arXiv (All)
open access
TIE: Training-free CLIP Embedding Guidance for Compositional Text-to-Image Alignment
Text-to-image (T2I) diffusion models have advanced notably, but still fail to satisfy prompt conditions, resulting in attribute mismatches and missing objects. These errors normally fall into two categories: concept loss, when an object or attribute (i.e., concept) goes missing, and concept confusio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models#1130943
- How Far Do Simple Transformations Translate Across Text Embedding Models?#924634
- How Far Do Simple Transformations Translate Across Text Embedding Models?#920402
- GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models#1070205
- CompArt: Operationalizing Aesthetic Alignment in Text-to-Image Generation via Principles of Art#1012007
Record · ID 1447774
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive