AI RESEARCH
ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis
arXiv CS.CV
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ArXi:2404.10141v2 Announce Type: replace Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contained prompts, failing to capture the complexity of real-world captions. Human-written captions often involve multiple interacting subjects, rich contextual references, and abstractive phrasing, conditions under which current image-text encoders like CLIP struggle. To systematically study these deficiencies, we