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MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

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arxiv 2406.07209 v3 pith:SE4BUCEH submitted 2024-06-11 cs.CV

Xierui Wang , Siming Fu , Qihan Huang , Wanggui He , Hao Jiang This is my paper
classification cs.CV
keywords imagems-diffusionmulti-subjectgenerationtext-to-imagecross-attentionfidelityguidance
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advancements in text-to-image generation models have dramatically enhanced the generation of photorealistic images from textual prompts, leading to an increased interest in personalized text-to-image applications, particularly in multi-subject scenarios. However, these advances are hindered by two main challenges: firstly, the need to accurately maintain the details of each referenced subject in accordance with the textual descriptions; and secondly, the difficulty in achieving a cohesive representation of multiple subjects in a single image without introducing inconsistencies. To address these concerns, our research introduces the MS-Diffusion framework for layout-guided zero-shot image personalization with multi-subjects. This innovative approach integrates grounding tokens with the feature resampler to maintain detail fidelity among subjects. With the layout guidance, MS-Diffusion further improves the cross-attention to adapt to the multi-subject inputs, ensuring that each subject condition acts on specific areas. The proposed multi-subject cross-attention orchestrates harmonious inter-subject compositions while preserving the control of texts. Comprehensive quantitative and qualitative experiments affirm that this method surpasses existing models in both image and text fidelity, promoting the development of personalized text-to-image generation. The project page is https://MS-Diffusion.github.io.

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Cited by 8 Pith papers

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

  1. DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation

    cs.CV 2026-03 unverdicted novelty 7.0 of 10

    DSH-Bench supplies a hierarchical 58-category subject set, difficulty/scenario labels, and a human-aligned SICS metric that exposes systematic failures of 19 subject-driven T2I models.

  2. UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A reinforcement-learning reward based on bipartite face matching improves multi-identity consistency and reduces identity confusion in image customization models.

  3. OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    A training-free cache-reuse scheme that spreads computation across the full diffusion trajectory and subtracts estimated noise, accelerating DiT sampling with claimed competitive quality.

  4. FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers

    cs.CV 2025-07 conditional novelty 6.0 of 10

    FreeCus is a training-free method that combines pivotal attention sharing, reversed noise shifting, and MLLM captions to personalize Flux.1 text-to-image generation from a single reference image.

  5. FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A method for multi-subject image personalization that fuses independently trained LoRA modules at inference time on visual autoregressive models.

  6. FontAdapter: Instant Font Adaptation in Visual Text Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A two-stage curriculum with synthetic paired font data enables instant adaptation of unseen fonts in text-to-image generation using one reference glyph, without test-time fine-tuning.

  7. Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A video diffusion model, HunyuanVideo-I2V, is adapted with mixup transitions, frame-skip position embeddings, and attention masking to outperform image-only models on several controllable image generation benchmarks.

  8. FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy

    cs.RO 2025-08 reject novelty 3.0 of 10

    The abstract claims a new visuotactile robot manipulation policy (FBI) that outperforms baselines, but the manuscript body is an unrelated paper on text-to-image synthesis, so the claimed result is absent.

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