Parallel Rescaling: Rebalancing Consistency Guidance for Personalized Diffusion Models

• URL: http://arxiv.org/abs/2506.00607v1
• Date: Sat, 31 May 2025 15:36:36 GMT
• Title: Parallel Rescaling: Rebalancing Consistency Guidance for Personalized Diffusion Models
• Abstract summary: We propose a parallel rescaling technique for personalized diffusion models.<n>Unlike prior personalization methods, our technique does not require additional training data or expensive annotations.
• Score: 3.1964044595140217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalizing diffusion models to specific users or concepts remains challenging, particularly when only a few reference images are available. Existing methods such as DreamBooth and Textual Inversion often overfit to limited data, causing misalignment between generated images and text prompts when attempting to balance identity fidelity with prompt adherence. While Direct Consistency Optimization (DCO) with its consistency-guided sampling partially alleviates this issue, it still struggles with complex or stylized prompts. In this paper, we propose a parallel rescaling technique for personalized diffusion models. Our approach explicitly decomposes the consistency guidance signal into parallel and orthogonal components relative to classifier free guidance (CFG). By rescaling the parallel component, we minimize disruptive interference with CFG while preserving the subject's identity. Unlike prior personalization methods, our technique does not require additional training data or expensive annotations. Extensive experiments show improved prompt alignment and visual fidelity compared to baseline methods, even on challenging stylized prompts. These findings highlight the potential of parallel rescaled guidance to yield more stable and accurate personalization for diverse user inputs.

