Add diffusers checkpoints (#5)

- add diffusers (3083e08b5475d3b3378c2f99aa7f3da0fb73660d)


Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>

README_diffusers.md

@@ -0,0 +1,334 @@
+---
+license: mit
+library_name: diffusers
+tags:
+- image-to-video
+pipeline_tag: text-to-video
+---
+# VGen
+
+
+![figure1](source/VGen.jpg "figure1")
+
+VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods:
+
+
+- [I2VGen-xl: High-quality image-to-video synthesis via cascaded diffusion models](https://i2vgen-xl.github.io/)
+- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://videocomposer.github.io/)
+- [Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation](https://higen-t2v.github.io/)
+- [A Recipe for Scaling up Text-to-Video Generation with Text-free Videos]()
+- [InstructVideo: Instructing Video Diffusion Models with Human Feedback]()
+- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://dreamvideo-t2v.github.io/)
+- [VideoLCM: Video Latent Consistency Model](https://arxiv.org/abs/2312.09109)
+- [Modelscope text-to-video technical report](https://arxiv.org/abs/2308.06571)
+
+
+VGen can produce high-quality videos from the input text, images, desired motion, desired subjects, and even the feedback signals provided.  It also offers a variety of commonly used video generation tools such as visualization, sampling, training, inference, join training using images and videos, acceleration, and more. 
+
+
+<a href='https://i2vgen-xl.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2311.04145'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/XUi0y7dxqEQ) <a href='https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441039979087.mp4'><img src='source/logo.png'></a>
+
+
+## 🔥News!!!
+- __[2024.01]__ Diffusers now supports I2VGenXL
+- __[2023.12]__ We release the high-efficiency video generation method [VideoLCM](https://arxiv.org/abs/2312.09109)
+- __[2023.12]__ We release the code and model of I2VGen-XL and the ModelScope T2V
+- __[2023.12]__ We release the T2V method [HiGen](https://higen-t2v.github.io) and customizing T2V method [DreamVideo](https://dreamvideo-t2v.github.io).
+- __[2023.12]__ We write an [introduction docment](doc/introduction.pdf) for VGen and compare I2VGen-XL with SVD.
+- __[2023.11]__ We release a high-quality I2VGen-XL model, please refer to the [Webpage](https://i2vgen-xl.github.io)
+
+
+## TODO
+- [x] Release the technical papers and webpage of [I2VGen-XL](doc/i2vgen-xl.md)
+- [x] Release the code and pretrained models that can generate 1280x720 videos
+- [ ] Release models optimized specifically for the human body and faces
+- [ ] Updated version can fully maintain the ID and capture large and accurate motions simultaneously
+- [ ] Release other methods and the corresponding models
+
+
+## Preparation
+
+The main features of VGen are as follows:
+- Expandability, allowing for easy management of your own experiments.
+- Completeness, encompassing all common components for video generation.
+- Excellent performance, featuring powerful pre-trained models in multiple tasks.
+
+
+### Installation
+
+```
+conda create -n vgen python=3.8
+conda activate vgen
+pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
+pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+### Datasets
+
+We have provided a **demo dataset** that includes images and videos, along with their lists in ``data``. 
+
+*Please note that the demo images used here are for testing purposes and were not included in the training.*
+
+
+### Clone codeb
+
+```
+git clone https://github.com/damo-vilab/i2vgen-xl.git
+cd i2vgen-xl
+```
+
+
+## Getting Started with VGen
+
+### (1) Train your text-to-video model
+
+
+Executing the following command to enable distributed training is as easy as that.
+```
+python train_net.py --cfg configs/t2v_train.yaml
+```
+
+In the `t2v_train.yaml` configuration file, you can specify the data, adjust the video-to-image ratio using `frame_lens`, and validate your ideas with different Diffusion settings, and so on.
+
+- Before the training, you can download any of our open-source models for initialization. Our codebase supports custom initialization and `grad_scale` settings, all of which are included in the `Pretrain` item in yaml file.
+- During the training, you can view the saved models and intermediate inference results in the `workspace/experiments/t2v_train`directory.
+
+After the training is completed, you can perform inference on the model using the following command.
+```
+python inference.py --cfg configs/t2v_infer.yaml
+```
+Then you can find the videos you generated in the `workspace/experiments/test_img_01` directory. For specific configurations such as data, models, seed, etc., please refer to the `t2v_infer.yaml` file.
+
+<!-- <table>
+<center>
+  <tr>
+    <td ><center>
+      <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4"></video>	
+    </center></td>
+    <td ><center>
