--- license: apache-2.0 language: - en - zh pipeline_tag: image-to-video library_name: diffusers tags: - video - video genration --- # Wan2.1 π Wan | π₯οΈ GitHub | π€ Hugging Face | π€ ModelScope | π Paper (Coming soon) | π Blog | π¬ WeChat Group | π Discord ----- [**Wan: Open and Advanced Large-Scale Video Generative Models**]() 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: - π **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - π **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. - π **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. - π **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. - π **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 Your browser does not support the video tag. ## π₯ Latest News!! * Feb 25, 2025: π We've released the inference code and weights of Wan2.1. ## π 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 | π€ [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) π€ [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P | I2V-14B-720P | π€ [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) π€ [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P | I2V-14B-480P | π€ [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) π€ [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P | T2V-1.3B | π€ [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) π€ [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P > π‘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 π€ huggingface-cli: ``` pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./Wan2.1-I2V-14B-720P ``` Download models using π€ modelscope-cli: ``` pip install modelscope modelscope 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: Task Resolution Model 480P 720P i2v-14B β βοΈ Wan2.1-I2V-14B-720P i2v-14B βοΈ β Wan2.1-T2V-14B-480P ##### (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." ``` > π‘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. ## 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)**. > 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 modelβs 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. ##### (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. | 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.  ##### 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...
