A Practical Guide to Cue Word Optimization
A three-layer optimization strategy is recommended for the content bias problem:
- Structured description method: Write prompts based on a "subject-action-environment-style" structure, e.g., "Golden Retriever (subject) running on the beach (action), sunset (environment), 8k film texture (style)."
- GPT-4o Enhanced Mode: Enable the -refine-prompt True parameter, the system will automatically expand simple prompts into long prompts containing specialized terminology such as camera angles, lighting, and so on.
- Negative cueing techniques: Add unprompted.txt to the configuration file to define excluded elements, e.g. "blurry, distorted face, extra limbs".
- Staged validation:: Mr. Mister into a 2-second preview video (-num-frames 17) to confirm the direction before generating the full version
- Reference Chart Locked: image to video mode, use -ref-strength 0.7 to preserve the original composition, the higher the value the more features of the reference image are preserved
This answer comes from the articleOpen Sora: An Open Source Video Generation Tool for Optimizing Face ConsistencyThe
May not be reproduced without permission:AI productivity tools " How to avoid Open-Sora generating content that doesn't match the prompt words?
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