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+
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+
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README.md

@@ -1,3 +1,99 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# Pusa V1.0 Model
+
+[Code Repository](https://github.com/Yaofang-Liu/Pusa-VidGen) | [Project Page](https://yaofang-liu.github.io/Pusa_Web/) |[Dataset](https://huggingface.co/datasets/RaphaelLiu/PusaV0.5_Training) |[Model](https://huggingface.co/RaphaelLiu/PusaV1) |  [Paper (Pusa V1.0)](https://github.com/Yaofang-Liu/Pusa-VidGen) | [Paper (FVDM)](https://arxiv.org/abs/2410.03160) | [Follow on X](https://x.com/stephenajason) | [Xiaohongshu](https://www.xiaohongshu.com/explore/67f898dc000000001c008339?source=webshare&xhsshare=pc_web&xsec_token=ABAhG8mltqyMxL9kI0eRxwj7EwiW7MFYH2oPl4n8ww0OM=&xsec_source=pc_share)
+
+## Overview
+
+The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. While task-specific adaptations and autoregressive models have sought to address these challenges, they remain constrained by computational inefficiency, catastrophic forgetting, or narrow applicability.
+
+In this work, we present **Pusa**¹, a groundbreaking paradigm that leverages **vectorized timestep adaptation (VTA)** to enable fine-grained temporal control within a unified video diffusion framework. VTA is a non-destructive adaptation, meaning it fully preserves the capabilities of the base model. By finetuning the SOTA Wan2.1-T2V-14B model with VTA, we achieve unprecedented efficiency—**surpassing the performance of Wan-I2V-14B with ≤ 1/200 of the training cost ($500 vs. ≥ $100,000)** and **≤ 1/2500 of the dataset size (4K vs. ≥ 10M samples)**.
+
+Pusa not only sets a new standard for image-to-video (I2V) generation but also **unlocks many zero-shot multi-task capabilities such as start-end frames and video extension**—all without task-specific training. Meanwhile, **Pusa can still perform text-to-video generation**. This work establishes a scalable, efficient, and versatile paradigm for next-generation video synthesis, democratizing high-fidelity video generation for research and industry alike.
+
+¹*Pusa (菩萨, /pu: 'sA:/) normally refers to "Thousand-Hand Guanyin" in Chinese, reflecting the iconography of many hands to symbolize her boundless compassion and ability. We use this name to indicate that our model uses many timestep variables to achieve numerous video generation capabilities, and we will fully open source it to let the community benefit from this tech.*
+
+## Performance Highlights
+
+Pusa V1.0, with only 10 inference steps, achieves state-of-the-art performance among open-source models. It surpasses its direct baseline, `Wan-I2V`, which was trained with vastly greater resources. Our model obtains a VBench-I2V total score of **87.32%**, outperforming `Wan-I2V`'s 86.86%.
+
+<!-- <p align="center"><img src="pusa_benchmark_figure_dark.png" width="80%"></p> -->
+
+## ✨ Key Features
+
+- **Comprehensive Multi-task Support**:
+  - Image-to-Video
+  - Start-End Frames
+  - Video Completion
+  - Video Extension
+  - Text-to-Video
+  - Video Transition
+  - And more...
+
+- **Unprecedented Efficiency**:
+  - Fine-tuned on `Wan-T2V` using LoRA for training with less resources.
+  - Total training cost: **$0.5K** (at least **200x** more efficient than the `Wan-I2V` baseline).
+  - Dataset size: **4K samples** (at least **2500x** smaller than the `Wan-I2V` baseline).
+  - Hardware: 8x 80GB GPUs with DeepSpeed Zero2.
+  - *Note: Our method is also compatible with full fine-tuning as demonstrated in Pusa V0.5.*
+
+- **Complete Open-Source Release**
+  - Full codebase
+  - Checkpoints
+  - Training Dataset
+  - Paper
+
+
+## 🔍 Unique Architecture
+
+- **Novel Diffusion Paradigm**: Enables frame-level noise control with vectorized timesteps, originally introduced in the [FVDM paper](https://arxiv.org/abs/2410.03160), enabling unprecedented flexibility and scalability.
+
+- **Efficient & Non-destructive Adaptation**: We perform lightweight fine-tuning on the SOTA open-source `Wan-T2V` model using the vectorized timestep adaptation (VTA) technique. This VTA approach make it easy to injects temporal dynamics while fully preserving the foundation model's generative priors and T2V capabilities.
+
+- **Universal Applicability**: Pusa V1.0 demonstrates the successful application of FVDM framework to the state-of-the-art `Wan-T2V` model. The methodology can be readily applied to other leading video diffusion models. *Collaborations enthusiastically welcomed!*
+
+## Installation and Usage
+
+### Download Weights and Recover The Checkpoint
+
+**Option 1**: Use the Hugging Face CLI:
+```bash
+pip install huggingface_hub
+huggingface-cli download RaphaelLiu/PusaV1 --local-dir <path_to_downloaded_directory>
+cat ./PusaV1/pusa_v1.pt.part* > ./PusaV1/pusa_v1.pt
+```
+
+**Option 2**: Download directly from [Hugging Face](https://huggingface.co/RaphaelLiu/PusaV1) to your local machine.
+
+## Related Work
+
+- [FVDM](https://arxiv.org/abs/2410.03160): Introduces the groundbreaking frame-level noise control with vectorized timestep approach that inspired Pusa.
+- [Wan-T2V](https://huggingface.co/papers/2405.07772): The base model for Pusa V1.0, a state-of-the-art open-source video generation model.
+- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): We leverage its optimized LoRA implementation for efficient diffusion model training.
+
+## Citation
+
+If you find our work useful in your research, please consider citing:
+
+```
+@misc{Liu2025pusa,
+  title={Pusa: Thousands Timesteps Video Diffusion Model},
+  author={Yaofang Liu and Rui Liu},
+  year={2025},
+  url={https://github.com/Yaofang-Liu/Pusa-VidGen},
+}
+```
+
+```
+@article{liu2024redefining,
+  title={Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach},
+  author={Liu, Yaofang and Ren, Yumeng and Cun, Xiaodong and Artola, Aitor and Liu, Yang and Zeng, Tieyong and Chan, Raymond H and Morel, Jean-michel},
+  journal={arXiv preprint arXiv:2410.03160},
+  year={2024}
+}
+```
+
+

