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| title: Pyramid Flow | |
| sdk: gradio | |
| emoji: ⚱️ | |
| sdk_version: 5.0.1 | |
| suggested_hardware: l40sx1 | |
| <div align="center"> | |
| # ⚡️Pyramid Flow⚡️ | |
| [[Paper]](https://arxiv.org/abs/2410.05954) [[Project Page ✨]](https://pyramid-flow.github.io) [[Model 🤗]](https://huggingface.co/rain1011/pyramid-flow-sd3) | |
| </div> | |
| This is the official repository for Pyramid Flow, a training-efficient **Autoregressive Video Generation** method based on **Flow Matching**. By training only on **open-source datasets**, it can generate high-quality 10-second videos at 768p resolution and 24 FPS, and naturally supports image-to-video generation. | |
| <table class="center" border="0" style="width: 100%; text-align: left;"> | |
| <tr> | |
| <th>10s, 768p, 24fps</th> | |
| <th>5s, 768p, 24fps</th> | |
| <th>Image-to-video</th> | |
| </tr> | |
| <tr> | |
| <td><video src="https://github.com/user-attachments/assets/9935da83-ae56-4672-8747-0f46e90f7b2b" autoplay muted loop playsinline></video></td> | |
| <td><video src="https://github.com/user-attachments/assets/3412848b-64db-4d9e-8dbf-11403f6d02c5" autoplay muted loop playsinline></video></td> | |
| <td><video src="https://github.com/user-attachments/assets/3bd7251f-7b2c-4bee-951d-656fdb45f427" autoplay muted loop playsinline></video></td> | |
| </tr> | |
| </table> | |
| ## News | |
| * `COMING SOON` ⚡️⚡️⚡️ Training code for both the Video VAE and DiT; New model checkpoints trained from scratch. | |
| > We are training Pyramid Flow from scratch to fix human structure issues related to the currently adopted SD3 initialization and hope to release it in the next few days. | |
| * `2024.10.10` 🚀🚀🚀 We release the [technical report](https://arxiv.org/abs/2410.05954), [project page](https://pyramid-flow.github.io) and [model checkpoint](https://huggingface.co/rain1011/pyramid-flow-sd3) of Pyramid Flow. | |
| ## Introduction | |
|  | |
| Existing video diffusion models operate at full resolution, spending a lot of computation on very noisy latents. By contrast, our method harnesses the flexibility of flow matching ([Lipman et al., 2023](https://openreview.net/forum?id=PqvMRDCJT9t); [Liu et al., 2023](https://openreview.net/forum?id=XVjTT1nw5z); [Albergo & Vanden-Eijnden, 2023](https://openreview.net/forum?id=li7qeBbCR1t)) to interpolate between latents of different resolutions and noise levels, allowing for simultaneous generation and decompression of visual content with better computational efficiency. The entire framework is end-to-end optimized with a single DiT ([Peebles & Xie, 2023](http://openaccess.thecvf.com/content/ICCV2023/html/Peebles_Scalable_Diffusion_Models_with_Transformers_ICCV_2023_paper.html)), generating high-quality 10-second videos at 768p resolution and 24 FPS within 20.7k A100 GPU training hours. | |
| ## Usage | |
| You can directly download the model from [Huggingface](https://huggingface.co/rain1011/pyramid-flow-sd3). We provide both model checkpoints for 768p and 384p video generation. The 384p checkpoint supports 5-second video generation at 24FPS, while the 768p checkpoint supports up to 10-second video generation at 24FPS. | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| model_path = 'PATH' # The local directory to save downloaded checkpoint | |
| snapshot_download("rain1011/pyramid-flow-sd3", local_dir=model_path, local_dir_use_symlinks=False, repo_type='model') | |
| ``` | |
| To use our model, please follow the inference code in `video_generation_demo.ipynb` at [this link](https://github.com/jy0205/Pyramid-Flow/blob/main/video_generation_demo.ipynb). We further simplify it into the following two-step procedure. First, load the downloaded model: | |
| ```python | |
| import torch | |
| from PIL import Image | |
| from pyramid_dit import PyramidDiTForVideoGeneration | |
| from diffusers.utils import load_image, export_to_video | |
| torch.cuda.set_device(0) | |
| model_dtype, torch_dtype = 'bf16', torch.bfloat16 # Use bf16, fp16 or fp32 | |
| model = PyramidDiTForVideoGeneration( | |
| 'PATH', # The downloaded checkpoint dir | |
| model_dtype, | |
| model_variant='diffusion_transformer_768p', # 'diffusion_transformer_384p' | |
| ) | |
| model.vae.to("cuda") | |
| model.dit.to("cuda") | |
| model.text_encoder.to("cuda") | |
| model.vae.enable_tiling() | |
| ``` | |
| Then, you can try text-to-video generation on your own prompts: | |
| ```python | |
| prompt = "A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors" | |
| with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype): | |
| frames = model.generate( | |
| prompt=prompt, | |
| num_inference_steps=[20, 20, 20], | |
| video_num_inference_steps=[10, 10, 10], | |
| height=768, | |
| width=1280, | |
| temp=16, # temp=16: 5s, temp=31: 10s | |
