A pixel-space Diffusion Transformer with Unified Transformer architecture unifies image generation, editing, and personalization in an end-to-end model that maps all inputs to a shared token space and scales from 8B to over 200B parameters.
arXiv preprint arXiv:2411.06558 (2024)
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BindEdit suppresses two forms of attention leakage in diffusion-based editing by binding target tokens to regions, rebalancing cross-attention, and adding a region fidelity term, plus a new multi-object benchmark.
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.
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HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
A pixel-space Diffusion Transformer with Unified Transformer architecture unifies image generation, editing, and personalization in an end-to-end model that maps all inputs to a shared token space and scales from 8B to over 200B parameters.
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BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing
BindEdit suppresses two forms of attention leakage in diffusion-based editing by binding target tokens to regions, rebalancing cross-attention, and adding a region fidelity term, plus a new multi-object benchmark.
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Training-Free Object-Background Compositional T2I via Dynamic Spatial Guidance and Multi-Path Pruning
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.