Add link to paper and project page

This PR adds a link to the paper and the project page.

README.md

@@ -1,14 +1,15 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
-pipeline_tag: text-to-video
 library_name: diffusers
+license: apache-2.0
+pipeline_tag: text-to-video
 tags:
 - video
 - video-generation
 ---
+
 # Wan2.1
 
 <p align="center">
@@ -16,12 +17,12 @@ tags:
 <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
+    💜 <a href="https://wan.video"><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="https://huggingface.co/papers/2503.20314">Paper</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">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/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
 <br>
 
 -----
 
-[**Wan: Open and Advanced Large-Scale Video Generative Models**]("#") <be>
+[**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.
@@ -30,23 +31,23 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
 - 👍 **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 hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.
-
-
 ## 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>
+  <video src="https://github.com/user-attachments/assets/4aca6063-60bf-4953-bfb7-e265053f49ef" width="70%" poster=""> </video>
 </div>
 
-
 ## 🔥 Latest News!!
 
-* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
+* Mar 21, 2025: 👋 We are excited to announce the release of the **Wan2.1** [technical report](https://files.alicdn.com/tpsservice/5c9de1c74de03972b7aa657e5a54756b.pdf). We welcome discussions and feedback!
+* Mar 3, 2025: 👋 **Wan2.1**'s T2V and I2V have been integrated into Diffusers ([T2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline) | [I2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanImageToVideoPipeline)). Feel free to give it a try!
+* Feb 27, 2025: 👋 **Wan2.1** has been integrated into [ComfyUI](https://comfyanonymous.github.io/ComfyUI_examples/wan/). Enjoy!
+* Feb 25, 2025: 👋 We've released the inference code and weights of **Wan2.1**.
+
+## Community Works
+If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
+- [TeaCache](https://github.com/ali-vilab/TeaCache) now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!
+- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for **Wan2.1**, 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).
 
 
 ## 📑 Todo List
@@ -54,27 +55,29 @@ This repository hosts our T2V-1.3B model, a versatile solution for video generat
     - [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
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
 - 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
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
 
 
 ## Quickstart
 
 #### Installation
 Clone the repo:
-```
+```sh
 git clone https://github.com/Wan-Video/Wan2.1.git
 cd Wan2.1
 ```
 
 Install dependencies:
-```
+```sh
 # Ensure torch >= 2.4.0
 pip install -r requirements.txt
 ```
@@ -89,22 +92,20 @@ pip install -r requirements.txt
 | 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:
-```
+Download models using huggingface-cli:
+``` sh
 pip install "huggingface_hub[cli]"
-huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B
+huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
 ```
 
-Download models using 🤖 modelscope-cli:
-```
+Download models using modelscope-cli:
+``` sh
 pip install modelscope
-modelscope download Wan-AI/Wan2.1-T2V-1.3B --local_dir ./Wan2.1-T2V-1.3B
+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:
@@ -138,33 +139,47 @@ This repository supports two Text-to-Video models (1.3B and 14B) and two resolut
 </table>
 
 
-##### (1) Without Prompt Extention
+##### (1) Without Prompt Extension
 
 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-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+``` sh
+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:
 
-```
+``` sh
 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
 
-```
+  We use FSDP and [xDiT](https://github.com/xdit-project/xDiT) USP to accelerate  inference.
+
+  * Ulysess Strategy
+
+    If you want to use [`Ulysses`](https://arxiv.org/abs/2309.14509) strategy, you should set `--ulysses_size $GPU_NUMS`. Note that the `num_heads` should be divisible by `ulysses_size` if you wish to use `Ulysess` strategy. For the 1.3B model, the `num_heads` is `12` which can't be divided by 8 (as most multi-GPU machines have 8 GPUs). Therefore, it is recommended to use `Ring Strategy` instead.
+
+  * Ring Strategy
+
+    If you want to use [`Ring`](https://arxiv.org/pdf/2310.01889) strategy, you should set `--ring_size $GPU_NUMS`. Note that the `sequence length` should be divisible by `ring_size` when using the `Ring` strategy.
+
+  Of course, you can also combine the use of `Ulysses` and `Ring` strategies.
+
+
+``` sh
 pip install "xfuser>=0.4.1"
-torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 8 --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+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
+##### (2) Using Prompt Extension
 
 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:
 
@@ -173,44 +188,245 @@ Extending the prompts can effectively enrich the details in the generated videos
   - 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-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --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'
+```sh
+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 'zh'
 ```
 
 - 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`
+  - By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models 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-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --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'
+``` sh
+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 'zh'
 ```
 
-##### (3) Runing local gradio
 
+##### (3) Running with Diffusers
+
+You can easily inference **Wan2.1**-T2V using Diffusers with the following command:
+``` python
+import torch
+from diffusers.utils import export_to_video
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+
+# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
+scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift)
+pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+pipe.scheduler = scheduler
+pipe.to("cuda")
+
+prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+output = pipe(
+     prompt=prompt,
+     negative_prompt=negative_prompt,
+     height=720,
+     width=1280,
+     num_frames=81,
+     guidance_scale=5.0,
+    ).frames[0]
+export_to_video(output, "output.mp4", fps=16)
 ```
+> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
+
+
+##### (4) Running local gradio
+
+``` sh
 cd gradio
 # if one uses dashscope’s API for prompt extension
-DASH_API_KEY=your_key python t2v_1.3B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-1.3B
+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_1.3B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-1.3B
+python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+
+
+#### 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 Extension
+
+- Single-GPU inference
+```sh
+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
+
+```sh
+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."
+```
 
-## Evaluation
+##### (2) Using Prompt Extension
 
-We employ our **Wan-Bench** framework to evaluate the performance of the T2V-1.3B model, with the results displayed in the table below. The results indicate that our smaller 1.3B model surpasses the overall metrics of larger open-source models, demonstrating the effectiveness of **WanX2.1**'s architecture and the data construction pipeline.
+
+The process of prompt extension can be referenced [here](#2-using-prompt-extention).
+
+Run with local prompt extension 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 extension 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) Running with Diffusers
+
+You can easily inference **Wan2.1**-I2V using Diffusers with the following command:
+``` python
+import torch
+import numpy as np
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+
+# Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+model_id = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+image = image.resize((width, height))
+prompt = (
+    "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
+    "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+)
+negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=height, width=width,
+    num_frames=81,
+    guidance_scale=5.0
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+
+```
+> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
+
+
+##### (4) Running local gradio
+
+```sh
+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
+```
+
+
+#### Run Text-to-Image Generation
+
+Wan2.1 is a unified model for both image and video generation. Since it was trained on both types of data, it can also generate images. The command for generating images is similar to video generation, as follows:
+
+##### (1) Without Prompt Extension
+
+- Single-GPU inference
+```sh
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人'
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```sh
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+##### (2) With Prompt Extention
+
+- Single-GPU inference
+```sh
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+```sh
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+
+## Manual Evaluation
+
+##### (1) Text-to-Video 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/vben_1.3b_vs_sota.png" alt="" style="width: 80%;" />
+    <img src="assets/t2v_res.jpg" alt="" style="width: 80%;" />
 </div>
 
 
+##### (2) Image-to-Video Evaluation
+
+We also 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
 
@@ -227,6 +443,9 @@ We test the computational efficiency of different **Wan2.1** models on different
 > (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.
 
+> 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps.
+
+
 -------
 
 ## Introduction of Wan2.1
@@ -255,44 +474,4 @@ We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically d
 | 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. Then we calculated the total score through a weighted average based on the importance of each dimension. 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)!
+| 14B   | 5120       | 16              | 16               | 13824