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name: dreamvideo description: "Route DreamVideo subject, motion, and joint customization workflows, plus DreamVideo metric calculation." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

dreamvideo

Use this sub-skill when a VGen task is about DreamVideo subject personalization, motion personalization, or joint subject-plus-motion composition. It covers the training and inference configs, the adapter checkpoints they produce, and the DreamVideo metric helper.

Do not use this route for I2VGen image-to-video, InstructVideo reward fine-tuning, or the generic text-to-video families unless the task is specifically comparing them to DreamVideo.

Fast route

1. Decide which DreamVideo stage the user wants:
   • Subject learning: learn an identity/appearance adapter from configs/dreamvideo/subjectLearning/*.yaml.
   • Motion learning: learn a motion adapter from configs/dreamvideo/motionLearning/*.yaml.
   • Joint inference: load both subject and motion configs with configs/dreamvideo/infer/joint_*.yaml.
2. Read the config pair first. DreamVideo inference uses subject_cfg and/or motion_cfg to layer in adapter settings before the main inference config is merged.
3. Check whether the user has the adapter checkpoints the config expects:
   • identity_adapter_index or identity_adapter_path
   • motion_adapter_index or motion_adapter_path
4. For customization outputs, confirm the custom list rows are in the DreamVideo image|||prompt format and that test_data_dir points at the matching image directory.
5. For evaluation, use scripts/calc_metrics.py with explicit CLIP and DINO assets. The metric helper is not usable without those extra files.

What this sub-skill covers

• DreamVideo subject learning and motion learning configs.
• DreamVideo inference configs for subject-only, motion-only, and joint runs.
• Adapter-merging logic, use_textInversion, appearance_guide_strength_*, and inverse_noise_strength.
• DreamVideo custom list files under data/custom/infer/ and training examples under data/custom/train/.
• DreamVideo metric calculation for CLIP-T, CLIP-I, DINO-I, and Temporal Consistency.

References

• Detailed workflow map, config layering, list formats, and adapter-selection notes: references/workflows.md.
• Failure modes and fixes for adapter paths, placeholder checkpoints, metric dependencies, and memory issues: references/troubleshooting.md.
• Bundled helpers:
  • scripts/dump_adapter_keys.py
  • scripts/calc_metrics.py

Handoff notes for root integration

DreamVideo is the customization route that most clearly needs a reusable adapter-key helper and an explicit metric wrapper. Keep those helpers self-contained and do not depend on the original test_func/ or metric/ files at runtime.