Comments (2)
Ah it seems that actually one difference was that the scheduler for stable-diffusion 2.1 uses a prediction type of v_prediction
in normal generation but uses a prediction type of epsilon
for inpainting (whereas stable-diffusion 1.5 uses epsilon
for both). I've just merged a PR #63 that should hopefully help with this - at least on a single generated image it looks better now.
There is still a scheduler inconsistency as we use DDIM rather than PNDM - also using a proper DPM solver would likely help here but hopefully this doesn't make much of a difference.
Please give a spin to the current github tip if you can and let us know how it gets.
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No clue what is going on here, I also tried it and got the same results using the weights from stabilityai/stable-diffusion-2-inpainting. I also tried the native resolution of 768x768 without luck. Spotting the json config files for the different versions, I haven't noticed anything that would obviously require some adaptation. I guess at this point the simpler would likely be to run the inpainting process on the Python and Rust side and see at which layer things start to diverge.
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Related Issues (20)
- Feature Request: Negative prompts HOT 1
- Add Scheduler trait/enum HOT 2
- m1 mac gpu HOT 6
- Google Colab Notebook to run diffusion experiment on the GPU
- Embed the examples logic into the pipeline HOT 1
- How to load a parameter file in safetensors format? HOT 1
- PytorchStreamReader failed reading zip archive HOT 2
- ControlNet support? HOT 5
- Bad distorted picture using the in-painting example provided HOT 4
- Loading of text embeddings in pt format? HOT 2
- CUDA out of memory on 12GB GPU HOT 2
- Error: The system cannot find the file specified. (os error 2) HOT 2
- Tracking issue for SD ecosystem feature parity HOT 6
- DirectML Support HOT 1
- Cannot link when used together with cxx-qt crate HOT 1
- CUDA/GPU Not Working. HOT 1
- STATUS_DLL_NOT_FOUND HOT 1
- Benchmarks? HOT 1
- Integration with Stable Diffusion XL 1.0 ? HOT 1
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