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README.md

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+---
+tags:
+- text-to-video
+duplicated_from: cerspense/zeroscope_v2_576w
+---
+
+# LLM-grounded Video Diffusion Models
+[Long Lian](https://tonylian.com/), [Baifeng Shi](https://bfshi.github.io/), [Adam Yala](https://www.adamyala.org/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Boyi Li](https://sites.google.com/site/boyilics/home) at UC Berkeley/UCSF. **ICLR 2024**.
+
+[Project Page](https://llm-grounded-video-diffusion.github.io/) | [Related Project: LMD](https://llm-grounded-diffusion.github.io/) | [Citation](https://llm-grounded-video-diffusion.github.io/#citation)
+
+This model is based on [zeroscope](https://huggingface.co/cerspense/zeroscope_v2_576w) but with additional conditioning from bounding boxes in a [GLIGEN](https://gligen.github.io/) fashion.
+
+Similar to [LLM-grounded Diffusion (LMD)](https://llm-grounded-diffusion.github.io/), LLM-grounded Video Diffusion (LVD)'s boxes-to-video stage allows cross-attention-based bounding box conditioning, which uses Zeroscope off-the-shelf. This huggingface model offers an alternative: we train a GLIGEN model (i.e., transformer adapters) with Zeroscope's weights without the temporal transformers blocks on [SA-1B](https://ai.meta.com/datasets/segment-anything/), treating it as a SD v2.1 model that has been fine-tuned to 256x256 resolution. We then merge the adapters into Zeroscope to offer conditioning. The resulting model is in this hugginface model. This can be used with cross-attention-based conditioning or on its own, similar to [LMD+](https://github.com/TonyLianLong/LLM-groundedDiffusion). This can be used with LLM-based text-to-dynamic scene layout generator in LVD, or on its own as a video version of GLIGEN.
+
+## Citation (LVD)
+If you use our work, model, or our implementation in this repo, or find them helpful, please consider giving a citation.
+```
+@article{lian2023llmgroundedvideo,
+      title={LLM-grounded Video Diffusion Models}, 
+      author={Lian, Long and Shi, Baifeng and Yala, Adam and Darrell, Trevor and Li, Boyi},
+      journal={arXiv preprint arXiv:2309.17444},
+      year={2023},
+}
+
+@article{lian2023llmgrounded,
+    title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models}, 
+    author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor},
+    journal={arXiv preprint arXiv:2305.13655},
+    year={2023}
+}
+```
+
+## Citation (GLIGEN)
+The adapters in this model are trained in a mannar similar to training GLIGEN adapters.
+```
+@article{li2023gligen,
+  title={GLIGEN: Open-Set Grounded Text-to-Image Generation},
+  author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},
+  journal={CVPR},
+  year={2023}
+}
+```
+
+## Citation (ModelScope)
+ModelScope is LVD's base model.
+
+```
+@article{wang2023modelscope,
+    title={Modelscope text-to-video technical report},
+    author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei},
+    journal={arXiv preprint arXiv:2308.06571},
+    year={2023}
+}
+@InProceedings{VideoFusion,
+    author    = {Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu},
+    title     = {VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2023}
+}
+```
+
+## LICENSE
+Zeroscope follows CC-BY-NC 4.0 license. The gligen adapters are trained on SA-1B, which follows [SA-1B license](https://ai.meta.com/datasets/segment-anything/).

README_old.md

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+---
+pipeline_tag: text-to-video
+license: cc-by-nc-4.0
+---
+
+![model example](https://i.imgur.com/1mrNnh8.png)
+
+# zeroscope_v2 576w
+A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained from the [original weights](https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis) using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br />
+zeroscope_v2_567w is specifically designed for upscaling with [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) using vid2vid in the [1111 text2video](https://github.com/kabachuha/sd-webui-text2video) extension by [kabachuha](https://github.com/kabachuha). Leveraging this model as a preliminary step allows for superior overall compositions at higher resolutions in zeroscope_v2_XL, permitting faster exploration in 576x320 before transitioning to a high-resolution render. See some [example outputs](https://www.youtube.com/watch?v=HO3APT_0UA4) that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of [dotsimulate](https://www.instagram.com/dotsimulate/))<br />
+
+zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320
+
+### Using it with the 1111 text2video extension
+
+1. Download files in the zs2_576w folder.
+2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory.
+
+### Upscaling recommendations
+
+For upscaling, it's recommended to use [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) via vid2vid in the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip. <br />
+
+### Usage in 🧨 Diffusers
+
+Let's first install the libraries required:
+
+```bash
+$ pip install diffusers transformers accelerate torch
+```
+
+Now, generate a video:
+
+```py
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+
+pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+
+prompt = "Darth Vader is surfing on waves"
+video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
+video_path = export_to_video(video_frames)
+```
+
+Here are some results:
+
+<table>
+    <tr>
+        Darth vader is surfing on waves.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif"
+            alt="Darth vader surfing in waves."
