Proposes V2V-Zero, a training-free framework replacing text conditioning with VLM final-layer hidden states from visual pages, achieving 0.85 on GenEval and 32.7/100 on new Simple-V2V Bench across models including video extension.
Adding conditional control to text-to-image diffusion models
4 Pith papers cite this work. Polarity classification is still indexing.
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Flow matching velocity fields are governed solely by conditional endpoint means, so changing the reference-set mean steers generation without parameter updates.
OPAD enables reliable high-quality personalization of one-step diffusion models via multi-step teacher distillation combined with adversarial alignment losses.
InsHuman proposes Human-Background Adaptive Fusion, Face-to-Face ID-Preserving, and Bidirectional Data Pairing to enable natural human insertion in images without altering identity.
citing papers explorer
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Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm
Proposes V2V-Zero, a training-free framework replacing text conditioning with VLM final-layer hidden states from visual pages, achieving 0.85 on GenEval and 32.7/100 on new Simple-V2V Bench across models including video extension.
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Follow the Mean: Reference-Guided Flow Matching
Flow matching velocity fields are governed solely by conditional endpoint means, so changing the reference-set mean steers generation without parameter updates.
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InsHuman: Towards Natural and Identity-Preserving Human Insertion
InsHuman proposes Human-Background Adaptive Fusion, Face-to-Face ID-Preserving, and Bidirectional Data Pairing to enable natural human insertion in images without altering identity.