ASTRA disentangles subject identity from pose structure in diffusion transformers via retrieval-augmented pose guidance, asymmetric EURoPE embeddings, and a DSM adapter to improve multi-subject generation.
Realcustom++: Representing images as real-word for real-time customization
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Lance presents a dual-stream mixture-of-experts model with modality-aware positional encoding and staged multi-task training that outperforms prior open-source unified models on image and video generation while keeping strong understanding performance.
MUSIC is the first MLLM for multi-subject in-context image generation that uses an automatic data pipeline, vision chain-of-thought reasoning, and semantics-driven spatial layout planning to outperform prior methods on a new MSIC benchmark.
A data-generation pipeline plus pairwise subject-consistency rewards in RL improve consistency and prompt adherence for multi-subject personalized image generation.
citing papers explorer
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ASTRA: Enhancing Multi-Subject Generation with Retrieval-Augmented Pose Guidance and Disentangled Position Embedding
ASTRA disentangles subject identity from pose structure in diffusion transformers via retrieval-augmented pose guidance, asymmetric EURoPE embeddings, and a DSM adapter to improve multi-subject generation.
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Lance: Unified Multimodal Modeling by Multi-Task Synergy
Lance presents a dual-stream mixture-of-experts model with modality-aware positional encoding and staged multi-task training that outperforms prior open-source unified models on image and video generation while keeping strong understanding performance.
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Multimodal Large Language Models for Multi-Subject In-Context Image Generation
MUSIC is the first MLLM for multi-subject in-context image generation that uses an automatic data pipeline, vision chain-of-thought reasoning, and semantics-driven spatial layout planning to outperform prior methods on a new MSIC benchmark.
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PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards
A data-generation pipeline plus pairwise subject-consistency rewards in RL improve consistency and prompt adherence for multi-subject personalized image generation.