A Consistency Control Approach with a Multi-Role Perspective
To achieve character feature harmonization across images, the following techniques need to be combined:
- Prompt Engineering: Add detailed descriptions of features (e.g., hair color/clothing style) to prompts, with BREAK separators to emphasize key features
- Seed lock: Fixing the random seed (Seed parameter) in LanPaint KSampler ensures that the generation style is stable
- feature extraction: Extract character traits with Textual Inversion or LoRA first, then enter them as additional conditions
- hierarchical treatment: Create separate masks for different parts of the garment/face, etc., and use the same parameters to repair them step by step.
Workflow Example1) Create a base character image 2) Generate a multi-angle skeleton image with OpenPose 3) Set the same mask area for each angle image 4) Add a description like "same character in different poses" in the prompt 5) Run batch processing. It is recommended to use a low Step value to quickly verify the consistency of the test phase, and then adjusted to 30-50 steps after satisfaction with the fine generation.
This answer comes from the articleLanPaint: A ComfyUI Image Restoration Workflow for Any ModelThe
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