Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

Bringing Nunchaku 4-bit Diffusion Inference to Diffusers Bringing Nunchaku 4-bit Diffusion Inference to Diffusers Published July 23, 2026 Update on GitHub Upvote 69 Pham Hong Vinh rootonchair guest Sayak Paul sayakpaul Large diffusion transformers can create stunning images (or even videos, audio snippets, and now text), but loading a modern text-to-image model in BF16 precision often requires 20-30 GB of VRAM, which puts these models out of reach of most consumer GPUs. Quantization is a powerful solution to this problem, and Diffusers already integrates several quantization backends such as bitsandbytes, GGUF, torchao, and Quanto, which we covered in Exploring Quantization Backends in Diffusers .

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• Bringing Nunchaku 4-bit Diffusion Inference to Diffusers Published July 23, 2026 Update on GitHub Upvote 69 Pham Hong Vinh rootonchair guest Sayak Paul sayakpaul Large diffusion transformers can create stunning images (or even videos, audio snippets, and now text), but loading a modern text-to-image model in BF16 precision often requires 20-30 GB of VRAM, which puts these models out of reach of most consumer GPUs.
• Quantization is a powerful solution to this problem, and Diffusers already integrates several quantization backends such as bitsandbytes, GGUF, torchao, and Quanto, which we covered in Exploring Quantization Backends in Diffusers .
• Most of these backends are weight-only .
• This means that they store the weights in low precision and dequantize them back to high precision at compute time.
• This reduces memory usage significantly, but it usually does not make inference faster, and can even add a small latency overhead.
• SVDQuant , the quantization method behind the popular Nunchaku inference engine, takes a different approach.