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InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity

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arxiv 2503.16418 v2 pith:X4VXROWW submitted 2025-03-20 cs.CV cs.LG

Liming Jiang , Qing Yan , Yumin Jia , Zichuan Liu , Hao Kang , Xin Lu This is my paper · ORCID
classification cs.CVcs.LG
keywords infuidentityexistinggenerationalignmentditsflexibleimage
verification ladder T0 review T1 audit T2 compute T3 formal
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Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.

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Cited by 7 Pith papers

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

  1. LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers

    cs.CV 2025-05 conditional novelty 7.0 of 10

    LoRAShop localizes each LoRA's effect to attention-derived spatial masks inside a Flux transformer, enabling training-free multi-concept image generation and editing.

  2. iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    iMontage repurposes a pretrained video diffusion model to generate coherent yet highly dynamic image sets from arbitrary numbers of input images.

  3. USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

    cs.CV 2025-08 conditional novelty 6.0 of 10

    USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.

  4. LaVieID: Local Autoregressive Diffusion Transformers for Identity-Preserving Video Creation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    LaVieID improves identity-preserving text-to-video by routing local facial parts into early DiT blocks and autoregressively refining denoised video tokens in temporal chunks.

  5. FastFace: Tuning Identity Preservation in Distilled Diffusion via Guidance and Attention

    cs.CV 2025-05 conditional novelty 6.0 of 10

    An inference-time framework of decoupled classifier-free guidance and attention manipulation improves identity preservation and prompt alignment when pretrained face ID adapters are used with few-step distilled diffus...

  6. OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A benchmark and five-million-clip dataset for evaluating and training subject-to-video generation models, with three new metrics for subject consistency, naturalness, and text alignment.

  7. EditIDv2: Editable ID Customization with Data-Lubricated ID Feature Integration for Text-to-Image Generation

    cs.CV 2025-09 reject novelty 3.0 of 10

    EditIDv2 fine-tunes only PerceiverAttention cross-attention weights on 3K images to inject editability into Flux-based ID customization, reporting selective gains on the self-proposed IBench benchmark.

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