lllyasviel / lllyasviel/ControlNet
Generated Images are not like the input one
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Description
Dear great authors,
Currently I've tried to use canny info as the input of ControlNet.
My used case is a child portrait. As the code described, the input will generate edge info and combined with prompts to de-noise sequentially.
Finally, it can generate a child based on the scribble and edge info. However, the generated child does not look like the input child
Is it possible the results can be more similar to the input image? That is, preserve the identity characters? Thanks
PS: I've tried to alter guidance-scale/strength already
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First steps
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Research direction
The issue names no files, tests, or entry points; start by reproducing the child-portrait example with Canny input and the reported guidance-scale and strength settings. Done would require an agreed way to measure identity preservation and results that are more similar to the input, but the issue does not define either.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100