__(self): + return 1 + + def __getitem__(self, index): + # load and sample video frames + vr = decord.VideoReader(self.video_path, width=self.width, height=self.height) + sample_index = list(range(self.sample_start_idx, len(vr), self.sample_frame_rate))[:self.n_sample_frames] + video = vr.get_batch(sample_index) + video = rearrange(video, "f h w c -> f c h w") + + example = { + "pixel_values": (video / 127.5 - 1.0), + "prompt_ids": self.prompt_ids + } + + return example diff --git a/tuneavideo/data/frames_dataset.py b/tuneavideo/data/frames_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8dd654befbe65b5615da93783f2f433cc63a2a2b --- /dev/null +++ b/tuneavideo/data/frames_dataset.py @@ -0,0 +1,244 @@ +from typing import Callable, List, Optional, Union +from torch.utils.data import Dataset + +import decord +decord.bridge.set_bridge('torch') + +from einops import rearrange + +import random +import os +import json +from PIL import Image, ImageFilter +import numpy as np +import cv2 +from scipy import ndimage +import tempfile +import ffmpeg + +from transformers import CLIPTokenizer + + +class FramesDataset(Dataset): + def __init__( + self, + samples_dir: str, + prompt_map_path: Union[str, list[str]], + width: int = 512, + height: int = 512, + video_length: int = 16, + sample_start_index: int = 0, + sample_count: int = 1, + sample_frame_rate: int = 8, + variance_threshold: int = 50, + tokenizer: CLIPTokenizer = None, + ): + + print("FramesDataset", "init", width, height, video_length, sample_count) + + self.width = width + self.height = height + self.video_length = video_length + self.sample_count = sample_count + self.tokenizer = tokenizer + self.samples_dir = samples_dir + self.sample_start_index = sample_start_index + self.sample_frame_rate = sample_frame_rate + self.variance_threshold = variance_threshold + + self.samples = [] + + self.prompt_map = None + + with open(prompt_map_path, 'r') as f: + self.prompt_map = json.loads(f.read()) + + self.frames_path = [str(k) for k in self.prompt_map.keys()] + + print("FramesDataset", "init", "frames_path", len(self.frames_path)) + + def load(self): + print("FramesDataset", "load", "samples_dir", self.samples_dir) + + def extract_integer(filename): + return int(filename.split('.')[0]) + + self.samples = [] + files = sorted(os.listdir(self.samples_dir), key=extract_integer) + for filename in files: + if 'json' in filename: + with open(f"{self.samples_dir}/{filename}", 'r') as f: + sample = json.loads(f.read()) + sample['prompt_ids'] = self.tokenize(sample['prompt']) + self.samples.append(sample) + print("FramesDataset", "load", "samples", len(self.samples)) + + def tokenize(self, prompt): + input_ids = self.tokenizer( + prompt, + max_length=self.tokenizer.model_max_length, + padding="max_length", + truncation=True, + return_tensors="pt" + ).input_ids[0] + + return input_ids + + def prepare(self): + print("FramesDataset", "prepare") + + candidates = [] + for dir_path in self.frames_path: + candidates = candidates + self.load_key_frames(dir_path) + + print("FramesDataset", "prepare", "candidates", len(candidates)) + + self.pick(self.sample_count, candidates) + + def pick(self, count, candidates): + print("FramesDataset", "pick", count, len(candidates)) + + sample_index = self.sample_start_index + while True: + key_frame = random.choice(candidates) + print("FramesDataset", "pick", "key_frame", key_frame) + + dir_name = os.path.dirname(key_frame) + file_name = os.path.basename(key_frame) + frame_number = int(file_name.split(".")[0]) + + sample = [] + for i in range(frame_number, frame_number + self.video_length): + frame_path = f"{dir_name}/{i}.png" + frame = Image.open(frame_path) + frame = frame.resize((self.width, self.height)) + sample.append(np.array(frame)) + + sample = np.array(sample) + + print("FramesDataset", "pick", "reading sample", sample.shape) + + if not self.check(sample): + print("FramesDataset", "pick", "skip") + continue + + print("FramesDataset", "pick", "checked") + + prompt = self.get_prompt(key_frame) + + sample_file = f"{self.samples_dir}/{sample_index}.mp4" + self.write_video(sample, sample_file, self.sample_frame_rate) + print("FramesDataset", "pick", "sample_file", sample_file) + + meta_file = f"{self.samples_dir}/{sample_index}.json" + with open(meta_file, 'w') as f: + f.write(json.dumps({ + 'key_frame': key_frame, + 'video_file': sample_file, + 'prompt': prompt, + })) + print("FramesDataset", "pick", "meta_file", meta_file) + + sample_index = sample_index + 1 + if sample_index == self.sample_start_index + self.sample_count: + print("FramesDataset", "pick", "done") + break + + def write_video(self, frames, video_file, video_fps): + with tempfile.TemporaryDirectory() as frames_dir: + for index, frame in enumerate(frames): + Image.fromarray(frame).save(f"{frames_dir}/{index}.png") + + (ffmpeg + .input(f"{frames_dir}/%d.png") + .output(video_file, vcodec='libx264', vf=f"fps={video_fps}") + .overwrite_output() + .run()) + + def get_prompt(self, key_frame): + print("FramesDataset", "get_prompt", key_frame) + + dir_name = os.path.dirname(key_frame) + file_name = os.path.basename(key_frame) + number = int(file_name.split(".")[0]) + prompt = "" + if dir_name in self.prompt_map: + prompt_map = self.prompt_map[dir_name] + for k in prompt_map: + if number >= int(k): + print("FramesDataset", "get_prompt", k, prompt_map[k]) + return prompt_map[k] + + print("FramesDataset", "get_prompt", "not found") + return prompt + + def check(self, sample): + diffs = [] + for i in range(0, len(sample)-1): + diffs.append(np.sum(self.blur(sample[i]) - self.blur(sample[i-1]))) + + first_diff = diffs[0] + variance = np.var(diffs)**(1/2)/first_diff * 100 + threshold = self.variance_threshold + + return variance < threshold + + def blur(self, frame): + image = Image.fromarray(frame) + image = image.filter(ImageFilter.GaussianBlur(radius=5)) + return np.array(image) + + def load_key_frames(self, dir_path): + print("FramesDataset", "load_key_frames", dir_path) + + if not os.path.isdir(dir_path): + raise Exception("Dir not exist") + + def extract_integer(filename): + return int(filename.split('.')