Comments (1)
lchunleo
commented on September 26, 2024
@CooperLuo32 thanks for sharing. may i check for the input to the model , is it random noises as defined by "Image.fromarray((current_image_tensor.cpu().permute(1, 2, 0).numpy() * 255).astype('uint8'))"
also is it a need to have the Trainer class to load the checkpoints? As i don't wish to train further but to use the checkpoint to perform inference
for "sampled_images = diffusion.sample(batch_size=8)", may i know what does the sample refers to ?
from denoising-diffusion-pytorch.
Related Issues (20)
- change of beta_schedule leads to significantly worse results
- Loss on Unet1D
- scale up UNet with different resolution
- Why 1D diffusion is so extremely slow?? HOT 2
- RePaint Improvements HOT 1
- Bug in RePaint implementation: p_sample input args and resample loop HOT 1
- A question related to batch size and training speed HOT 2
- The ./results folder is empty
- training on CIFAR-10 performs not well, whether L2 loss or FID
- denoising_diffusion_pytorch.py doesn't use eval mode for testing HOT 4
- Enable flash attention for compute capability >= 8.0, not == 8.0 HOT 6
- bug of UNet final step dim HOT 1
- Sanity Check - Looking for a basic CIFAR10 hyperparameter set HOT 6
- sampling loop time step HOT 2
- RMSNorm Implementation Question HOT 1
- How to do image conditioning
- Feature Request: ResSHIFT HOT 3
- KarrasUnet1D fails in the decoder for some sequence length/num. channel combinations HOT 2
- how to load the pretrained model of this code? HOT 1
- How can I choose my preferred GPU? HOT 1
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from denoising-diffusion-pytorch.