Wan2.2

How to run Wan2.2 locally on 8 GB VRAM (!!model code changed!!)

huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B
convert high_noise_model and low_noise_model to float16 to fit one block in 8GB VRAM with convert_safetensors.py
run optimize_files.py - split safetensors files by modules (run after convert_safetensors.py)
python generate_local.py --task t2v-A14B --size "1280*720" --ckpt_dir ./Wan2.2-T2V-A14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."

Generated frames are limited to 21 (1.3 sec 1280*704) to fit within 8 GB VRAM.

For full 5 second video max resolution: 720*405 (on 8Gb VRAM)

* tested on HELIOS PREDATOR 300 laptop (3070Ti 8GB) 1280*704 - 72.22 s/it for 21 frames, 60.74 s/it for 17 frames, 13 frames 48.70 s/it

Wan2.2 on 8GB VRAM: Run Advanced AI Video Generation Locally! (Optimization Guide) https://youtu.be/LlqnghCNxXM

UPDATE 10/02/2025

Optimized I2V-A14B run long video generation loop with loop.bat

c_o_t1.mp4

How it works

The setup is the same as for the T2V model: huggingface-cli download Wan-AI/Wan2.2-I2V-A14B --local-dir ./Wan2.2-I2V-A14B, then run the convert and optimize scripts.
run example: python generate_local.py --task i2v-A14B --size "1280*720" --image=./last_frame.png --ckpt_dir ./Wan2.2-I2V-A14B --prompt "In close-up, a cheetah runs at full speed in a narrow canyon, its golden fur gleaming in the sun, and its black tear marks clearly visible. Shot from a low angle, the cheetah's body is close to the ground, its muscles flowing, and its limbs alternately and powerfully step over stones and soil, stirring up dust. The cheetah's eyes are sharp, staring at the target in front of it, showing unparalleled speed and strength. The camera follows the cheetah's running trajectory, capturing every moment of leaping and turning, showing its amazing agility. The whole scene unfolds in a tense chase rhythm, full of wild charm and competition for survival."
or edit prompt in loop.bat and run (command runs in loop, each iteration do one spep: create latent from image -> y_latents.pt, run inference -> final_latents.pt, decode video final_latents.pt -> last_frame_latents.pt, create latent from last frame last_frame_latents.pt -> y_latents.pt, run inference ...)
to start new generation loop with new image / prompt / frame count / size - delete: y_latents.pt, final_latents.pt, last_frame_latents.pt

Results on a 3070 Ti laptop GPU with 8 GB VRAM + 25 GB free RAM (some layers are loaded from the NVME drive; to fit everything in RAM, 30 GB of free RAM is needed):

    # size 640*352
    # 81 frames             58.23 s/it 51.32 s/it (*FP8)
    # 33 frames             23.75 s/it              vae decode 4.5 sec

    # 704 * 396, sampling_steps 25+
    # frame_num = 49        24.72 s/it (FP16)
    # frame_num = 81        77.50 s/it (FP16)

    # size 720*405, sampling_steps 20+
    # frame_num = 17        21.23 s/it (FP16)        vae decode 5.4 sec
    # frame_num = 77        82.11 s/it (FP16)
    # frame_num = 81 (best) 70.74 s/it (*FP8)        vae decode 12.2 sec

    # size 832*464 / 848*448, sampling_steps 20+
    # frame_num = 17        23.68 s/it               vae decode 3.54 sec
    # frame_num = 53        74.34 s/it
    # 65                    79.73 s/it

    # size 960*540, sampling_steps 16+
    # 17 frames             34.30 s/it (FP16)
    # 41 frames             75.02 s/it (FP16)
    # 45 frames             72.35 s/it (*FP8)       vae decode 11.7 sec

    # size = 1120 * 630
    # 13 frames         29.24 s/it (*FP8)
    # 17 frames         37.90 s/it (*FP8)           vae decode 13.6 sec
    # 33 frames (max)   85.10 s/it (FP16)
    # 33 frames         76.49 s/it (*FP8)
    # 37                85.16 s/it (*FP8)

    ######################################################
    # for 8gb vram and sizes > 1120 * 630 vae use slow shared video memory

