arXiv (All)
open access
Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance
Post-training quantization for diffusion models increasingly exploits timestep, feature, and layer structure. While recent work has begun incorporating CFG structure into diffusion quantization, activation quantization still operates independently across conditional and unconditional coordinates, le
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
- QuantForge: Discovering Residual Decompositions for MXFP4 Post-Training Quantization#1128939
- Sampling-Aware Quantization for Diffusion Models#158705
- DSAQuant: Denoising-Stage-Aligned Quantization-Aware Training for Video Generation#926880
- SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models#924006
- SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models#919777
Record · ID 1155865
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