● Covered by 1 source · 1 reportMedium impact1 positive
Hugging Face Diffusers Now Natively Supports Nunchaku 4-bit Diffusion Inference
Key points
- Diffusers now natively supports Nunchaku 4-bit inference.
- Nunchaku uses SVDQuant for 4-bit weights and activations (W4A4).
- Integration reduces memory usage and speeds up denoising loops.
- No local CUDA compilation or separate inference library is required.
Native Nunchaku Integration
Hugging Face Diffusers now provides native support for Nunchaku 4-bit diffusion inference. This integration allows users to load Nunchaku checkpoints directly using the `from_pretrained()` function, simplifying the workflow for optimized diffusion models.
The `kernels` package facilitates this by handling CUDA kernel downloads from the Hugging Face Hub, removing the requirement for local CUDA compilation.
SVDQuant Technology
Nunchaku inference engine employs SVDQuant, a quantization method that processes main transformer layers with 4-bit weights and activations (W4A4). This approach differs from weight-only quantization by not only reducing memory usage but also accelerating the denoising loop.
Traditional weight-only backends primarily reduce memory but do not typically speed up inference and can introduce latency.
Simplified Model Deployment and Customization
Users can now load pre-quantized pipelines, such as the ERNIE-Image-Turbo-nunchaku-lite checkpoint, directly within Diffusers without custom pipeline classes or separate inference engines. The `diffuse-compressor` toolkit also enables users to quantize new architectures and publish them as standard Diffusers repositories.
An example using ERNIE-Image-Turbo-nunchaku-lite on an RTX 5090 generated a 1024x1024 image in approximately 1.7 seconds with a peak memory usage of about 12 GB, compared to 24 GB for the BF16 pipeline.
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Primary sources
GitHub huggingface/blog GitHub nunchaku-tech/nunchaku GitHub huggingface/kernels GitHub rootonchair/diffuse-compressor GitHub nunchaku-ai/nunchaku GitHub huggingface/diffusersReporting from
Hugging Face Diffusers now natively supports Nunchaku 4-bit diffusion inference, which utilizes SVDQuant for 4-bit weights and activations. This integration allows users to run diffusion models with reduced memory usage and faster inference directly within Diffusers, eliminating the need for separate inference libraries or local CUDA compilation.