Wan2.1-I2V-14B-720P/.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+assets/comp_effic.png filter=lfs diff=lfs merge=lfs -text
+assets/data_for_diff_stage.jpg filter=lfs diff=lfs merge=lfs -text
+assets/i2v_res.png filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text
+assets/t2v_res.jpg filter=lfs diff=lfs merge=lfs -text
+assets/vben_1.3b_vs_sota.png filter=lfs diff=lfs merge=lfs -text
+assets/vben_vs_sota.png filter=lfs diff=lfs merge=lfs -text
+assets/video_dit_arch.jpg filter=lfs diff=lfs merge=lfs -text
+assets/video_vae_res.jpg filter=lfs diff=lfs merge=lfs -text
+examples/i2v_input.JPG filter=lfs diff=lfs merge=lfs -text

Wan2.1-I2V-14B-720P/README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: image-to-video
+library_name: diffusers
+tags:
+- video
+- video genration
+---
+# Wan2.1
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    💜 <a href=""><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="">Paper (Coming soon)</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wanxai.com">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>&nbsp&nbsp
+<br>
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**]() <be>
+
+In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
+- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
+- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
+- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
+- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
+- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
+
+
+This repository contains our I2V-14B model, which is capable of generating 720P high-definition videos. After thousands of rounds of human evaluations, this model has outperformed both closed-source and open-source alternatives, achieving state-of-the-art performance.
+
+
+## Video Demos
+
+<div align="center">
+    <video width="80%" controls>
+        <source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+</div>
+
+## 🔥 Latest News!!
+
+* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
+
+
+## 📑 Todo List
+- Wan2.1 Text-to-Video
+    - [x] Multi-GPU Inference code of the 14B and 1.3B models
+    - [x] Checkpoints of the 14B and 1.3B models
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+- Wan2.1 Image-to-Video
+    - [x] Multi-GPU Inference code of the 14B model
+    - [x] Checkpoints of the 14B model
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+
+
+## Quickstart
+
+#### Installation
+Clone the repo:
+```
+git clone https://github.com/Wan-Video/Wan2.1.git
+cd Wan2.1
+```
+
+Install dependencies:
+```
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
+
+
+#### Model Download
+
+| Models        |                       Download Link                                           |    Notes                      |
+| --------------|-------------------------------------------------------------------------------|-------------------------------|
+| T2V-14B       |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)      🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)          | Supports both 480P and 720P
+| I2V-14B-720P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)     | Supports 720P
+| I2V-14B-480P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)      | Supports 480P
+| T2V-1.3B      |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)         | Supports 480P
+
+> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
+
+
+Download models using huggingface-cli:
+```
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./Wan2.1-I2V-14B-720P
+```
+
+
+#### Run Image-to-Video Generation
+
+Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows:
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">❌</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-I2V-14B-720P</td>
+        </tr>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-14B-480P</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extention
+
+- Single-GPU inference
+```
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+> 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+##### (2) Using Prompt Extention
+
+
+Run with local prompt extention using `Qwen/Qwen2.5-VL-7B-Instruct`:
+```
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+Run with remote prompt extention using `dashscope`:
+```
+DASH_API_KEY=your_key python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+##### (3) Runing local gradio
+
+```
+cd gradio
+# if one only uses 480P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P
+
+# if one only uses 720P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+
+# if one uses both 480P and 720P models in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+```
+
+
+## Manual Evaluation
+
+We conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that **Wan2.1** outperforms both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/i2v_res.png" alt="" style="width: 80%;" />
+</div>
+
+
+## Computational Efficiency on Different GPUs
+
+We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
+
+
+<div align="center">
+    <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
+</div>
+
+> The parameter settings for the tests presented in this table are as follows:
+> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1**  is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+<div align="center">
+    <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+<div align="center">
+    <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+| Model  | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B   | 1536      | 16              | 16               | 8960                  | 256                 | 12              | 30               |
+| 14B   | 5120       | 16              | 16               | 13824                 | 256                 | 40              | 40               |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+![figure1](assets/data_for_diff_stage.jpg "figure1")
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+![figure1](assets/vben_vs_sota.png "figure1")
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2.1,
+    title   = {Wan: Open and Advanced Large-Scale Video Generative Models},
+    author  = {Wan Team},
+    journal = {},
+    year    = {2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
+
+
+
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!