    # size 1280*720, sampling_steps 16+
    # 13 frames         48.70 s/it (FP16)
    # 13 frames         39.61 s/it (*FP8)          vae decode 17.4 sec
    # 17 frames         60.74 s/it (FP16)
    # 17 frames         54.02 s/it (*FP8)
    # 21 frames (max)   72.22 s/it (FP16)
    # 21 frames         66.18 s/it (*FP8)           vae decode 28 sec

    # size 1600*896 / 1568*896, sampling_steps 15+
    # 13 frames (max)   85.47 s/it (FP16)
    # 13 frames (max)   63.88 s/it (*FP8)           vae decode ~115 sec

    self.offload_large_tensors = False  # slower 20% inference but more frames per video
    # ################# large tensors offloading ##########################

    # 1280*720
    # 33 frames         118.83 s/it (*FP8)          vae decode 38 sec

    # 1568*896
    # 21 frames         127.01 s/it (*FP8)          vae decode: 182 sec

Compared to ComfyUA

          ComfyUA (fp8)                        This (fp16) optimized vae
1120*630 33 frames * 16 steps 1470 sec         85.10 s/it * 16 = 1362 sec 
vae decode                    +117 sec                            +26 sec    
total                         1587 sec                           1388 sec  1.14x faster 
                                               
                                               This (*fp8) optimized vae
                                               76.49 s/it * 16 = 1224 sec
                                                                  +26 sec
                                                                 1282 sec  1.27x faster !!!

1568*896 13 frames * 10 steps 69.31 s/it       63.88 s/it * 10 = 638.8 sec 
                                OOM                               +115 sec
                                                                 922.8 sec

*fp8 - 3070 Ti doesn`t support calculations in fp8, loaded weights in fp8 converting for calculations to fp16 "on the fly"

Visualy hard to notice diference in quality between fp8 and fp16..

!!! The original documentation is below. This version is optimized for speed on GPUs with low VRAM. The following text is for reference only. !!!

Wan: Open and Advanced Large-Scale Video Generative Models

We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations:

👍 Effective MoE Architecture: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
👍 Cinematic-level Aesthetics: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
👍 Complex Motion Generation: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
👍 Efficient High-Definition Hybrid TI2V: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously.

Video Demos

videos_v3.mp4

🔥 Latest News!!

Aug 26, 2025: 🎵 We introduce Wan2.2-S2V-14B, an audio-driven cinematic video generation model, including inference code, model weights, and technical report! Now you can try it on wan.video, ModelScope Gradio or HuggingFace Gradio!
Jul 28, 2025: 👋 We have open a HF space using the TI2V-5B model. Enjoy!
Jul 28, 2025: 👋 Wan2.2 has been integrated into ComfyUI (CN | EN). Enjoy!
Jul 28, 2025: 👋 Wan2.2's T2V, I2V and TI2V have been integrated into Diffusers (T2V-A14B | I2V-A14B | TI2V-5B). Feel free to give it a try!
Jul 28, 2025: 👋 We've released the inference code and model weights of Wan2.2.

Community Works

If your research or project builds upon Wan2.1 or Wan2.2, and you would like more people to see it, please inform us.

DiffSynth-Studio provides comprehensive support for Wan 2.2, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training.
Kijai's ComfyUI WanVideoWrapper is an alternative implementation of Wan models for ComfyUI. Thanks to its Wan-only focus, it's on the frontline of getting cutting edge optimizations and hot research features, which are often hard to integrate into ComfyUI quickly due to its more rigid structure.

📑 Todo List

Wan2.2 Text-to-Video
- Multi-GPU Inference code of the A14B and 14B models
- Checkpoints of the A14B and 14B models
- ComfyUI integration
- Diffusers integration
Wan2.2 Image-to-Video
- Multi-GPU Inference code of the A14B model
- Checkpoints of the A14B model
- ComfyUI integration
- Diffusers integration
Wan2.2 Text-Image-to-Video
- Multi-GPU Inference code of the 5B model
- Checkpoints of the 5B model
- ComfyUI integration
- Diffusers integration
Wan2.2-S2V Speech-to-Video
- Inference code of Wan2.2-S2V
- Checkpoints of Wan2.2-S2V-14B
- ComfyUI integration
- Diffusers integration

Run Wan2.2

Installation

Clone the repo:

git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2

Install dependencies:

