lllyasviel / lllyasviel/LayerDiffuse_DiffusersCLI
replicated paper and training code but..
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- Dominant language
- Python
- Stars
- 425
- Forks
- 40
- PR merge metrics
- No merged PRs in 30d
Description
hi @lllyasviel amazing work, i tried replicating the paper, and was able to get a model working, but the results i see are cloudy and have artifacts sometimes, lack consistency across seeds. did you regularize using any additional losses other than regular diffusion objective in latent space while training the Unet?
did you use any additional tricks not mentioned in paper?
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Research direction
Compare the paper with the referenced training code, focusing on the latent-space diffusion objective, U-Net training, and any regularization or additional losses. Determine whether the cloudy artifacts and seed inconsistency come from an undocumented training detail or a replication problem; the issue is resolved when the expected training procedure is clarified.
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Assessment
- Tech stack
- machine-learning, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100