arXiv (All)
open access
Accelerating Masked Image Generation by Learning Controlled Latent Dynamics
Masked Image Generation Models (MIGMs) have achieved great success, yet their efficiency is hampered by the multiple steps of bi-directional attention. In fact, there exists notable redundancy in their computation: when sampling discrete tokens, the rich semantics contained in the continuous feature
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
- InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation#196161
- Denoising Diffusion Generative Models Secretly Calculate Attentions#820887
- Arbitrary-Scale Image Generation and Upsampling Using Latent Diffusion Model and Implicit Neural Decoder#454678
- Alternative-Space Structure Shapes Negation Resolution in Multimodal Generation#196192
- On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators#1050556
Record · ID 925937
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