[0]) + + candidates = [] + + files = sorted(os.listdir(dir_path), key=extract_integer) + print("FramesDataset", "load_key_frames", "files", len(files)) + count = len(files) + for index, file_name in enumerate(files): + file_path = f"{dir_path}/{file_name}" + + if 'png' in file_name and index + self.video_length <= count: + candidates.append(file_path) + + print("FramesDataset", "load_key_frames", "candidates", len(candidates)) + return candidates + + def __len__(self): + return len(self.samples) + + def __getitem__(self, index): + meta = self.samples[index] + vr = decord.VideoReader(meta['video_file']) + sample_index = list(range(0, len(vr)))[:self.video_length] + video = vr.get_batch(sample_index) + video = rearrange(video, "f h w c -> f c h w") + meta['pixel_values'] = (video / 127.5 - 1.0) + return meta + +if __name__ == "__main__": + + tokenizer = CLIPTokenizer.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder="tokenizer") + + dataset = FramesDataset( + samples_dir = "test/FramesDataset/samples_dir", + prompt_map_path = 'test/FramesDataset/prompt_map.json', + width = 512, + height = 512, + video_length = 16, + sample_count = 1, + tokenizer = tokenizer, + variance_threshold = 40, + ) + + dataset.prepare() + #dataset.load() + #print(len(dataset), dataset[0]['key_frame'], dataset[0]['prompt']) + diff --git a/tuneavideo/data/multi_dataset.py b/tuneavideo/data/multi_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..4b08bd59fa27fecf5c7334b3b09740014a1ee3be --- /dev/null +++ b/tuneavideo/data/multi_dataset.py @@ -0,0 +1,45 @@ +import decord +decord.bridge.set_bridge('torch') + +from typing import Callable, List, Optional, Union +from torch.utils.data import Dataset +from einops import rearrange + + +class MultiTuneAVideoDataset(Dataset): + def __init__( + self, + video_path: Union[str, list[str]], + prompt: Union[str, list[str]], + width: int = 512, + height: int = 512, + n_sample_frames: int = 8, + sample_start_idx: int = 0, + sample_frame_rate: int = 1, + ): + self.video_path = [video_path] if isinstance(video_path, str) else video_path + self.prompt = [prompt] * len(self.video_path) if isinstance(prompt, str) else prompt + self.prompt_ids = [] + + self.width = width + self.height = height + self.n_sample_frames = n_sample_frames + self.sample_start_idx = sample_start_idx + self.sample_frame_rate = sample_frame_rate + + def __len__(self): + return len(self.video_path) + + def __getitem__(self, index): + # load and sample video frames + vr = decord.VideoReader(self.video_path[index], width=self.width, height=self.height) + sample_index = list(range(self.sample_start_idx, len(vr), self.sample_frame_rate))[:self.n_sample_frames] + video = vr.get_batch(sample_index) + video = rearrange(video, "f h w c -> f c h w") + + example = { + "pixel_values": (video / 127.5 - 1.0), + "prompt_ids": self.prompt_ids[index] + } + + return example diff --git a/tuneavideo/models/attention.py b/tuneavideo/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..dd248734079419ddc7a983ec07b0b60674d83826 --- /dev/null +++ b/tuneavideo/models/attention.py @@ -0,0 +1,328 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py + +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.modeling_utils import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm + +from einops import rearrange, repeat + + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +class Transformer3DModel(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): + # Input + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length) + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length + ) + + # Output + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + ) + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + ) + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + + # SC-Attn + self.attn1 = SparseCausalAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + # Cross-Attn + if cross_attention_dim is not None: + self.attn2 = CrossAttention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + else: + self.attn2 = None + + if cross_attention_dim is not None: + self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + else: + self.norm2 = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + + # Temp-Attn + self.attn_temp = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): + if not is_xformers_available(): + print("Here is how to install it") + raise ModuleNotFoundError( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers", + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" + " available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + if self.attn2 is not None: + self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None): + # SparseCausal-Attention + norm_hidden_states = ( + self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) + ) + + if self.only_cross_attention: + hidden_states = ( + self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states + ) + else: + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states + + if self.attn2 is not None: + # Cross-Attention + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states) + ) + hidden_states = self.attn_temp(norm_hidden_states) + hidden_states + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states + + +class SparseCausalAttention(CrossAttention): + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + batch_size, sequence_length, _ = hidden_states.shape + + encoder_hidden_states = encoder_hidden_states + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + dim = query.shape[-1] + query = self.reshape_heads_to_batch_dim(query) + + if self.added_kv_proj_dim is not None: + raise NotImplementedError + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + former_frame_index = torch.arange(video_length) - 1 + former_frame_index[0] = 0 + + key = rearrange(key, "(b f) d c -> b f d c", f=video_length) + key = torch.cat([key[:, [0] * video_length], key[:, former_frame_index]], dim=2) + key = rearrange(key, "b f d c -> (b f) d c") + + value = rearrange(value, "(b f) d c -> b f d c", f=video_length) + value = torch.cat([value[:, [0] * video_length], value[:, former_frame_index]], dim=2) + value = rearrange(value, "b f d c -> (b f) d c") + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # attention, what we cannot get enough of + if self._use_memory_efficient_attention_xformers: + hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # Some versions of xformers return output in fp32, cast it back to the dtype of the input + hidden_states = hidden_states.to(query.dtype) + else: + if self._slice_size is None or query.shape[0] // self._slice_size == 1: + hidden_states = self._attention(query, key, value, attention_mask) + else: + hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + return hidden_states diff --git a/tuneavideo/models/resnet.py b/tuneavideo/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..32cc00b49fef084880b78eb2b58cfc7ce9a4f662 --- /dev/null +++ b/tuneavideo/models/resnet.py @@ -0,0 +1,209 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + if name == "conv": + self.conv = conv + else: + self.Conv2d_0 = conv + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + if self.use_conv: + if self.name == "conv": + hidden_states = self.conv(hidden_states) + else: + hidden_states = self.Conv2d_0(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + raise NotImplementedError + + if name == "conv": + self.Conv2d_0 = conv + self.conv = conv + elif name == "Conv2d_0": + self.conv = conv + else: + self.conv = conv + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + + self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + time_emb_proj_out_channels = out_channels + elif self.time_embedding_norm == "scale_shift": + time_emb_proj_out_channels = out_channels * 2 + else: + raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") + + self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) + else: + self.time_emb_proj = None + + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) \ No newline at end of file diff --git a/tuneavideo/models/unet.py b/tuneavideo/models/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..6155eeb5674e54f1485e84a64ddbe7564438af59 --- /dev/null +++ b/tuneavideo/models/unet.py @@ -0,0 +1,450 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py + +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import os +import json + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.modeling_utils import ModelMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from .unet_blocks import ( + CrossAttnDownBlock3D, + CrossAttnUpBlock3D, + DownBlock3D, + UNetMidBlock3DCrossAttn, + UpBlock3D, + get_down_block, + get_up_block, +) +from .resnet import InflatedConv3d + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D" + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + down_block_res_samples += res_samples + + # mid + sample = self.mid_block( + sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d(cls, pretrained_model_path, subfolder=None): + if subfolder is not None: + pretrained_model_path = os.path.join(pretrained_model_path, subfolder) + + config_file = os.path.join(pretrained_model_path, 'config.json') + if not os.path.isfile(config_file): + raise RuntimeError(f"{config_file} does not exist") + with open(config_file, "r") as f: + config = json.load(f) + config["_class_name"] = cls.__name__ + config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D" + ] + config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D" + ] + + from diffusers.utils import WEIGHTS_NAME + model = cls.from_config(config) + model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME) + if not os.path.isfile(model_file): + raise RuntimeError(f"{model_file} does not exist") + state_dict = torch.load(model_file, map_location="cpu") + for k, v in model.state_dict().items(): + if '_temp.' in k: + state_dict.update({k: v}) + model.load_state_dict(state_dict) + + return model \ No newline at end of file diff --git a/tuneavideo/models/unet_blocks.py b/tuneavideo/models/unet_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..e48e9320490c7fdccd2f3cb1d28e7f694609beea --- /dev/null +++ b/tuneavideo/models/unet_blocks.py @@ -0,0 +1,588 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py + +import torch +from torch import nn + +from .attention import Transformer3DModel +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", +): + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", +): + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + output_states = () + + for resnet, attn in zip(self.resnets, self.attentions): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None): + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + else: + hidden_states = resnet(hidden_states, temb) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + attention_mask=None, + ): + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + else: + hidden_states = resnet(hidden_states, temb) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/tuneavideo/pipelines/pipeline_tuneavideo.py b/tuneavideo/pipelines/pipeline_tuneavideo.py new file mode 100644 index 