Model reference · open weights
Wan2.1-I2V-480P is an open-weight video model from Wan-AI. Wan2.1-I2V-14B-480P-Diffusers (FP32) weighs 45.0 GB; the smallest configuration that runs it is L40S 48 GB.
Summary of the Wan-AI/Wan2.1-I2V-14B-480P-Diffusers model card, 2026-10-01
What it is
| Released by | Wan-AI |
|---|---|
| Released | 2025-03-01 |
| Parameters | 16.4B |
| VRAM | 45.0 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 5090 32 GB | does not fit | |
| L40S 48 GB | fits (encoders offloaded) | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face   |   🤖 ModelScope   |    📑 Paper (Coming soon)    |    📑 Blog    |   💬 WeChat Group   |    📖 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:
This repo contains our I2V-14B model, which is capable of generating 480P videos, offering advantages in terms of fast generation and excellent quality.
Your browser does not support the video tag.
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
| Models | Download Link | Notes |
|---|---|---|
| T2V-14B | 🤗 Huggingface 🤖 ModelScope | Supports both 480P and 720P |
| I2V-14B-720P | 🤗 Huggingface 🤖 ModelScope | Supports 720P |
| I2V-14B-480P | 🤗 Huggingface 🤖 ModelScope | Supports 480P |
| T2V-1.3B | 🤗 Huggingface 🤖 ModelScope | 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-480P-Diffusers --local-dir ./Wan2.1-I2V-14B-480P-Diffusers
Download models using 🤖 modelscope-cli:
pip install modelscope
modelscope download Wan-AI/Wan2.1-I2V-14B-480P-Diffusers --local_dir ./Wan2.1-I2V-14B-480P-Diffusers
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:
python generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --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
sizeparameter represents the area of the generated video, with the aspect ratio following that of the original input image.
pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --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 highligQuoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel, then Templates → Wan 2.1 Image to Video: it loads ComfyUI's own build of this model, the files above.
# on your rented machine: pip install diffusers transformers accelerate ftfy
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", torch_dtype=torch.bfloat16).to("cuda")
frames = pipe(prompt="a drone shot over a forest at sunrise").frames[0]
export_to_video(frames, "/workspace/out.mp4", fps=16)# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the files of the template “Wan 2.1 Image to Video” (38.2 GB)
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/diffusion_models/wan2.1_i2v_480p_14B_fp16.safetensors diffusion_models
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors text_encoders
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/vae/wan_2.1_vae.safetensors vae
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/clip_vision/clip_vision_h.safetensors clip_vision
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT