arXiv (All)
open access
DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space
Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high resolutions, like 4K image generation. While previous research accelerates diffusion models in various aspects, it seldom handles the inherent redundancy wi
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
- Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models#793582
- Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models#789578
- 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
- Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models#1122596
Record · ID 984899
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