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$\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space
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abstract
Despite the recent advances in personalized text-to-image (P-T2I) generative models, it remains challenging to perform finetuning-free multi-subject-driven T2I in a resource-efficient manner. Predominantly, contemporary approaches, involving the training of Hypernetworks and Multimodal Large Language Models (MLLMs), require heavy computing resources that range from 600 to 12300 GPU hours of training. These subject-driven T2I methods hinge on Latent Diffusion Models (LDMs), which facilitate T2I mapping through cross-attention layers. While LDMs offer distinct advantages, P-T2I methods' reliance on the latent space of these diffusion models significantly escalates resource demands, leading to inconsistent results and necessitating numerous iterations for a single desired image. In this paper, we present $\lambda$-ECLIPSE, an alternative prior-training strategy that works in the latent space of a pre-trained CLIP model without relying on the diffusion UNet models. $\lambda$-ECLIPSE leverages the image-text interleaved pre-training for fast and effective multi-subject-driven P-T2I. Through extensive experiments, we establish that $\lambda$-ECLIPSE surpasses existing baselines in composition alignment while preserving concept alignment performance, even with significantly lower resource utilization. $\lambda$-ECLIPSE performs multi-subject driven P-T2I with just 34M parameters and is trained on a mere 74 GPU hours. Additionally, $\lambda$-ECLIPSE demonstrates the unique ability to perform multi-concept interpolations.
Forward citations
Cited by 6 Pith papers
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Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors
Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.
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DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
DSH-Bench is a benchmark for subject-driven T2I generation that uses hierarchical taxonomy sampling, difficulty/scenario classification, and a new SICS metric showing 9.4% higher human correlation than prior measures.
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DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
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.
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Adversarial Concept Distillation for One-Step Diffusion Personalization
OPAD enables reliable high-quality personalization of one-step diffusion models via multi-step teacher distillation combined with adversarial alignment losses.
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FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers
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.
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FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
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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