Few-Step Diffusion via Score identity Distillation

• URL: http://arxiv.org/abs/2505.12674v1
• Date: Mon, 19 May 2025 03:45:16 GMT
• Title: Few-Step Diffusion via Score identity Distillation
• Authors: Mingyuan Zhou, Yi Gu, Zhendong Wang,
• Abstract summary: Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models.<n>Existing methods rely on real or teacher-synthesized images to perform well when distilling high-resolution T2I diffusion models.<n>We propose two new guidance strategies: Zero-CFG, which disables CFG in the teacher and removes text conditioning in the fake score network, and Anti-CFG, which applies negative CFG in the fake score network.
• Score: 67.07985339442703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models by distilling a pretrained score network into a one- or few-step generator. While existing methods have made notable progress, they often rely on real or teacher-synthesized images to perform well when distilling high-resolution T2I diffusion models such as Stable Diffusion XL (SDXL), and their use of classifier-free guidance (CFG) introduces a persistent trade-off between text-image alignment and generation diversity. We address these challenges by optimizing Score identity Distillation (SiD) -- a data-free, one-step distillation framework -- for few-step generation. Backed by theoretical analysis that justifies matching a uniform mixture of outputs from all generation steps to the data distribution, our few-step distillation algorithm avoids step-specific networks and integrates seamlessly into existing pipelines, achieving state-of-the-art performance on SDXL at 1024x1024 resolution. To mitigate the alignment-diversity trade-off when real text-image pairs are available, we introduce a Diffusion GAN-based adversarial loss applied to the uniform mixture and propose two new guidance strategies: Zero-CFG, which disables CFG in the teacher and removes text conditioning in the fake score network, and Anti-CFG, which applies negative CFG in the fake score network. This flexible setup improves diversity without sacrificing alignment. Comprehensive experiments on SD1.5 and SDXL demonstrate state-of-the-art performance in both one-step and few-step generation settings, along with robustness to the absence of real images. Our efficient PyTorch implementation, along with the resulting one- and few-step distilled generators, will be released publicly as a separate branch at https://github.com/mingyuanzhou/SiD-LSG.

Related papers

• Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis [65.77083310980896]
  We propose Adrial Distribution Matching (ADM) to align latent predictions between real and fake score estimators for score distillation.<n>Our proposed method achieves superior one-step performance on SDXL compared to DMD2 while consuming less GPU time.<n>Additional experiments that apply multi-step ADM distillation on SD3-Medium, SD3.5-Large, and CogVideoX set a new benchmark towards efficient image and video synthesis.
  arXiv  Detail & Related papers  (2025-07-24T16:45:05Z)
• Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization [34.53986517177061]
  We propose a novel framework to existing diffusion-based distillation methods, leveraging diffusion models for selection rather than generation.<n>Our method starts by predicting noise generated by the diffusion model based on input images and text prompts, then calculates the corresponding loss for each pair.<n>This streamlined framework enables a single-step distillation process, and extensive experiments demonstrate that our approach outperforms state-of-the-art methods across various metrics.
  arXiv  Detail & Related papers  (2024-12-13T08:34:46Z)
• Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation [1.8434042562191815]
  We introduce Regularized Distribution Matching Distillation, applicable to unpaired image-to-image (I2I) problems. We demonstrate its empirical performance in application to several translation tasks, including 2D examples and I2I between different image datasets.
  arXiv  Detail & Related papers  (2024-06-20T22:22:31Z)
• Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation [62.30570286073223]
  Diffusion-based text-to-image generation models have demonstrated the ability to produce images aligned with textual descriptions.<n>We introduce a data-free guided distillation method that enables the efficient distillation of pretrained Diffusion models without access to the real training data.<n>By exclusively training with synthetic images generated by its one-step generator, our data-free distillation method rapidly improves FID and CLIP scores, achieving state-of-the-art FID performance while maintaining a competitive CLIP score.
  arXiv  Detail & Related papers  (2024-06-03T17:44:11Z)
• Improved Distribution Matching Distillation for Fast Image Synthesis [54.72356560597428]
  We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images.
  arXiv  Detail & Related papers  (2024-05-23T17:59:49Z)
• Distilling Diffusion Models into Conditional GANs [90.76040478677609]
  We distill a complex multistep diffusion model into a single-step conditional GAN student model. For efficient regression loss, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models.
  arXiv  Detail & Related papers  (2024-05-09T17:59:40Z)
• Iterative Token Evaluation and Refinement for Real-World Super-Resolution [77.74289677520508]
  Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. We propose an Iterative Token Evaluation and Refinement framework for RWSR. We show that ITER is easier to train than Generative Adversarial Networks (GANs) and more efficient than continuous diffusion models.
  arXiv  Detail & Related papers  (2023-12-09T17:07:32Z)

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.