+            style="width: 576;" />
+        </center></td>
+    </tr>
+</table>
+
+### Known issues
+
+Lower resolutions or fewer frames could lead to suboptimal output. <br />
+
+Thanks to [camenduru](https://github.com/camenduru), [kabachuha](https://github.com/kabachuha), [ExponentialML](https://github.com/ExponentialML), [dotsimulate](https://www.instagram.com/dotsimulate/), [VANYA](https://twitter.com/veryVANYA), [polyware](https://twitter.com/polyware_ai), [tin2tin](https://github.com/tin2tin)<br />

lvd_pipeline.py

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+# Copyright 2024 LLM-grounded Video Diffusion Models (LVD) Team and The HuggingFace Team. All rights reserved.
+# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.
+# Copyright 2024 The ModelScope 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.
+
+import inspect
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+import numpy as np
+from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL
+from diffusers.models.attention import GatedSelfAttentionDense
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.models.unets import UNet3DConditionModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.text_to_video_synthesis import \
+    TextToVideoSDPipelineOutput
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (USE_PEFT_BACKEND, deprecate, logging,
+                             replace_example_docstring, scale_lora_layers,
+                             unscale_lora_layers)
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import CLIPTextModel, CLIPTokenizer
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import TextToVideoSDPipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> pipe = TextToVideoSDPipeline.from_pretrained(
+        ...     "damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"
+        ... )
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> prompt = "Spiderman is surfing"
+        >>> video_frames = pipe(prompt).frames
+        >>> video_path = export_to_video(video_frames)
+        >>> video_path
+        ```
+"""
+
+
+def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
+    # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
+    # reshape to ncfhw
+    mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
+    std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
+    # unnormalize back to [0,1]
+    video = video.mul_(std).add_(mean)
+    video.clamp_(0, 1)
+    # prepare the final outputs
+    i, c, f, h, w = video.shape
+    images = video.permute(2, 3, 0, 4, 1).reshape(
+        f, h, i * w, c
+    )  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
+    # prepare a list of indvidual (consecutive frames)
+    images = images.unbind(dim=0)
+    images = [(image.cpu().numpy() * 255).astype("uint8")
+              for image in images]  # f h w c
+    return images
+
+
+class GroundedTextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+    r"""
+    Pipeline for text-to-video generation.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer (`CLIPTokenizer`):
+            A [`~transformers.CLIPTokenizer`] to tokenize text.
+        unet ([`UNet3DConditionModel`]):
+            A [`UNet3DConditionModel`] to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+    """
+
+    model_cpu_offload_seq = "text_encoder->unet->vae"
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet3DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+    ):
+        super().__init__()
+
+        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)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        **kwargs,
+    ):
+        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
+        deprecate("_encode_prompt()", "1.0.0",
+                  deprecation_message, standard_warn=False)
+
+        prompt_embeds_tuple = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=lora_scale,
+            **kwargs,
+        )
+
+        # concatenate for backwards comp
+        prompt_embeds = torch.cat(
+            [prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            lora_scale (`float`, *optional*):
+                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            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
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(
+                    prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(
+            dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and 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
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(
+                    uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.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
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(
+                dtype=prompt_embeds_dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(
+                1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(
+                batch_size * num_images_per_prompt, seq_len, -1)
+
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        batch_size, channels, num_frames, height, width = latents.shape
+        latents = latents.permute(0, 2, 1, 3, 4).reshape(
+            batch_size * num_frames, channels, height, width)
+
+        image = self.vae.decode(latents).sample
+        video = (
+            image[None, :]
+            .reshape(
+                (
+                    batch_size,
+                    num_frames,
+                    -1,
+                )
+                + image.shape[2:]
+            )
+            .permute(0, 2, 1, 3, 4)
+        )
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        video = video.float()
+        return video
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    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
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        lvd_gligen_phrases,
+        lvd_gligen_boxes,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        num_frames=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        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 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)}."