| guidance_scale=9.0, # The guidance for the first frame | |
| video_guidance_scale=5.0, # The guidance for the other video latent | |
| output_type="pil", | |
| save_memory=True, # If you have enough GPU memory, set it to `False` to improve vae decoding speed | |
| ) | |
| export_to_video(frames, "./text_to_video_sample.mp4", fps=24) | |
| ``` | |
| As an autoregressive model, our model also supports (text conditioned) image-to-video generation: | |
| ```python | |
| image = Image.open('assets/the_great_wall.jpg').convert("RGB").resize((1280, 768)) | |
| prompt = "FPV flying over the Great Wall" | |
| with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype): | |
| frames = model.generate_i2v( | |
| prompt=prompt, | |
| input_image=image, | |
| num_inference_steps=[10, 10, 10], | |
| temp=16, | |
| video_guidance_scale=4.0, | |
| output_type="pil", | |
| save_memory=True, # If you have enough GPU memory, set it to `False` to improve vae decoding speed | |
| ) | |
| export_to_video(frames, "./image_to_video_sample.mp4", fps=24) | |
| ``` | |
| Usage tips: | |
| * The `guidance_scale` parameter controls the visual quality. We suggest using a guidance within [7, 9] for the 768p checkpoint during text-to-video generation, and 7 for the 384p checkpoint. | |
| * The `video_guidance_scale` parameter controls the motion. A larger value increases the dynamic degree and mitigates the autoregressive generation degradation, while a smaller value stabilizes the video. | |
| * For 10-second video generation, we recommend using a guidance scale of 7 and a video guidance scale of 5. | |
| ## Gallery | |
| The following video examples are generated at 5s, 768p, 24fps. For more results, please visit our [project page](https://pyramid-flow.github.io). | |
| <table class="center" border="0" style="width: 100%; text-align: left;"> | |
| <tr> | |
| <td><video src="https://github.com/user-attachments/assets/5b44a57e-fa08-4554-84a2-2c7a99f2b343" autoplay muted loop playsinline></video></td> | |
| <td><video src="https://github.com/user-attachments/assets/5afd5970-de72-40e2-900d-a20d18308e8e" autoplay muted loop playsinline></video></td> | |
| </tr> | |
| <tr> | |
| <td><video src="https://github.com/user-attachments/assets/1d44daf8-017f-40e9-bf18-1e19c0a8983b" autoplay muted loop playsinline></video></td> | |
| <td><video src="https://github.com/user-attachments/assets/7f5dd901-b7d7-48cc-b67a-3c5f9e1546d2" autoplay muted loop playsinline></video></td> | |
| </tr> | |
| </table> | |
| ## Comparison | |
| On VBench ([Huang et al., 2024](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard)), our method surpasses all the compared open-source baselines. Even with only public video data, it achieves comparable performance to commercial models like Kling ([Kuaishou, 2024](https://kling.kuaishou.com/en)) and Gen-3 Alpha ([Runway, 2024](https://runwayml.com/research/introducing-gen-3-alpha)), especially in the quality score (84.74 vs. 84.11 of Gen-3) and motion smoothness. | |
|  | |
| We conduct an additional user study with 20+ participants. As can be seen, our method is preferred over open-source models such as [Open-Sora](https://github.com/hpcaitech/Open-Sora) and [CogVideoX-2B](https://github.com/THUDM/CogVideo) especially in terms of motion smoothness. | |
|  | |
| ## Acknowledgement | |
| We are grateful for the following awesome projects when implementing Pyramid Flow: | |
| * [SD3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium) and [Flux 1.0](https://huggingface.co/black-forest-labs/FLUX.1-dev): State-of-the-art image generation models based on flow matching. | |
| * [Diffusion Forcing](https://boyuan.space/diffusion-forcing) and [GameNGen](https://gamengen.github.io): Next-token prediction meets full-sequence diffusion. | |
| * [WebVid-10M](https://github.com/m-bain/webvid), [OpenVid-1M](https://github.com/NJU-PCALab/OpenVid-1M) and [Open-Sora Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan): Large-scale datasets for text-to-video generation. | |
| * [CogVideoX](https://github.com/THUDM/CogVideo): An open-source text-to-video generation model that shares many training details. | |
| * [Video-LLaMA2](https://github.com/DAMO-NLP-SG/VideoLLaMA2): An open-source video LLM for our video recaptioning. | |
| ## Citation | |
| Consider giving this repository a star and cite Pyramid Flow in your publications if it helps your research. | |
| ``` | |
| @article{jin2024pyramidal, | |
| title={Pyramidal Flow Matching for Efficient Video Generative Modeling}, | |
| author={Jin, Yang and Sun, Zhicheng and Li, Ningyuan and Xu, Kun and Xu, Kun and Jiang, Hao and Zhuang, Nan and Huang, Quzhe and Song, Yang and Mu, Yadong and Lin, Zhouchen}, | |
| jounal={arXiv preprint arXiv:2410.05954}, | |
| year={2024} | |
| } | |
| ``` |