INSET embeds images as native tokens in interleaved instructions, outperforming prior methods on multi-image consistency and text alignment as complexity grows.
Dreambench++: A human-aligned bench- mark for personalized image generation
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Omni-Attribute is a new open-vocabulary image attribute encoder trained on semantically linked pairs with dual objectives to produce disentangled representations for personalization and compositional generation.
T2I-FactualBench is a new three-tier benchmark for factuality of knowledge-intensive concepts in T2I models, using multi-round VQA evaluation to show SOTA models need improvement.
Scone unifies subject understanding and generation in a two-stage trained model to improve both composition and distinction in multi-subject image generation, outperforming prior open-source models on new benchmarks.
HunyuanImage 3.0 delivers an 80B-parameter MoE model unifying multimodal understanding and generation that matches prior state-of-the-art results while being fully open-sourced.
DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
FLUX.1 Kontext unifies image generation and editing via flow matching and sequence concatenation, delivering improved multi-turn consistency and speed on the new KontextBench benchmark.
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.
citing papers explorer
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Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation
INSET embeds images as native tokens in interleaved instructions, outperforming prior methods on multi-image consistency and text alignment as complexity grows.
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Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
Omni-Attribute is a new open-vocabulary image attribute encoder trained on semantically linked pairs with dual objectives to produce disentangled representations for personalization and compositional generation.
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T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts
T2I-FactualBench is a new three-tier benchmark for factuality of knowledge-intensive concepts in T2I models, using multi-round VQA evaluation to show SOTA models need improvement.
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Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling
Scone unifies subject understanding and generation in a two-stage trained model to improve both composition and distinction in multi-subject image generation, outperforming prior open-source models on new benchmarks.
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HunyuanImage 3.0 Technical Report
HunyuanImage 3.0 delivers an 80B-parameter MoE model unifying multimodal understanding and generation that matches prior state-of-the-art results while being fully open-sourced.
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DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
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FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
FLUX.1 Kontext unifies image generation and editing via flow matching and sequence concatenation, delivering improved multi-turn consistency and speed on the new KontextBench benchmark.
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ID-Sim: An Identity-Focused Similarity Metric
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.