0000000000000000000000000000000000000000..87c26785a509f1dcbae3408dacecbe7d23530cf0 --- /dev/null +++ b/tuneavideo/pipelines/pipeline_tuneavideo.py @@ -0,0 +1,407 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py + +import inspect +from typing import Callable, List, Optional, Union +from dataclasses import dataclass + +import numpy as np +import torch + +from diffusers.utils import is_accelerate_available +from packaging import version +from transformers import CLIPTextModel, CLIPTokenizer + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import deprecate, logging, BaseOutput + +from einops import rearrange + +from ..models.unet import UNet3DConditionModel + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class TuneAVideoPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class TuneAVideoPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet3DConditionModel, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + text_embeddings = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + text_embeddings = text_embeddings[0] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) + text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + uncond_embeddings = uncond_embeddings[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + video = self.vae.decode(latents).sample + video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) + video = (video / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + video = video.cpu().float().numpy() + return video + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, prompt, height, width, callback_steps): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + rand_device = "cpu" if device.type == "mps" else device + + if isinstance(generator, list): + shape = (1,) + shape[1:] + latents = [ + torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) + for i in range(batch_size) + ] + latents = torch.cat(latents, dim=0).to(device) + else: + latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) + else: + if latents.shape != shape: + raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]], + video_length: Optional[int], + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_videos_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + **kwargs, + ): + # Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # Check inputs. Raise error if not correct + self.check_inputs(prompt, height, width, callback_steps) + + # Define call parameters + batch_size = 1 if isinstance(prompt, str) else len(prompt) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # Encode input prompt + text_embeddings = self._encode_prompt( + prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt + ) + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # Prepare latent variables + num_channels_latents = self.unet.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_channels_latents, + video_length, + height, + width, + text_embeddings.dtype, + device, + generator, + latents, + ) + latents_dtype = latents.dtype + + # Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + # Post-processing + video = self.decode_latents(latents) + + # Convert to tensor + if output_type == "tensor": + video = torch.from_numpy(video) + + if not return_dict: + return video + + return TuneAVideoPipelineOutput(videos=video) \ No newline at end of file diff --git a/tuneavideo/util.py b/tuneavideo/util.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7519002c955874c8d56f538db5567e16a91dbb --- /dev/null +++ b/tuneavideo/util.py @@ -0,0 +1,84 @@ +import os +import imageio +import numpy as np +from typing import Union + +import torch +import torchvision + +from tqdm import tqdm +from einops import rearrange + + +def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=1): + videos = rearrange(videos, "b c t h w -> t b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.mimsave(path, outputs, duration=len(outputs)/fps, loop=0) + + +# DDIM Inversion +@torch.no_grad() +def init_prompt(prompt, pipeline): + uncond_input = pipeline.tokenizer( + [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, + return_tensors="pt" + ) + uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0] + text_input = pipeline.tokenizer( + [prompt], + padding="max_length", + max_length=pipeline.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] + context = torch.cat([uncond_embeddings, text_embeddings]) + + return context + + +def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, + sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler): + timestep, next_timestep = min( + timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep + alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod + alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep] + beta_prod_t = 1 - alpha_prod_t + next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 + next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output + next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction + return next_sample + + +def get_noise_pred_single(latents, t, context, unet): + noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"] + return noise_pred + + +@torch.no_grad() +def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt): + context = init_prompt(prompt, pipeline) + uncond_embeddings, cond_embeddings = context.chunk(2) + all_latent = [latent] + latent = latent.clone().detach() + for i in tqdm(range(num_inv_steps)): + t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1] + noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet) + latent = next_step(noise_pred, t, latent, ddim_scheduler) + all_latent.append(latent) + return all_latent + + +@torch.no_grad() +def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""): + ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt) + return ddim_latents