Quanto
quanto-acc2852e·2 events·first seen 28d agoAliases: Quanto
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Memory-efficient Diffusion Transformers with Quanto and Diffusers
This Hugging Face blog post describes integrating the Quanto quantization library with the Diffusers framework to reduce memory requirements for diffusion transformer models. The approach enables running large image/video generation models on consumer-grade hardware by applying int8 and int4 quantization to model weights. The post covers practical implementation details and benchmarks showing memory savings for models like Flux and others in the diffusion transformer family.
Quanto: a PyTorch quantization backend for Optimum
Hugging Face introduced Quanto, a new PyTorch-based quantization backend integrated into the Optimum library. Quanto supports multiple quantization schemes and data types, targeting efficient inference for large language models and other neural networks. The tool is designed to work across hardware backends and integrates with the Hugging Face ecosystem.