Comments (2)
@Lizhe1228 for each tilmestep for a given image, I have saved intermediate feature representations from the diffusion model decoder as a .mat
file. Then wrote a simple code which takes the saved features one by one and normalized it to 0 - 1
scale, then used jet
colormap to get the final output.
from ddpm-cd.
@Lizhe1228 for each tilmestep for a given image, I have saved intermediate feature representations from the diffusion model decoder as a
.mat
file. Then wrote a simple code which takes the saved features one by one and normalized it to0 - 1
scale, then usedjet
colormap to get the final output.
Thank you very much for your assistance. I just found this part in the code. Finally, I wish you smooth work and successful research.
from ddpm-cd.
Related Issues (20)
- about the meaning of the feature images' names HOT 3
- lightweight head HOT 2
- about the theory HOT 3
- Can this method be extended to multi-band data HOT 1
- About the training time and training data set size of diffusion model HOT 1
- About Multi-gpus parallel training. HOT 1
- About can't load the trained model. HOT 2
- has a wrong answer
- Training diffusion model with remote sensing data HOT 2
- Data for pretraining the diffusion model HOT 1
- question for 'GaussianDiffusion'
- About the expansion of the pre-training dataset HOT 2
- Question for typo in paper HOT 1
- Prediction Images Are All Black HOT 3
- Practicality of the model on 64x64 images HOT 7
- TypeError: unsupported operand type(s) for %: 'int' and 'NoneType' HOT 1
- When loading your pretraining file, errors may occur in the following model file sections due to structural mismatch between your pretraining model directly under the link and the ddpm model under ddpm_cd. HOT 4
- Broken wandb link HOT 1
- Train/Val Reports on wandb can not be opened HOT 4
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