+      <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4"></video>	
+    </center></td>
+  </tr>
+</center>
+</table>
+</center> -->
+
+<table>
+<center>
+  <tr>
+    <td ><center>
+      <image  height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01Ya2I5I25utrJwJ9Jf_!!6000000007587-2-tps-1280-720.png"></image>	
+    </center></td>
+    <td ><center>
+      <image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01CrmYaz1zXBetmg3dd_!!6000000006723-2-tps-1280-720.png"></image>	
+    </center></td>
+  </tr>
+  <tr>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4">HRER</a> to view the generated video.</p>
+    </center></td>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4">HRER</a> to view the generated video.</p>
+    </center></td>
+  </tr>
+</center>
+</table>
+</center>
+
+
+### (2) Run the I2VGen-XL model
+
+(i) Download model and test data:
+```
+!pip install modelscope
+from modelscope.hub.snapshot_download import snapshot_download
+model_dir = snapshot_download('damo/I2VGen-XL', cache_dir='models/', revision='v1.0.0')
+```
+
+(ii) Run the following command:
+```
+python inference.py --cfg configs/i2vgen_xl_infer.yaml
+```
+In a few minutes, you can retrieve the high-definition video you wish to create from the `workspace/experiments/test_img_01` directory. At present, we find that the current model performs inadequately on **anime images** and **images with a black background** due to the lack of relevant training data. We are consistently working to optimize it.
+
+
+<span style="color:red">Due to the compression of our video quality in GIF format, please click 'HRER' below to view the original video.</span>
+
+<center>
+<table>
+<center>
+  <tr>
+    <td ><center>
+      <image  height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01CCEq7K1ZeLpNQqrWu_!!6000000003219-0-tps-1280-720.jpg"></image>	
+    </center></td>
+    <td ><center>
+      <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4"></video>	 -->
+      <image  height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01hIQcvG1spmQMLqBo0_!!6000000005816-1-tps-1280-704.gif"></image>	
+    </center></td>
+  </tr> 
+  <tr>
+    <td ><center>
+      <p>Input Image</p>
+    </center></td>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4">HRER</a> to view the generated video.</p>
+    </center></td>
+  </tr> 
+  <tr>
+    <td ><center>
+      <image  height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01ZXY7UN23K8q4oQ3uG_!!6000000007236-2-tps-1280-720.png"></image>	
+    </center></td>
+    <td ><center>
+      <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4"></video>	 -->
+      <image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01iaSiiv1aJZURUEY53_!!6000000003309-1-tps-1280-704.gif"></image>	
+    </center></td>
+  </tr>
+  <tr>
+    <td ><center>
+      <p>Input Image</p>
+    </center></td>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4">HRER</a> to view the generated video.</p>
+    </center></td>
+  </tr> 
+  <tr>
+    <td ><center>
+      <image  height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01NHpVGl1oat4H54Hjf_!!6000000005242-2-tps-1280-720.png"></image>	
+    </center></td>
+    <td ><center>
+      <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4"></video>	 -->
+      <!-- <image muted="true" height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>	
+       -->
+      <image  height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>
+    </center></td>
+  </tr> 
+  <tr>
+    <td ><center>
+      <p>Input Image</p>
+    </center></td>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4">HERE</a> to view the generated video.</p>
+    </center></td>
+  </tr> 
+  <tr>
+    <td ><center>
+      <image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01odS61s1WW9tXen21S_!!6000000002795-0-tps-1280-720.jpg"></image>	
+    </center></td>
+    <td ><center>
+      <!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4"></video>	 -->
+      <image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01Jyk1HT28JkZtpAtY6_!!6000000007912-1-tps-1280-704.gif"></image>	
+    </center></td>
+  </tr>
+  <tr>
+    <td ><center>
+      <p>Input Image</p>
+    </center></td>
+    <td ><center>
+      <p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4">HERE</a> to view the generated video.</p>
+    </center></td>
+  </tr> 
+</center>
+</table>
+</center>
+
+### (3) Other methods
+
+In preparation.
+
+
+## Customize your own approach
+
+Our codebase essentially supports all the commonly used components in video generation. You can manage your experiments flexibly by adding corresponding registration classes, including `ENGINE, MODEL, DATASETS, EMBEDDER, AUTO_ENCODER, DISTRIBUTION, VISUAL, DIFFUSION, PRETRAIN`, and can be compatible with all our open-source algorithms according to your own needs. If you have any questions, feel free to give us your feedback at any time.
+
+## Integration of I2VGenXL with 🧨 diffusers
+