+            )
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (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 negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        if lvd_gligen_boxes:
+            if len(lvd_gligen_phrases) != num_frames or len(lvd_gligen_boxes) != num_frames:
+                raise ValueError(
+                    "length of `lvd_gligen_phrases` and `lvd_gligen_boxes` has to be same and match `num_frames`, but"
+                    f" got: `lvd_gligen_phrases` {len(lvd_gligen_phrases)}, `lvd_gligen_boxes` {len(lvd_gligen_boxes)}, `num_frames` {num_frames}"
+                )
+            else:
+                for frame_index, (lvd_gligen_phrases_frame, lvd_gligen_boxes_frame) in enumerate(zip(lvd_gligen_phrases, lvd_gligen_boxes)):
+                    if len(lvd_gligen_phrases_frame) != len(lvd_gligen_boxes_frame):
+                        raise ValueError(
+                            "length of `lvd_gligen_phrases` and `lvd_gligen_boxes` has to be same, but"
+                            f" got: `lvd_gligen_phrases` {len(lvd_gligen_phrases_frame)} != `lvd_gligen_boxes` {len(lvd_gligen_boxes_frame)} at frame {frame_index}"
+                        )
+
+    def prepare_latents(
+        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            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:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype)
+        else:
+            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
+
+    def enable_fuser(self, enabled=True):
+        for module in self.unet.modules():
+            if type(module) is GatedSelfAttentionDense:
+                module.enabled = enabled
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+        r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stages where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+        that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        if not hasattr(self, "unet"):
+            raise ValueError("The pipeline must have `unet` for using FreeU.")
+        self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+    def disable_freeu(self):
+        """Disables the FreeU mechanism if enabled."""
+        self.unet.disable_freeu()
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_frames: int = 16,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 9.0,
+        lvd_gligen_scheduled_sampling_beta: float = 0.3,
+        lvd_gligen_phrases: List[List[str]] = None,
+        lvd_gligen_boxes: List[List[List[float]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        callback: Optional[Callable[[
+            int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated video.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated video.
+            num_frames (`int`, *optional*, defaults to 16):
+                The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
+                amounts to 2 seconds of video.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            lvd_gligen_phrases (`List[str]`):
+                The phrases to guide what to include in each of the regions defined by the corresponding
+                `lvd_gligen_boxes`. There should only be one phrase per bounding box.
+            lvd_gligen_boxes (`List[List[float]]`):
+                The bounding boxes that identify rectangular regions of the image that are going to be filled with the
+                content described by the corresponding `lvd_gligen_phrases`. Each rectangular box is defined as a
+                `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
+            lvd_gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
+                Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
+                Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
+                scheduled sampling during inference for improved quality and controllability.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
+                `(batch_size, num_channel, num_frames, height, width)`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated video. Choose between `torch.FloatTensor` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
+                of a plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is called with the
+                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function is called. If not specified, the callback is called at
+                every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        Examples:
+
+        Returns:
+            [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
+                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
+        """
+        # 0. 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
+
+        num_images_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, callback_steps, lvd_gligen_phrases,
+            lvd_gligen_boxes, negative_prompt, prompt_embeds, negative_prompt_embeds, num_frames
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        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
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get(
+                "scale", None) if cross_attention_kwargs is not None else None
+        )
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=clip_skip,
+        )
+        # 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
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            num_frames,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5.1 Prepare GLIGEN variables
+        if lvd_gligen_boxes:
+            max_objs = 30
+            boxes_all, text_embeddings_all, masks_all = [], [], []
+            for lvd_gligen_phrases_frame, lvd_gligen_boxes_frame in zip(lvd_gligen_phrases, lvd_gligen_boxes):
+                if len(lvd_gligen_boxes_frame) > max_objs:
+                    warnings.warn(
+                        f"More than {max_objs} objects found. Only first {max_objs} objects will be processed.",
+                        FutureWarning,
+                    )
+                    lvd_gligen_phrases_frame = lvd_gligen_phrases_frame[:max_objs]
+                    lvd_gligen_boxes_frame = lvd_gligen_boxes_frame[:max_objs]
+
+                # prepare batched input to the PositionNet (boxes, phrases, mask)
+                # Get tokens for phrases from pre-trained CLIPTokenizer
+                tokenizer_inputs = self.tokenizer(
+                    lvd_gligen_phrases_frame, padding=True, return_tensors="pt").to(device)
+                # For the token, we use the same pre-trained text encoder
+                # to obtain its text feature
+                _text_embeddings = self.text_encoder(
+                    **tokenizer_inputs).pooler_output
+                n_objs = len(lvd_gligen_boxes_frame)
+                # For each entity, described in phrases, is denoted with a bounding box,
+                # we represent the location information as (xmin,ymin,xmax,ymax)
+                boxes = torch.zeros(max_objs, 4, device=device,
+                                    dtype=self.text_encoder.dtype)
+                boxes[:n_objs] = torch.tensor(lvd_gligen_boxes_frame)
+                text_embeddings = torch.zeros(
+                    max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype
+                )
+                text_embeddings[:n_objs] = _text_embeddings
+                # Generate a mask for each object that is entity described by phrases
+                masks = torch.zeros(max_objs, device=device,
+                                    dtype=self.text_encoder.dtype)
+                masks[:n_objs] = 1
+
+                repeat_batch = batch_size * num_images_per_prompt
+                boxes = boxes.unsqueeze(0).expand(repeat_batch, -1, -1).clone()
+                text_embeddings = text_embeddings.unsqueeze(
+                    0).expand(repeat_batch, -1, -1).clone()
+                masks = masks.unsqueeze(0).expand(repeat_batch, -1).clone()
+                if do_classifier_free_guidance:
+                    repeat_batch = repeat_batch * 2
+                    boxes = torch.cat([boxes] * 2)
+                    text_embeddings = torch.cat([text_embeddings] * 2)
+                    masks = torch.cat([masks] * 2)
+                    masks[: repeat_batch // 2] = 0
+
+                boxes_all.append(boxes)
+                text_embeddings_all.append(text_embeddings)
+                masks_all.append(masks)
+
+            if cross_attention_kwargs is None:
+                cross_attention_kwargs = {}
+
+            # In `UNet3DConditionModel`, there is a permute and reshape to merge batch dimension and frame dimension.
