Comments (1)
train_num_steps is the number of times the gradient is computed (for every train_batch_size * gradient_accumulate_every samples in your dataset) until the model stops training
timesteps is "the one from the paper"
from denoising-diffusion-pytorch.
Related Issues (20)
- "transcribed from official implementation" -> "Inspired by official implementation" in README.md HOT 3
- Karras UNet 1D + 3D HOT 30
- did anyone use this library in conjunction with wav2vec? HOT 1
- Training on Celeba-hq HOT 5
- 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
- Fast attention in Windows possible?
- No available kernel HOT 1
- 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
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from denoising-diffusion-pytorch.