+I2VGenXL is supported in the 🧨 diffusers library. Here's how to use it:
+
+```python
+import torch
+from diffusers import I2VGenXLPipeline
+from diffusers.utils import load_image, export_to_gif
+
+repo_id = "ali-vilab/i2vgen-xl" 
+pipeline = I2VGenXLPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+image_url = "https://github.com/ali-vilab/i2vgen-xl/blob/main/data/test_images/img_0009.png?download=true"
+image = load_image(image_url).convert("RGB")
+prompt = "Papers were floating in the air on a table in the library"
+
+generator = torch.manual_seed(8888)
+frames = pipeline(
+    prompt=prompt,
+    image=image,
+    generator=generator
+).frames[0]
+
+print(export_to_gif(frames))
+```
+
+Find the official documentation [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/i2vgenxl). 
+
+Sample output with I2VGenXL:
+
+<table>
+    <tr>
+        <td><center>
+        masterpiece, bestquality, sunset.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/i2vgen-xl-example.gif"
+            alt="library"
+            style="width: 300px;" />
+        </center></td>
+    </tr>
+</table>
+
+## BibTeX
+
+If this repo is useful to you, please cite our corresponding technical paper.
+
+
+```bibtex
+@article{2023i2vgenxl,
+  title={I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models},
+  author={Zhang, Shiwei and Wang, Jiayu and Zhang, Yingya and Zhao, Kang and Yuan, Hangjie and Qing, Zhiwu and Wang, Xiang  and Zhao, Deli and Zhou, Jingren},
+  booktitle={arXiv preprint arXiv:2311.04145},
+  year={2023}
+}
+@article{2023videocomposer,
+  title={VideoComposer: Compositional Video Synthesis with Motion Controllability},
+  author={Wang, Xiang and Yuan, Hangjie and Zhang, Shiwei and Chen, Dayou and Wang, Jiuniu, and Zhang, Yingya, and Shen, Yujun, and Zhao, Deli and Zhou, Jingren},
+  booktitle={arXiv preprint arXiv:2306.02018},
+  year={2023}
+}
+@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}
+}
+@article{dreamvideo,
+  title={DreamVideo: Composing Your Dream Videos with Customized Subject and Motion},
+  author={Wei, Yujie and Zhang, Shiwei and Qing, Zhiwu and Yuan, Hangjie and Liu, Zhiheng and Liu, Yu and Zhang, Yingya and Zhou, Jingren and Shan, Hongming},
+  journal={arXiv preprint arXiv:2312.04433},
+  year={2023}
+}
+@article{qing2023higen,
+  title={Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation},
+  author={Qing, Zhiwu and Zhang, Shiwei and Wang, Jiayu and Wang, Xiang and Wei, Yujie and Zhang, Yingya and Gao, Changxin and Sang, Nong },
+  journal={arXiv preprint arXiv:2312.04483},
+  year={2023}
+}
+@article{wang2023videolcm,
+  title={VideoLCM: Video Latent Consistency Model},
+  author={Wang, Xiang and Zhang, Shiwei and Zhang, Han and Liu, Yu and Zhang, Yingya and Gao, Changxin and Sang, Nong },
+  journal={arXiv preprint arXiv:2312.09109},
+  year={2023}
+}
+```
+
+## Disclaimer
+
+This open-source model is trained with using [WebVid-10M](https://m-bain.github.io/webvid-dataset/) and [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/) datasets and is intended for <strong>RESEARCH/NON-COMMERCIAL USE ONLY</strong>.
+
+
+

feature_extractor/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
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+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
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+  }
+}

image_encoder/config.json

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+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 32,
+  "patch_size": 14,
+  "projection_dim": 1024,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0.dev0"
+}

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model_index.json

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+{
+  "_class_name": "I2VGenXLPipeline",
+  "_diffusers_version": "0.26.0.dev0",
+  "_name_or_path": "diffusers/i2vgen-xl",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "I2VGenXLUNet"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.26.0.dev0",
+  "beta_end": 0.02,
+  "beta_schedule": "squaredcos_cap_v2",
+  "beta_start": 0.0001,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": true,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": true,
+  "steps_offset": 1,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

text_encoder/config.json

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+  "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": 24,
+  "pad_token_id": 1,
+  "projection_dim": 1024,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0.dev0",
+  "vocab_size": 49408
+}

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tokenizer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+}

tokenizer/tokenizer_config.json

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+{
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+  "added_tokens_decoder": {
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unet/config.json

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