Comments (7)
I think that adding the conditional input is quite easy.
You could have a look to section 5 of this paper:
https://arxiv.org/pdf/2102.09672.pdf
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@abdalazizrashid This was transcribed from the original TensorFlow implementation to Pytorch and it does not support conditional generation.
Take a look at the this paper, they introduced a condition encoding network which you can implement, based on the code here by adding a few modifications to some functions as well as the loss to accomodate for this new condition.
Hope it helps.
from denoising-diffusion-pytorch.
Class-conditional generation can be found in https://github.com/openai/improved-diffusion.
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If anyone tries this out, would be great if you could link to some code or provide a MWE. Or if anyone is aware of other implementations
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Has anyone been able to apply this code for tasks like inpainting or super-resolution? They are both forms of conditional generation, and it's not clear to me how one should go about doing it.
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See also #21 (comment)
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I'm curious if there are any updates on this topic. I've been reviewing the SR3 and Palette papers, and I plan to test an approach of concatenating the source image with
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Related Issues (20)
- what changes we need for grey scale images?
- When should the model be saved, and how should it be implemented?
- Self condition inference mode HOT 1
- How to load EMA Model Weight
- Size of dataset
- Adapt code to support RePaint HOT 9
- when do we use objective "pred_x_start"? HOT 3
- learned_gaussian_diffusion issue HOT 2
- Wrong normalisation dimension HOT 13
- "transcribed from official implementation" -> "Inspired by official implementation" in README.md HOT 3
- Karras UNet 1D + 3D HOT 30
- problem HOT 1
- did anyone use this library in conjunction with wav2vec? HOT 1
- Training on Celeba-hq HOT 4
- Unable to train HOT 1
- Failed to load image Python extension: '[WinError 127] 找不到指定的程序 HOT 1
- Any implements on classify free guidance?
- How could load gpu to train?
- Question about the normalization of the input data for ddpm.
- Question about how to use elucidated_diffusion HOT 1
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