Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration

• URL: http://arxiv.org/abs/2512.10954v1
• Date: Thu, 11 Dec 2025 18:59:55 GMT
• Title: Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration
• Authors: Sicheng Mo, Thao Nguyen, Richard Zhang, Nick Kolkin, Siddharth Srinivasan Iyer, Eli Shechtman, Krishna Kumar Singh, Yong Jae Lee, Bolei Zhou, Yuheng Li,
• Abstract summary: We propose Group Diffusion, unlocking the attention mechanism to be shared across images.<n>We observe a clear scaling effect - larger group sizes yield stronger cross-sample attention and better generation quality.<n>Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.
• Score: 88.94434023253872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be generated collaboratively. We propose Group Diffusion, unlocking the attention mechanism to be shared across images, rather than limited to just the patches within an image. This enables images to be jointly denoised at inference time, learning both intra and inter-image correspondence. We observe a clear scaling effect - larger group sizes yield stronger cross-sample attention and better generation quality. Furthermore, we introduce a qualitative measure to capture this behavior and show that its strength closely correlates with FID. Built on standard diffusion transformers, our GroupDiff achieves up to 32.2% FID improvement on ImageNet-256x256. Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.

Related papers

• Detecting Generated Images by Fitting Natural Image Distributions [75.31113784234877]
  We propose a novel framework that exploits geometric differences between the data manifold of natural and generated images.<n>We employ a pair of functions engineered to yield consistent outputs for natural images but divergent outputs for generated ones.<n>An image is identified as generated if a transformation along its data manifold induces a significant change in the loss value of a self-supervised model pre-trained on natural images.
  arXiv  Detail & Related papers  (2025-11-03T07:20:38Z)
• Context-guided Responsible Data Augmentation with Diffusion Models [29.41191005466334]
  We propose a text-to-image (T2I) data augmentation method, named DiffCoRe-Mix, that computes a set of generative counterparts for a training sample.<n>To preserve key semantic axes, we also filter out undesired generative samples in our augmentation process.<n>We extensively evaluate our technique on ImageNet-1K,Tiny ImageNet-200, CIFAR-100, Flowers102, CUB-Birds, Stanford Cars, and Caltech datasets.
  arXiv  Detail & Related papers  (2025-03-12T00:12:27Z)
• Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas [33.334956022229846]
  We propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting. Specifically, we merge the diffusion paths, reprogramming self- and cross-attention to operate on the aggregated latent space. Our method maintains compatibility with the input prompt and visual quality of the generated images while increasing their semantic coherence.
  arXiv  Detail & Related papers  (2024-08-28T09:22:32Z)
• Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination [13.238373528922194]
  We propose a novel technique for post-processing Consistency-based generated images, enhancing their perceptual quality.<n>Our approach utilizes a joint classifier-discriminator model, in which both portions are trained adversarially.<n>By employing example-specific projected gradient under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the ImageNet 64x64 dataset.
  arXiv  Detail & Related papers  (2024-05-25T14:53:52Z)
• Cross-Image Attention for Zero-Shot Appearance Transfer [68.43651329067393]
  We introduce a cross-image attention mechanism that implicitly establishes semantic correspondences across images. We harness three mechanisms that either manipulate the noisy latent codes or the model's internal representations throughout the denoising process. Experiments show that our method is effective across a wide range of object categories and is robust to variations in shape, size, and viewpoint.
  arXiv  Detail & Related papers  (2023-11-06T18:33:24Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)
• Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation [15.411325887412413]
  This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM) FSA-CDM introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Experiments are conducted on four benchmark datasets from different domains.
  arXiv  Detail & Related papers  (2023-08-02T13:43:03Z)
• SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions [14.48564620768044]
  naive stitching of multiple images often results in visible seams. Recent techniques have attempted to address this issue by performing joint diffusions in multiple windows. We propose SyncDiffusion, a plug-and-play module that synchronizes multiple diffusions through gradient descent from a perceptual similarity loss.
  arXiv  Detail & Related papers  (2023-06-08T13:18:23Z)
• Real-World Image Variation by Aligning Diffusion Inversion Chain [53.772004619296794]
  A domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. We propose a novel inference pipeline called Real-world Image Variation by ALignment (RIVAL) Our pipeline enhances the generation quality of image variations by aligning the image generation process to the source image's inversion chain.
  arXiv  Detail & Related papers  (2023-05-30T04:09:47Z)
• Your Diffusion Model is Secretly a Zero-Shot Classifier [90.40799216880342]
  We show that density estimates from large-scale text-to-image diffusion models can be leveraged to perform zero-shot classification. Our generative approach to classification attains strong results on a variety of benchmarks. Our results are a step toward using generative over discriminative models for downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T17:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.