lllyasviel / lllyasviel/ControlNet
how to use DDIM inversion during inference?
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Description
Hi, thanks for releasing this amazing project. From my best understanding, current demo code does not use DDIM inversion during inference as the x_T variable is None in the following line? If so, could you please provide some suggestions on how to implement DDIM inversion to better use some input images as prior? Thanks!
https://github.com/lllyasviel/ControlNet/blob/3d25e488c7e6c2efdfe94f1673f2de261c662902/cldm/ddim_hacked.py#L132-L133
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start with cldm/ddim_hacked.py at lines 132-133 and trace how the demo handles x_T during inference. Compare that path with the requested DDIM inversion workflow for using input images as a prior. Done means the inference behavior or implementation guidance clearly addresses whether and how inversion is supported.
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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