USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.
Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing.Advancesin Neural Information Processing Systems, 36:30146–30166, 2023
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USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning
USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.