Model reference · open weights
Wan2.1-T2V is an open-weight video model from Wan-AI. Wan2.1-T2V-14B-Diffusers (FP32) weighs 40.2 GB; the smallest configuration that runs it is RTX 5090 32 GB.
Summary of the Wan-AI/Wan2.1-T2V-1.3B-Diffusers model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | Wan-AI |
|---|---|
| Released | 2025-03-01 |
| Parameters | 1.4B |
| VRAM | 40.2 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 4090 24 GB | does not fit | |
| RTX 5090 32 GB | tight (encoders offloaded) | 31.0 GB |
| L40S 48 GB | tight | 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 |
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| Wan2.1-T2V-1.3B-Diffusers ↗ | 1.4B | FP32 | 14.5 GB | RTX 4060 Ti 16 GB |
| Wan2.1-T2V-14B-Diffusers (above) ↗ | 14.3B | FP32 | 40.2 GB | RTX 5090 32 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 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.
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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-T2V-1.3B-Diffusers --local-dir ./Wan2.1-T2V-1.3B-Diffusers
Download models using 🤖 modelscope-cli:
pip install modelscope
modelscope download Wan-AI/Wan2.1-T2V-1.3B-Diffusers --local_dir ./Wan2.1-T2V-1.3B-Diffusers
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:
To facilitate implementation, we will start with a basic version of the inference process that skips the prompt extension step.
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."
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-1.3Bmodel, we recommend setting the parameter--sample_guide_scale 6. The--sample_shift parametercan be adjusted within the range of 8 to 12 based on the performance.
pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --tasQuoted 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 Text 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-T2V-1.3B-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 Text to Video” (9.2 GB)
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/diffusion_models/wan2.1_t2v_1.3B_fp16.safetensors diffusion_models
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