Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

Nunchaku Lite introduces a more efficient way to run diffusion transformers by leveraging the SVDQuant quantization method, which enables models to use 4-bit weights and activations, significantly reducing memory usage and improving inference speed. This innovation allows consumer-grade hardware to run large models that typically require substantial VRAM, making them more accessible. By integrating Nunchaku Lite with Diffusers, users can load these quantized models without needing a custom pipeline or separate inference engine, simply using the from_pretrained() function. Additionally, the diffuse-compressor toolkit offers users the ability to quantize new architectures and publish them as Diffusers repositories. The Nunchaku Lite version does not achieve the same speedup as the original Nunchaku engine due to its more generic implementation but still offers a substantial 30% speedup with reduced VRAM usage, maintaining image quality close to the original BF16 precision. This advance is supported by a range of GPUs and offers a practical solution for running efficient diffusion transformers on consumer hardware.

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