# Ensure torch >= 2.4.0
# If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last
pip install -r requirements.txt

Model Download

Models	Download Links	Description
T2V-A14B	🤗 Huggingface 🤖 ModelScope	Text-to-Video MoE model, supports 480P & 720P
I2V-A14B	🤗 Huggingface 🤖 ModelScope	Image-to-Video MoE model, supports 480P & 720P
TI2V-5B	🤗 Huggingface 🤖 ModelScope	High-compression VAE, T2V+I2V, supports 720P
S2V-14B	🤗 Huggingface 🤖 ModelScope	Speech-to-Video model, supports 480P & 720P

💡Note: The TI2V-5B model supports 720P video generation at 24 FPS.

Download models using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B

Download models using modelscope-cli:

pip install modelscope
modelscope download Wan-AI/Wan2.2-T2V-A14B --local_dir ./Wan2.2-T2V-A14B

Run Text-to-Video Generation

This repository supports the Wan2.2-T2V-A14B Text-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.

(1) Without Prompt Extension

To facilitate implementation, we will start with a basic version of the inference process that skips the prompt extension step.

Single-GPU inference

python generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."

💡 This command can run on a GPU with at least 80GB VRAM.

💡If you encounter OOM (Out-of-Memory) issues, you can use the --offload_model True, --convert_model_dtype and --t5_cpu options to reduce GPU memory usage.

Multi-GPU inference using FSDP + DeepSpeed Ulysses

We use PyTorch FSDP and DeepSpeed Ulysses to accelerate inference.

torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --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 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:

Use the Dashscope API for extension.
- Apply for a dashscope.api_key in advance (EN | CN).
- 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.
- 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 torchrun --nproc_per_node=8 generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --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 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:

torchrun --nproc_per_node=8 generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --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'

Run Image-to-Video Generation

This repository supports the Wan2.2-I2V-A14B Image-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.

Single-GPU inference

python generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --offload_model True --convert_model_dtype --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."

This command can run on a GPU with at least 80GB VRAM.

💡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 + DeepSpeed Ulysses

torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --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."

Image-to-Video Generation without prompt

DASH_API_KEY=your_key torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --prompt '' --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --use_prompt_extend --prompt_extend_method 'dashscope'

💡The model can generate videos solely from the input image. You can use prompt extension to generate prompt from the image.

The process of prompt extension can be referenced here.

Run Text-Image-to-Video Generation

This repository supports the Wan2.2-TI2V-5B Text-Image-to-Video model and can support video generation at 720P resolutions.

Single-GPU Text-to-Video inference

python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage"

💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is 1280*704 or 704*1280.

This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU).

💡If you are running on a GPU with at least 80GB VRAM, you can remove the --offload_model True, --convert_model_dtype and --t5_cpu options to speed up execution.

Single-GPU Image-to-Video inference

python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --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."

💡If the image parameter is configured, it is an Image-to-Video generation; otherwise, it defaults to a Text-to-Video generation.

💡Similar to Image-to-Video, 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 + DeepSpeed Ulysses

torchrun --nproc_per_node=8 generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --dit_fsdp --t5_fsdp --ulysses_size 8 --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."

The process of prompt extension can be referenced here.

Run Speech-to-Video Generation

This repository supports the Wan2.2-S2V-14B Speech-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.

Single-GPU Speech-to-Video inference

python generate.py  --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --offload_model True --convert_model_dtype --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard."  --image "examples/i2v_input.JPG" --audio "examples/talk.wav"
# Without setting --num_clip, the generated video length will automatically adjust based on the input audio length

💡 This command can run on a GPU with at least 80GB VRAM.

Multi-GPU inference using FSDP + DeepSpeed Ulysses

torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav"

Pose + Audio driven generation

torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "a person is singing" --image "examples/pose.png" --audio "examples/sing.MP3" --pose_video "./examples/pose.mp4"

💡For the Speech-to-Video task, the size parameter represents the area of the generated video, with the aspect ratio following that of the original input image.

💡The model can generate videos from audio input combined with reference image and optional text prompt.

💡The --pose_video parameter enables pose-driven generation, allowing the model to follow specific pose sequences while generating videos synchronized with audio input.

💡The --num_clip parameter controls the number of video clips generated, useful for quick preview with shorter generation time.

