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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
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 9 Pith papers

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

  1. StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition

    cs.GR 2026-04 unverdicted novelty 7.0 of 10

    StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.

  2. InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation

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    InstructMoLE replaces per-token routing with instruction-guided global routing for mixture-of-low-rank-experts in diffusion transformers and adds an output-space orthogonality loss to improve multi-conditional image g...

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

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

  4. Emu3.5: Native Multimodal Models are World Learners

    cs.CV 2025-10 unverdicted novelty 6.0 of 10

    Emu3.5 is a native multimodal world model pre-trained on over 10 trillion vision-language tokens with next-token prediction, post-trained via reinforcement learning, and accelerated by Discrete Diffusion Adaptation fo...

  5. 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.

  6. 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.

  7. When Few Steps Are Enough: Training-Free Acceleration of Identity-Preserved Generation

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    Frozen identity adapter from FLUX dev works on distilled schnell model, enabling 5.9x faster generation with better identity preservation in few steps.

  8. OmniGen2: Towards Instruction-Aligned Multimodal Generation

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    OmniGen2 introduces a unified generative model with two distinct decoding pathways and a decoupled image tokenizer that achieves competitive results on text-to-image and editing benchmarks plus state-of-the-art consis...

  9. 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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