---

name: mimicmotion description: "Routes MimicMotion's CUDA-first local inference and Cog deployment workflows, including runtime preflight checks, sample-config generation, and MP4 output." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MimicMotion

MimicMotion generates human-motion videos from a reference image and a motion video. Use this skill when the task mentions MimicMotion, Stable Video Diffusion, DWPose, a sample config, checkpoint switching, or MP4 generation.

Start here

1. Read references/environment.md if the environment is not already known to be CUDA-ready.
2. Read references/configuration.md for the required weights layout and the canonical sample config.
3. Read references/workflows.md to choose between local inference and Cog deployment.
4. Read references/troubleshooting.md if imports, weights, CUDA, or video writing fail.
5. Read references/repo-provenance.md when checking whether this skill matches the current checkout.

Install the runtime

Use a Python 3.11 CUDA environment. The verified stack was:

• torch==2.5.1+cu124
• torchvision==0.20.1+cu124
• diffusers==0.27.0
• transformers==4.32.1
• huggingface-hub==0.20.2
• decord==0.6.0
• onnxruntime-gpu==1.29.0
• einops==0.8.0
• omegaconf==2.3.0
• opencv-python==4.10.0.84
• matplotlib==3.9.1
• av==12.2.0
• cog==0.22.0 when using the deployment path
• ffmpeg available on PATH

If diffusers import errors mention cached_download, pin huggingface-hub==0.20.2. If decord is only available as a failing PyPI wheel on your platform, use the conda-forge build documented in references/environment.md.

Main workflows

Workflow	When to use	Read / run
Local inference	You have a MimicMotion checkout, the CUDA stack is ready, and you want to generate a video from the sample config or a custom config.	Read references/workflows.md, then run scripts/check_runtime.py and scripts/run_inference.py.
Cog deployment	You want to understand the packaged predictor surface, input validation, or weight-fetch behavior.	Read references/workflows.md and references/configuration.md.
Runtime preflight	You want to confirm imports, CUDA providers, ffmpeg, and the local package surface before generating a video.	Run scripts/check_runtime.py --repo-root <checkout>.

Scripts

• scripts/check_runtime.py — run this first. It checks the imported package stack, CUDA availability, ONNXRuntime CUDA providers, ffmpeg, and optionally the local model files.
• scripts/run_inference.py — run this after preflight when you want the bundled local-inference wrapper that mirrors the repository CLI.

What to expect from the model

• The default sample config uses assets/example_data/videos/pose1.mp4 and assets/example_data/images/demo1.jpg.
• The canonical checkpoint is models/MimicMotion_1-1.pth.
• The local inference flow writes one MP4 per test case.
• The Cog predictor supports checkpoint switching between v1 and v1-1 and validates numeric bounds before generation.

Safety and capability notes

• Treat CUDA as required for the main workflow. A CPU-only environment is not a verified substitute for MimicMotion generation.
• Use scripts/check_runtime.py --skip-models only when you need to confirm the Python/CUDA stack before the weights are available.
• Do not assume the environment is ready just because imports succeed; the pipeline also needs the model files and ffmpeg.

Related references

• references/api-reference.md for verified signatures and output behavior.
• references/environment.md for the installation shape and backend checks.
• references/configuration.md for weights, config, and asset layout.
• references/troubleshooting.md for predictable import, backend, and video-writing failures.
• references/repo-routing-metadata.json for managed skill routing metadata.
• references/repo-provenance.md for the checkout snapshot that this skill matches.