Related papers

• Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding [56.38477453373368]
  LazyMCoT is a dynamic and training-free framework that adaptively allocates visual grounding efforts based on sample difficulty.<n>LazyMCoT rivals training-based approaches by simultaneously improving reasoning accuracy and reducing average inference latency.
  arXiv  Detail & Related papers  (2026-06-15T03:17:44Z)
• Sparse Personalized Text Generation with Multi-Trajectory Reasoning [95.19317487799292]
  PAT (Personalization with Aligned Trajectories) is a reasoning framework for cold-start LLM personalization.<n>It first retrieves information along two complementary trajectories: writing-style cues from stylistically similar users and topic-specific context from preference-aligned users.<n>It then employs a reinforcement learning-based, iterative dual-reasoning mechanism that enables the LLM to jointly refine and integrate these signals.
  arXiv  Detail & Related papers  (2026-04-27T20:58:49Z)
• Directing the Narrative: A Finetuning Method for Controlling Coherence and Style in Story Generation [2.9255420892138306]
  We propose a two-stage framework designed for robust and consistent story generation.<n>First, we introduce Group-Shared Attention (GSA), a mechanism that fosters intrinsic consistency.<n>Second, we leverage Direct Preference Optimization (DPO) to align generated outputs with human aesthetic and narrative standards.
  arXiv  Detail & Related papers  (2026-03-18T02:43:02Z)
• Relevance Feedback in Text-to-Image Diffusion: A Training-Free And Model-Agnostic Interactive Framework [7.254162537916632]
  We propose RFD, an interactive framework that adapts the relevance feedback mechanism from information retrieval to diffusion models.<n>In RFD, users replace explicit textual dialogue with implicit, multi-select visual feedback to minimize cognitive load.<n>RFD operates entirely within the external text space, making it strictly training-free and model-agnostic as a universal plug-and-play solution.
  arXiv  Detail & Related papers  (2026-03-16T07:38:23Z)
• Fine-tuning MLLMs Without Forgetting Is Easier Than You Think [72.59321247529975]
  We design a 2x2 experimental framework to assess model performance across in-distribution and out-of-distribution image and text inputs.<n>Our results show that appropriate regularization, such as constraining the number of trainable parameters or adopting a low learning rate, effectively prevents forgetting when dealing with out-of-distribution images.<n>We attribute this forgetting as task-specific overfitting and address this issue by introducing a data-hybrid training strategy.
  arXiv  Detail & Related papers  (2026-03-15T17:16:19Z)
• OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching [14.664226708184676]
  Flow-based text-to-image models follow deterministic trajectories, forcing users to repeatedly sample to discover diverse modes.<n>We present a training-free, inference-time control mechanism that makes the flow itself diversity-aware.
  arXiv  Detail & Related papers  (2025-10-10T07:07:19Z)
• TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling [53.61290359948953]
  Tangential Amplifying Guidance (TAG) operates solely on trajectory signals without modifying the underlying diffusion model.<n>We formalize this guidance process by leveraging a first-order Taylor expansion.<n> TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition.
  arXiv  Detail & Related papers  (2025-10-06T06:53:29Z)
• Dynamic Classifier-Free Diffusion Guidance via Online Feedback [53.54876309092376]
  "One-size-all" approach fails to adapt to the diverse requirements of different prompts.<n>We introduce a framework for dynamic CFG scheduling.<n>We demonstrate the effectiveness of our approach on both small-scale models and the state-of-the-art Imagen 3.
  arXiv  Detail & Related papers  (2025-09-19T16:27:19Z)
• Steering Guidance for Personalized Text-to-Image Diffusion Models [19.550718192994353]
  Existing sampling guidance methods fail to guide the output toward well-balanced space.<n>We propose personalization guidance, a simple yet effective method leveraging an unlearned weak model conditioned on a null text prompt.<n>Our method explicitly steers the outputs toward a balanced latent space without additional computational overhead.
  arXiv  Detail & Related papers  (2025-08-01T05:02:26Z)
• A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets [26.167194142428475]
  Vision-language models (VLMs) often struggle with compositional reasoning due to insufficient high-quality image-text data.<n>We propose a novel block-based diffusion approach that automatically generates counterfactual datasets without manual annotation.<n>Our approach achieves state-of-the-art results across multiple benchmarks while using substantially less training data than existing methods.
  arXiv  Detail & Related papers  (2025-07-07T06:47:10Z)
• Feedback Guidance of Diffusion Models [0.0]
  Interval-Free Guidance (CFG) has become standard for improving sample fidelity in conditional diffusion models.<n>We propose FeedBack Guidance (FBG), which uses a state-dependent coefficient to self-regulate guidance amounts based on need.
  arXiv  Detail & Related papers  (2025-06-06T13:46:32Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples [23.92307798902212]
  This paper introduces D-Fusion, a method to construct DPO-trainable visually consistent samples.<n>On one hand, by performing mask-guided self-attention fusion, the resulting images are not only well-aligned, but also visually consistent with given poorly-aligned images.<n>On the other hand, D-Fusion can retain the denoising trajectories of the resulting images, which are essential for DPO training.
  arXiv  Detail & Related papers  (2025-05-28T06:03:41Z)
• Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion [52.315729095824906]
  MLLM Semantic-Corrected Ping-Pong-Ahead Diffusion (PPAD) is a novel framework that introduces a Multimodal Large Language Model (MLLM) as a semantic observer during inference.<n>It performs real-time analysis on intermediate generations, identifies latent semantic inconsistencies, and translates feedback into controllable signals that actively guide the remaining denoising steps.<n>Extensive experiments demonstrate PPAD's significant improvements.
  arXiv  Detail & Related papers  (2025-05-26T14:42:35Z)
• Towards Transformer-Based Aligned Generation with Self-Coherence Guidance [51.42269790543461]
  We introduce a training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs)<n>Existing TGDMs often struggle to generate semantically aligned images, particularly when dealing with complex text prompts or multi-concept attribute binding challenges.<n>Our method addresses these challenges by directly optimizing cross-attention maps during the generation process.
  arXiv  Detail & Related papers  (2025-03-22T07:03:57Z)
• DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling [14.621456944266802]
  We propose a training-free alignment method, DyMO, for aligning the generated images and human preferences during inference.<n>Apart from text-aware human preference scores, we introduce a semantic alignment objective for enhancing the semantic alignment in the early stages of diffusion.<n>Experiments with diverse pre-trained diffusion models and metrics demonstrate the effectiveness and robustness of the proposed method.
  arXiv  Detail & Related papers  (2024-12-01T10:32:47Z)
• Training-free Composite Scene Generation for Layout-to-Image Synthesis [29.186425845897947]
  This paper introduces a novel training-free approach designed to overcome adversarial semantic intersections during the diffusion conditioning phase. We propose two innovative constraints: 1) an inter-token constraint that resolves token conflicts to ensure accurate concept synthesis; and 2) a self-attention constraint that improves pixel-to-pixel relationships. Our evaluations confirm the effectiveness of leveraging layout information for guiding the diffusion process, generating content-rich images with enhanced fidelity and complexity.
  arXiv  Detail & Related papers  (2024-07-18T15:48:07Z)
• Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis [65.7968515029306]
  We propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for Pose-Guided Person Image Synthesis (PGPIS) A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt.
  arXiv  Detail & Related papers  (2024-02-28T06:07:07Z)
• Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback [70.32795295142648]
  Linear alignment is a novel algorithm that aligns language models with human preferences in one single inference step. Experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment.
  arXiv  Detail & Related papers  (2024-01-21T10:46:23Z)
• DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling [49.46842536813477]
  We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models. We combine one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects and scenes. Our approach allows increasing the number of available training pairs significantly without requiring additional annotations.
  arXiv  Detail & Related papers  (2023-03-24T15:42:54Z)

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.