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Relay Diffusion: Unifying diffusion process across resolutions for image synthesis

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arxiv 2309.03350 v1 pith:N2U2YQGY submitted 2023-09-04 cs.CV cs.LG

Jiayan Teng , Wendi Zheng , Ming Ding , Wenyi Hong , Jianqiao Wangni , Zhuoyi Yang , Jie Tang This is my paper
classification cs.CVcs.LG
keywords diffusionnoiseimagemodelhigh-resolutionhigherlow-resolutionprocess
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abstract

Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the main reason is that \emph{the same noise level on a higher resolution results in a higher Signal-to-Noise Ratio in the frequency domain}. In this work, we present Relay Diffusion Model (RDM), which transfers a low-resolution image or noise into an equivalent high-resolution one for diffusion model via blurring diffusion and block noise. Therefore, the diffusion process can continue seamlessly in any new resolution or model without restarting from pure noise or low-resolution conditioning. RDM achieves state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256, surpassing previous works such as ADM, LDM and DiT by a large margin. All the codes and checkpoints are open-sourced at \url{https://github.com/THUDM/RelayDiffusion}.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Phase-Aligned RoPE for Mixed-Resolution Diffusion Transformer

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Expressing all RoPE positions on the query's grid ('one attention, one scale') plus a small boundary content-exchange step restores mixed-resolution diffusion generation that naive position interpolation destroys.

  2. CineScale: Free Lunch in High-Resolution Cinematic Visual Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    CineScale extends pre-trained diffusion models to 8k image and 4k video generation with mostly tuning-free inference plus a small LoRA adaptation for video.

  3. PixNerd: Pixel Neural Field Diffusion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    PixNerd is a single-stage pixel-space diffusion transformer that uses predicted neural field weights to decode large patches, reaching 2.15 FID on ImageNet 256 without a VAE.

  4. UltraImageGen: Efficient Ultra-High-Resolution Image Generation with Hierarchical Local Attention

    cs.CV 2025-10 conditional novelty 5.0 of 10

    A pretrained FLUX diffusion model is adapted with local-window attention plus low-resolution global guidance, allowing 4K text-to-image generation from 1K-only training data at about 2x lower cost.

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