Poster
∞-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite Dimensions
Minh Quan Le · Alexandros Graikos · Srikar Yellapragada · Rajarsi Gupta · Joel Saltz · Dimitris Samaras
# 173
Strong Double Blind |
Wed 2 Oct 1:30 a.m. PDT
— 3:30 a.m. PDT
Abstract:
Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image domains such as digital histopathology and remote sensing. Existing methods face critical limitations: conditional diffusion models in pixel or latent space cannot exceed the resolution on which they were trained without losing fidelity, and computational demands increase significantly for larger image sizes. Patch-based methods offer computational efficiency but fail to capture long-range spatial relationships due to their overreliance on local information. In this paper, we introduce a novel conditional diffusion model in infinite dimensions, \texttt{-Brush} for controllable large image synthesis. We propose a cross-attention neural operator to enable conditioning in function space. Our model overcomes the constraints of traditional finite-dimensional diffusion models and patch-based methods, offering scalability and superior capability in preserving global image structures while maintaining fine details. To the best of our knowledge, \texttt{-Brush} is the first conditional diffusion model in function space, that can controllably synthesize images at arbitrary resolutions of up to pixels. The code will be released to the public.
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