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.
lambda-eclipse: Multi-concept personalized text-to-image diffusion models by leveraging clip latent space
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
OPAD enables reliable high-quality personalization of one-step diffusion models via multi-step teacher distillation combined with adversarial alignment losses.
citing papers explorer
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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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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.