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Multi-Concept Customization of Text-to-Image Diffusion

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arxiv 2212.04488 v2 pith:AXJIB34E submitted 2022-12-08 cs.CV cs.GRcs.LG

Nupur Kumari , Bingliang Zhang , Richard Zhang , Eli Shechtman , Jun-Yan Zhu This is my paper · ORCID
classification cs.CVcs.GRcs.LG
keywords conceptsmultiplemodelstext-to-imagewhilediffusionefficientexisting
verification ladder T0 review T1 audit T2 compute T3 formal
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While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to quickly acquire a new concept, given a few examples? Furthermore, can we compose multiple new concepts together? We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models. We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts while enabling fast tuning (~6 minutes). Additionally, we can jointly train for multiple concepts or combine multiple fine-tuned models into one via closed-form constrained optimization. Our fine-tuned model generates variations of multiple new concepts and seamlessly composes them with existing concepts in novel settings. Our method outperforms or performs on par with several baselines and concurrent works in both qualitative and quantitative evaluations while being memory and computationally efficient.

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Cited by 5 Pith papers

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    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.

  4. SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner

    cs.CV 2024-12 conditional novelty 5.0 of 10

    SUGAR combines a 2.5 million synthetic image-video-text dataset, selective attention, and two-stage training to generate videos of a subject from one image and a text prompt without per-subject fine-tuning.

  5. Stage-Aware Adaptation and Distribution Calibration for Subject-Driven Personalized Text-to-Image Generation

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    Stage-aware low-rank scaling and distribution-calibrated candidate selection improve identity consistency in personalized image generation but reduce output diversity.

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