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Nested Diffusion Processes for Anytime Image Generation

Noam Elata

Supervised by Bahjat Kawar & Michael Elad


In this work, we propose an anytime diffusion-based method
that can generate viable images when stopped at
arbitrary times before completion. Using existing
pretrained diffusion models, we show that the generation
scheme can be recomposed as two nested
diffusion processes, enabling fast iterative refinement
of a generated image. We use this Nested
Diffusion approach to peek into the generation
process and enable flexible scheduling based on
the instantaneous preference of the user. In experiments
on ImageNet and Stable Diffusion-based
text-to-image generation, we show, both qualitatively
and quantitatively, that our method’s intermediate
generation quality greatly exceeds that of
the original diffusion model, while the final slow
generation result remains comparable

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