---

name: image-to-video description: "Route I2VGen-XL image-to-video inference, local predictor/demo references, input lists, checkpoints, person variant overrides, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

image-to-video

Use this sub-skill when the task is about VGen's I2VGen-XL image-to-video path: one input image plus one text caption produces a short video. This includes local config-driven inference, the image|||caption list format, I2VGen-XL checkpoint selection, the person-specialized config variant, and optional local demo/deployment wrappers.

Do not use this sub-skill for text-to-video, DreamVideo subject/motion customization, InstructVideo reward fine-tuning/inference, or generic VideoComposer/HiGen/VideoLCM routing.

Route quickly

1. Confirm the user wants I2VGen-XL image-to-video, not a text-only or customization workflow.

2. Check that CUDA inference is available. The verified drafting environment had CUDA PyTorch plus xformers, open-clip-torch, fairscale, diffusers, transformers, piq, scikit-image, and an OpenCV-compatible NumPy stack; CPU-only execution is not a valid proof for this workflow.

3. Validate the input list before spending GPU time:

   python sub-skills/image-to-video/scripts/check_i2vgen_inputs.py data/test_list_for_i2vgen.txt --check-exists --root /path/to/VGen
   

4. Prefer the bundled local launcher for reproducible repository work. Point --repo-root at a checkout that contains inference.py:

   python sub-skills/image-to-video/scripts/run_i2vgen_inference.py --repo-root /path/to/VGen --dry-run --cfg configs/i2vgen_xl_infer.yaml -- \
     test_list_path data/test_list_for_i2vgen.txt \
     test_model models/i2vgen_xl_00854500.pth
   

5. Use configs/i2vgen_xl_infer_person.yaml only when the user has the person-specialized checkpoint and matching person input list/caption assets, or when they explicitly want to adapt that variant.

6. Treat predict.py and gradio_app.py as reference-only/demo surfaces unless the user explicitly asks for Cog/Replicate or ModelScope/Gradio deployment and accepts network-backed demo dependencies.

Bundled references

• references/workflows.md explains the config variants, list schema, local command shape, checkpoint expectations, person overrides, and optional demo wrappers.
• references/troubleshooting.md covers missing models, malformed lists, CUDA/distributed failures, demo dependency problems, save/playback issues, and known content limitations.
• scripts/check_i2vgen_inputs.py validates I2VGen-XL image-plus-caption list files and can optionally check that referenced images exist.

Operating guardrails

• Keep model downloads, ModelScope caches, Cog deployment setup, and Gradio installs out of the required local inference path unless the user asks for them.
• Do not silently switch to T2V, DreamVideo, or InstructVideo configs when an I2VGen-XL checkpoint or list file is missing; report the missing I2VGen asset and request or locate the correct replacement.
• For quick smoke runs, reduce round to 1 and keep only one active non-comment line in the list. The repository demo list recommends one data point at a time.
• Keep local environment paths and private cache locations out of handoffs and user-facing instructions.