lllyasviel / lllyasviel/ControlNet
[REQUEST] Custom trained model to support cryptomatte and depth pass
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- Dominant language
- Python
- Stars
- 34.1k
- Forks
- 3k
- PR merge metrics
- No merged PRs in 30d
Description
It would be great if we could use cryptomatte and depth passes generated from a rendering engine i.e. blender, and to use their combined information to inform the final "rendering" via controlnet.
This would be somewhat similar to a combination of depth and segmentation maps as they are currently implemented.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing how the project currently handles depth and segmentation maps, then compare that flow with cryptomatte and depth passes generated by Blender. Define how the combined information should condition ControlNet and what output would demonstrate that custom-trained support is working.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- blender, python
- Domain
- computer-graphics, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100