arXiv (All)
open access
DICE: Distilling Classifier-Free Guidance into Text Embeddings
Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these images failing to align closely with the given text prompts. Classifier-free guidance (CFG) is a popular and effective technique for improving text-imag
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
- DICE: Distilling Classifier-Free Guidance into Text Embeddings#809222
- Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning#1156994
- An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models#625432
- An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models#623159
- Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models#793582
Record · ID 819214
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