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Wan2.1-T2V-14B/LICENSE.txt

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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
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+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
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Wan2.1-T2V-14B/README.md

@@ -0,0 +1,298 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-to-video
+tags:
+- video generation
+library_name: diffusers
+---
+# Wan2.1
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    💜 <a href=""><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="">Paper (Coming soon)</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wanxai.com">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>&nbsp&nbsp
+<br>
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**]("") <be>
+
+In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
+- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
+- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
+- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
+- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
+- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
+
+This repository features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions.
+
+
+## Video Demos
+
+<div align="center">
+    <video width="80%" controls>
+        <source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+</div>
+
+## 🔥 Latest News!!
+
+* Feb 22, 2025: 👋 We've released the inference code and weights of Wan2.1.
+
+
+## 📑 Todo List
+- Wan2.1 Text-to-Video
+    - [x] Multi-GPU Inference code of the 14B and 1.3B models
+    - [x] Checkpoints of the 14B and 1.3B models
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+- Wan2.1 Image-to-Video
+    - [x] Multi-GPU Inference code of the 14B model
+    - [x] Checkpoints of the 14B model
+    - [x] Gradio demo
+    - [ ] Diffusers integration
+    - [ ] ComfyUI integration
+
+
+## Quickstart
+
+#### Installation
+Clone the repo:
+```
+git clone https://github.com/Wan-Video/Wan2.1.git
+cd Wan2.1
+```
+
+Install dependencies:
+```
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
+
+
+#### Model Download
+
+| Models        |                       Download Link                                           |    Notes                      |
+| --------------|-------------------------------------------------------------------------------|-------------------------------|
+| T2V-14B       |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)      🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)          | Supports both 480P and 720P
+| I2V-14B-720P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)     | Supports 720P
+| I2V-14B-480P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)      | Supports 480P
+| T2V-1.3B      |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)         | Supports 480P
+
+> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
+
+
+Download models using 🤗 huggingface-cli:
+```
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
+```
+
+Download models using 🤖 modelscope-cli:
+```
+pip install modelscope
+modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan2.1-T2V-14B
+```
+#### Run Text-to-Video Generation
+
+This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
+
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>t2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-T2V-14B</td>
+        </tr>
+        <tr>
+            <td>t2v-1.3B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-1.3B</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extention
+
+To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
+
+- Single-GPU inference
+
+```
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
+
+```
+python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
+
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+
+##### (2) Using Prompt Extention
+
+Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
+
+- Use the Dashscope API for extension.
+  - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
+  - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
+  - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
+```
+DASH_API_KEY=your_key python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'ch'
+```
+
+- Using a local model for extension.
+
+  - By default, the Qwen model on HuggingFace is used for this extension. Users can choose based on the available GPU memory size.
+  - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`
+  - For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
+  - Larger models generally provide better extension results but require more GPU memory.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
+
+```
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch'
+```
+
+##### (3) Runing local gradio
+
+```
+cd gradio
+# if one uses dashscope’s API for prompt extension
+DASH_API_KEY=your_key python t2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-14B
+
+# if one uses a local model for prompt extension
+python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+
+## Manual Evaluation
+
+
+Through manual evaluation, the results generated after prompt extension are superior to those from both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/t2v_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+
+## Computational Efficiency on Different GPUs
+
+We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
+
+
+<div align="center">
+    <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
+</div>
+
+> The parameter settings for the tests presented in this table are as follows:
+> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+
+## Community Contributions
+- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for Wan, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1**  is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+<div align="center">
+    <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+<div align="center">
+    <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+| Model  | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B   | 1536      | 16              | 16               | 8960                  | 256                 | 12              | 30               |
+| 14B   | 5120       | 16              | 16               | 13824                 | 256                 | 40              | 40               |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+![figure1](assets/data_for_diff_stage.jpg "figure1")
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+![figure1](assets/vben_vs_sota.png "figure1")
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2.1,
+    title   = {Wan: Open and Advanced Large-Scale Video Generative Models},
+    author  = {Wan Team},
+    journal = {},
+    year    = {2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
+
+
+
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!

Wan2.1-T2V-14B/Wan2.1_VAE.pth

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