Comments (2)
You can do that by putting the extra loss terms in the cond_fn: https://github.com/crowsonkb/k-diffusion/blob/master/sample_clip_guided.py#L101 :)
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Omg yes, thank you so much!
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Related Issues (20)
- Is there any plan to release pretrained HDiT models?
- Confirm JSON config for FFHQ-1024? HOT 1
- How to inference the trained model?
- bad sampler name: DPM++ 2M Karras
- Patch size 2 for HDiT HOT 1
- Blocking ops in several samplers, usually from sigmas being a GPU tensor
- Use of DCT in loss function?
- k-diffusion triggering of torch.compile/torch.multiprocessing leaves multiple child processes HOT 2
- Stabilizing DPM++2M SDE for SDXL HOT 1
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- UnboundLocalError: local variable 'h' referenced before assignment
- How to cite this? HOT 1
- `--mixed-precision` doesnt work with img transformer 2 HOT 2
- Pretrained models of Hourglass Diffusion Transformers
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- Consider Making Normalization Optional in MappingFeedForwardBlock HOT 2
- Scaling from 128x128, to 256x256, 512x512 and 1024x1024? HOT 4
- R2 setup in the paper HOT 4
- Questions about soft-min-snr loss HOT 1
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