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Diffusion Models

Created on September 13|Last edited on September 13
My implementation of Denoising Diffusion Probabilistic Models. Some notes:
  • My first attempts producing images at 256 x 256 weren't very successful, however lowering the resolution to 128 x 128 produced good results out of the box with the settings from the original paper.
  • While the paper doesn't report FID scores (at any resolution), they don't seem extremely competitive, nor do they continue going down with more training iterations.
  • The sample quality becomes okay after 50K steps (with 8 GPUs and batch size 16) and good to really good after 100K steps. The EMA model takes a bit longer to converge to high quality images, but suffers less from issues where all the sampled results tend to be biased towards a particular color.
  • Clipping x0x_0 to [-1, 1] during sampling was important to obtain high quality samples. This phenomena is discussed into more detailed in the Imagen paper.

Samples


Run: atomic-field-49
1



Run: atomic-field-49
1