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AsyncPatch Diffusion enables spatially flexible image generation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed AsyncPatch Diffusion, a novel framework for image generation that allows different spatial regions of an image to be denoised on distinct schedules. This approach enables more flexible and spatially adaptive generation compared to standard diffusion models. The method achieves competitive generation quality on benchmarks like ImageNet and LSUN, and is particularly effective for inpainting tasks without requiring task-specific fine-tuning. AI

IMPACT Introduces a new diffusion model technique that improves inpainting and spatially adaptive generation capabilities.

RANK_REASON The cluster contains an academic paper detailing a new method for image generation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AsyncPatch Diffusion enables spatially flexible image generation

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The cluster contains an academic paper detailing a new method for image generation.
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2 independent sources
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paper, model release
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118 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Samuele Papa, Valentin De Bortoli, Guillaume Couairon, Daniel S\'ykora, Romuald Elie, Klaus Greff ·

    AsyncPatch Diffusion: spatially-flexible image generation

    arXiv:2606.07079v1 Announce Type: new Abstract: Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Diffusion, a joint-diffusion framework that assigns di…

  2. arXiv cs.CV TIER_1 English(EN) · Klaus Greff ·

    AsyncPatch Diffusion: spatially-flexible image generation

    Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Diffusion, a joint-diffusion framework that assigns distinct noise levels to different input dimension…

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