MemoGen is a training-free agentic framework that stores task understanding, references, visual feedback, and lessons from past generations as reusable memory to improve text-to-image output over evolution rounds.
Knn- diffusion: Image generation via large-scale retrieval
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
BadRDM is a backdoor attack on retrieval-augmented diffusion models that poisons the retrieval database with toxicity surrogates and uses multimodal contrastive learning to force toxic generations from text triggers while preserving benign performance.
TokenFlow produces consistent text-driven video edits by propagating diffusion features according to inter-frame correspondences extracted from the source video.
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
RAVA retrieves view-consistent target-subject images via a learned cross-instance embedding and LogDet subset selection, then uses them in a multi-reference generator to improve cross-subject viewpoint alignment.
citing papers explorer
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MemoGen: Can Past Experience Improve Future Text-to-Image Generation?
MemoGen is a training-free agentic framework that stores task understanding, references, visual feedback, and lessons from past generations as reusable memory to improve text-to-image output over evolution rounds.
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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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SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation
SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
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Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models
BadRDM is a backdoor attack on retrieval-augmented diffusion models that poisons the retrieval database with toxicity surrogates and uses multimodal contrastive learning to force toxic generations from text triggers while preserving benign performance.
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TokenFlow: Consistent Diffusion Features for Consistent Video Editing
TokenFlow produces consistent text-driven video edits by propagating diffusion features according to inter-frame correspondences extracted from the source video.
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eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
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RAVA: Retrieval-Augmented Viewpoint Alignment for Subject-Driven Image Generation
RAVA retrieves view-consistent target-subject images via a learned cross-instance embedding and LogDet subset selection, then uses them in a multi-reference generator to improve cross-subject viewpoint alignment.