AI BriefingDiffusion Memory Optimization Based on Quanto
Key point
This presents a method for efficiently reducing the memory usage of Diffusion Transformer models by leveraging the Quanto quantization tool.
Details
With the recent introduction of Transformer-based diffusion backbones (Stable Diffusion 3, PixArt-Sigma, etc.) for high-resolution text-to-image (T2I) generation, memory requirements are surging as model scale grows. For example, Stable Diffusion 3 requires about 18.765 GB of GPU memory for FP16 precision inference, making it difficult to run on consumer-grade GPUs.
Hugging Face has integrated the Quanto quantization toolkit into the Diffusers library, presenting a technical approach that improves memory efficiency while minimizing quality degradation.
Key technical features:
- Quantization applied: Using PyTorch-based Quanto, the memory footprint of diffusion pipelines can be reduced.
- Target models: Supports major Transformer-based models such as PixArt-Sigma, Stable Diffusion 3, and Aura Flow.
- Optimization strategy: It is recommended to use
bfloat16as the main computation type and exclude the VAE from quantization for numerical stability. - Data types: Memory savings can be maximized through
qint8andINT4quantization.
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