Aligning Text to Image in Diffusion Models is Easier Than You Think

• URL: http://arxiv.org/abs/2503.08250v3
• Date: Fri, 21 Mar 2025 07:28:43 GMT
• Title: Aligning Text to Image in Diffusion Models is Easier Than You Think
• Authors: Jaa-Yeon Lee, Byunghee Cha, Jeongsol Kim, Jong Chul Ye,
• Abstract summary: We introduce a lightweight contrastive fine tuning strategy called SoftREPA that uses soft text tokens.<n>Our method explicitly increases the mutual information between text and image representations, leading to enhanced semantic consistency.
• Score: 47.623236425067326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent advancements in generative modeling have significantly improved text-image alignment, some residual misalignment between text and image representations still remains. Although many approaches have attempted to address this issue by fine-tuning models using various reward models, etc., we revisit the challenge from the perspective of representation alignment-an approach that has gained popularity with the success of REPresentation Alignment (REPA). We first argue that conventional text-to-image (T2I) diffusion models, typically trained on paired image and text data (i.e., positive pairs) by minimizing score matching or flow matching losses, is suboptimal from the standpoint of representation alignment. Instead, a better alignment can be achieved through contrastive learning that leverages both positive and negative pairs. To achieve this efficiently even with pretrained models, we introduce a lightweight contrastive fine tuning strategy called SoftREPA that uses soft text tokens. This approach improves alignment with minimal computational overhead by adding fewer than 1M trainable parameters to the pretrained model. Our theoretical analysis demonstrates that our method explicitly increases the mutual information between text and image representations, leading to enhanced semantic consistency. Experimental results across text-to-image generation and text-guided image editing tasks validate the effectiveness of our approach in improving the semantic consistency of T2I generative models.

Related papers

• Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models [92.18057318458528]
  Token-Shuffle is a novel method that reduces the number of image tokens in Transformer. Our strategy requires no additional pretrained text-encoder and enables MLLMs to support extremely high-resolution image synthesis. In GenAI-benchmark, our 2.7B model achieves 0.77 overall score on hard prompts, outperforming AR models LlamaGen by 0.18 and diffusion models LDM by 0.15.
  arXiv  Detail & Related papers  (2025-04-24T17:59:56Z)
• Removing Distributional Discrepancies in Captions Improves Image-Text Alignment [76.31530836622694]
  We introduce a model designed to improve the prediction of image-text alignment. Our approach focuses on generating high-quality training datasets for the alignment task. We also demonstrate the applicability of our model by ranking the images generated by text-to-image models based on text alignment.
  arXiv  Detail & Related papers  (2024-10-01T17:50:17Z)
• Information Theoretic Text-to-Image Alignment [49.396917351264655]
  Mutual Information (MI) is used to guide model alignment.<n>Our method uses self-supervised fine-tuning and relies on a point-wise (MI) estimation between prompts and images.<n>Our analysis indicates that our method is superior to the state-of-the-art, yet it only requires the pre-trained denoising network of the T2I model itself to estimate MI.
  arXiv  Detail & Related papers  (2024-05-31T12:20:02Z)
• Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models [67.68871360210208]
  Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency.<n>We propose a novel fine-tuning objective, dubbed Direct Consistency Optimization, which controls the deviation between fine-tuning and pretrained models.<n>We show that our approach achieves better prompt fidelity and subject fidelity than those post-optimized for merging regular fine-tuned models.
  arXiv  Detail & Related papers  (2024-02-19T09:52:41Z)
• Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training [33.51524424536508]
  Iterative Prompt Relabeling (IPR) is a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations.
  arXiv  Detail & Related papers  (2023-12-23T11:10:43Z)
• Improving Compositional Text-to-image Generation with Large Vision-Language Models [26.202725136839632]
  compositional text-to-image models frequently encounter difficulties in generating high-quality images that align with input texts. We employ large vision-language models (LVLMs) for multi-dimensional assessment of the alignment between generated images and their corresponding input texts. Our experimental results validate that the proposed methodology significantly improves text-image alignment in compositional image generation.
  arXiv  Detail & Related papers  (2023-10-10T05:09:05Z)
• RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment [112.45442468794658]
  We propose a two-stage coarse-to-fine semantic re-alignment method, named RealignDiff. In the coarse semantic re-alignment phase, a novel caption reward is proposed to evaluate the semantic discrepancy between the generated image caption and the given text prompt. The fine semantic re-alignment stage employs a local dense caption generation module and a re-weighting attention modulation module to refine the previously generated images from a local semantic view.
  arXiv  Detail & Related papers  (2023-05-31T06:59:21Z)
• If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection [53.320946030761796]
  diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt. We show that large T2I diffusion models are more faithful than usually assumed, and can generate images faithful to even complex prompts. We introduce a pipeline that generates candidate images for a text prompt and picks the best one according to an automatic scoring system.
  arXiv  Detail & Related papers  (2023-05-22T17:59:41Z)
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models [16.786221846896108]
  We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.
  arXiv  Detail & Related papers  (2021-12-20T18:42:55Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)

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.