arXiv (All)
open access
Controllable Diverse Sampling through Pullback Directions
Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random seeds may remain visually similar, limiting creative exploration. Existing approaches can increase variation, but often require additional optimizati
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
- Personalize at Test Time: Learning User Preferences for Image Generation#1233054
- Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling#1357922
- Diffusion Transformers are Provably Optimal In-context Generators#1201346
- PreviewDiff: Multimodal Critic-Guided Search over Diffusion Latents#1131041
- Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models#1201079
Record · ID 1324895
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