Model card
Wan2.1 T2V 1.3B is a compact, high-efficiency text-to-video model designed for developers who need a balance between generation quality and computational overhead. Unlike massive proprietary models, this 1.3B parameter version is optimized for faster inference and lower VRAM requirements, making it viable for local deployment or integration into agile pipelines. It excels at translating descriptive prompts into coherent motion, serving as a strong foundation for short-form content generation, prototyping visual assets, or building custom video-gen wrappers. Licensed under Apache-2.0, it offers the flexibility needed for commercial scaling without restrictive licensing hurdles. For devs, this means a streamlined path from prompt to render with a model that is lightweight enough to iterate on rapidly.
Model files and versions
Download this model
We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
Wan-AI/Wan2.1-T2V-1.3BInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model Wan-AI/Wan2.1-T2V-1.3BREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model Wan-AI/Wan2.1-T2V-1.3B README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('Wan-AI/Wan2.1-T2V-1.3B')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/Wan-AI/Wan2.1-T2V-1.3B.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Wan-AI/Wan2.1-T2V-1.3B.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
Discussions
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