Computational Efficiency on Different GPUs

We test the computational efficiency of different Wan2.2 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) Multi-GPU: 14B: --ulysses_size 4/8 --dit_fsdp --t5_fsdp, 5B: --ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu; Single-GPU: 14B: --offload_model True --convert_model_dtype, 5B: --offload_model True --convert_model_dtype --t5_cpu (--convert_model_dtype converts model parameter types to config.param_dtype); (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs; (3) Tests were run without the --use_prompt_extend flag; (4) Reported results are the average of multiple samples taken after the warm-up phase.

Introduction of Wan2.2

Wan2.2 builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.

(1) Mixture-of-Experts (MoE) Architecture

Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.

The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}{moe}$ corresponding to half of the ${SNR}{min}$, and switch to the low-noise expert when $t<{t}{moe}$.

To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline Wan2.1 model does not employ the MoE architecture. Among the MoE-based variants, the Wan2.1 & High-Noise Expert reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the Wan2.1 & Low-Noise Expert uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The Wan2.2 (MoE) (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.

(2) Efficient High-Definition Hybrid TI2V

To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.

Comparisons to SOTAs

We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.

Citation

If you find our work helpful, please cite us.

@article{wan2025,
      title={Wan: Open and Advanced Large-Scale Video Generative Models}, 
      author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
      journal = {arXiv preprint arXiv:2503.20314},
      year={2025}
}

License Agreement

The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated 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.

Acknowledgements

We would like to thank the contributors to the SD3, Qwen, umt5-xxl, diffusers and HuggingFace 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 or WeChat groups!

Name		Name	Last commit message	Last commit date
Latest commit History 53 Commits
assets		assets
examples		examples
tests		tests
wan		wan
.gitignore		.gitignore
INSTALL.md		INSTALL.md
LICENSE.txt		LICENSE.txt
Makefile		Makefile
README.md		README.md
convert_safetensors.py		convert_safetensors.py
generate.py		generate.py
generate_local.py		generate_local.py
loop.bat		loop.bat
optimize_files.py		optimize_files.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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Wan2.2

How to run Wan2.2 locally on 8 GB VRAM (!!model code changed!!)

UPDATE 10/02/2025

How it works

Results on a 3070 Ti laptop GPU with 8 GB VRAM + 25 GB free RAM (some layers are loaded from the NVME drive; to fit everything in RAM, 30 GB of free RAM is needed):

Compared to ComfyUA

!!! The original documentation is below. This version is optimized for speed on GPUs with low VRAM. The following text is for reference only. !!!

Video Demos

🔥 Latest News!!

Community Works

📑 Todo List

Run Wan2.2

Installation

Model Download

Run Text-to-Video Generation

(1) Without Prompt Extension

(2) Using Prompt Extension

Run Image-to-Video Generation

Run Text-Image-to-Video Generation

Run Speech-to-Video Generation

Computational Efficiency on Different GPUs

Introduction of Wan2.2

(1) Mixture-of-Experts (MoE) Architecture

(2) Efficient High-Definition Hybrid TI2V

Comparisons to SOTAs

Citation

License Agreement

Acknowledgements

Contact Us

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Wan2.2

How to run Wan2.2 locally on 8 GB VRAM (!!model code changed!!)

UPDATE 10/02/2025

How it works

Results on a 3070 Ti laptop GPU with 8 GB VRAM + 25 GB free RAM (some layers are loaded from the NVME drive; to fit everything in RAM, 30 GB of free RAM is needed):

Compared to ComfyUA

!!! The original documentation is below. This version is optimized for speed on GPUs with low VRAM. The following text is for reference only. !!!

Video Demos

🔥 Latest News!!

Community Works

📑 Todo List

Run Wan2.2

Installation

Model Download

Run Text-to-Video Generation

(1) Without Prompt Extension

(2) Using Prompt Extension

Run Image-to-Video Generation

Run Text-Image-to-Video Generation

Run Speech-to-Video Generation

Computational Efficiency on Different GPUs

Introduction of Wan2.2

(1) Mixture-of-Experts (MoE) Architecture

(2) Efficient High-Definition Hybrid TI2V

Comparisons to SOTAs

Citation

License Agreement

Acknowledgements

Contact Us

About

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License

Uh oh!

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