Comments (3)
I have the same question. I think it should work with Colorization, since when I look at the code, I do not think it forces a grayscale conversion. It takes the RGB values as input of the gray images as input, so if they are not gray, they should be colored as input.
However, it does not work very well for me either. I get very green images as output, it's very odd...
from palette-image-to-image-diffusion-models.
Working on the same topic now. @cristianpjensen are you getting results after 4 months?
from palette-image-to-image-diffusion-models.
Same here, any updates?
from palette-image-to-image-diffusion-models.
Related Issues (20)
- Palette [Palette() form models.mods] not recognized. HOT 1
- use specific mask HOT 3
- what's the valid mask HOT 5
- Question about encode the gama rather than t HOT 2
- pth2onnx,How should I use βtorch.onnx.export()β HOT 1
- Image-to-image translation with mostly black images HOT 3
- How can I add classifier guidance while doing the uncropping task?
- Broken pipeline error while training on multiple gpu
- use this project for image restoration
- How can I adapt the colorization model to work with different image resolutions?
- Training loss growing up
- why p_mean_variance use noise_level instead of sample_gammas like in training for time conditon of denoise function. HOT 3
- There was no result at the time of the test
- segmentation fault HOT 1
- Some of the results are full of noise. HOT 2
- test noise schedule and train noise schedule are different?
- Whether to use a lr scheduler when training from the scratch? HOT 1
- I'm fused by the output and target noise.
- [Uncropping]How to generate panoramas like Firgure 2?
- Error During Colorization Training
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from palette-image-to-image-diffusion-models.