+            boxes_all = torch.stack(boxes_all, dim=1).flatten(0, 1)
+            text_embeddings_all = torch.stack(
+                text_embeddings_all, dim=1).flatten(0, 1)
+            masks_all = torch.stack(masks_all, dim=1).flatten(0, 1)
+            cross_attention_kwargs["gligen"] = {
+                "boxes": boxes_all, "positive_embeddings": text_embeddings_all, "masks": masks_all}
+
+        num_grounding_steps = int(
+            lvd_gligen_scheduled_sampling_beta * len(timesteps))
+        self.enable_fuser(True)
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. 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):
+                # Scheduled sampling
+                if i == num_grounding_steps:
+                    self.enable_fuser(False)
+
+                assert latents.shape[1] == 4, f"latent channel mismatch: {latents.shape}"
+
+                # 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=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # 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)
+
+                # reshape latents
+                bsz, channel, frames, width, height = latents.shape
+                latents = latents.permute(0, 2, 1, 3, 4).reshape(
+                    bsz * frames, channel, width, height)
+                noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(
+                    bsz * frames, channel, width, height)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # reshape latents back
+                latents = latents[None, :].reshape(
+                    bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
+
+                # 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:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if output_type == "latent":
+            return TextToVideoSDPipelineOutput(frames=latents)
+
+        video_tensor = self.decode_latents(latents)
+
+        if output_type == "pt":
+            video = video_tensor
+        else:
+            video = tensor2vid(video_tensor)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return TextToVideoSDPipelineOutput(frames=video)

model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": ["lvd_pipeline", "GroundedTextToVideoSDPipeline"],
+  "_diffusers_version": "0.17.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "lvd_unet_3d_condition",
+    "GroundedUNet3DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.17.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "thresholding": false,
+  "trained_betas": null
+}

text_encoder/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "./models/model_scope_diffusers/",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "vocab_size": 49408
+}

text_encoder/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76877281ed10a4a71f6c2aa0edd286a9e5e23a852a05d13fb05965b464a305bb
+size 680904225

tokenizer/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "!",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

unet/config.json

@@ -0,0 +1,35 @@
+{
+  "_class_name": "GroundedUNet3DConditionModel",
+  "_diffusers_version": "0.17.0.dev0",
+  "_name_or_path": "/home/tony/text-to-video-lvd-zs-1.7b/unet",
+  "act_fn": "silu",
+  "attention_head_dim": 64,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "cross_attention_dim": 1024,
+  "attention_type": "gated",
+  "down_block_types": [
+    "CrossAttnDownBlock3D",
+    "CrossAttnDownBlock3D",
+    "CrossAttnDownBlock3D",
+    "DownBlock3D"
+  ],
+  "downsample_padding": 1,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "out_channels": 4,
+  "sample_size": 32,
+  "up_block_types": [
+    "UpBlock3D",
+    "CrossAttnUpBlock3D",
+    "CrossAttnUpBlock3D",
+    "CrossAttnUpBlock3D"
+  ]
+}

unet/diffusion_pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69d5422e6f3080caf390e2cf209941c20e21632cb68b724c8987588f4e8491c6
+size 3248197593

vae/config.json

@@ -0,0 +1,31 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.17.0.dev0",
+  "_name_or_path": "./models/model_scope_diffusers/",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b0d11ff25d00ceaa02f602831d9cfe650509fdc850c0a1bcb2acdfa03bd5d56
+size 167407857