arXiv (All)
open access
Image Tokenizers Should Be Trained Between Images
Latent diffusion models generate images as codes of an image tokenizer, whose decoder turns them into the pixel image. Recent tokenizers improve how well these codes reconstruct an image and how easily a generator learns them. Yet a generator does not reproduce the codes of training images, and when
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
- Elastic Token Compression for Pixel-Space Diffusion Transformers#794089
- Elastic Token Compression for Pixel-Space Diffusion Transformers#790070
- Diffusion Transformers are Provably Optimal In-context Generators#1201346
- GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models#1070205
- Training-Free Inpainting Across Domains with a Frozen Text-to-Image Diffusion Model#820864
Record · ID 1341644
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