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🚀 The 4-bit era has arrived! Meet #SVDQuant, our new W4A4 quantization paradigm for diffusion models. Now, 12B FLUX can run on a 16GB 4090 laptop without offloading—with 3x speedups over W4A16 models (like NF4) while maintaining top-tier image quality. #AI #Quantization. 1/7

50,291 次观看 • 2 年前 •via X (Twitter)

10 条评论

Muyang Li 的头像
Muyang Li2 年前

Quantization effectively accelerates LLM inference, primarily by cutting weight-loading latency. But for compute-heavy diffusion models, weight quantization alone doesn’t boost speed. For real speedup, we need to quantize both weights and activations to the same bit width. 2/7

Muyang Li 的头像
Muyang Li2 年前

However, W4A4 quantization is tough with massive outliers. #SVDQuant addresses this by smoothing activations and merging its outliers into weights. It then applies SVD to the weights to add a 16-bit low-rank component, which absorbs the quantization difficulty. 3/7

Muyang Li 的头像
Muyang Li2 年前

Running the low-rank branch separately incurs high latency from redundant memory access. Our co-designed #Nunchaku engine uses kernel fusion to share inputs and outputs between branches, cutting memory access and halving kernel calls, reducing overhead to negligible 5–10%.4/7

Muyang Li 的头像
Muyang Li2 年前

On 12B FLUX.1-dev, we cut memory use by 3.6× compared to BF16 and, on a 16GB 4090 GPU, speeds up by 8.7× over 16-bit and 3× over the NF4 W4A16 baseline without loss of image quality. On PixArt-∑, it also outperforms other W4A4 and even W4A8 models in visual quality. 5/7

Muyang Li 的头像
Muyang Li2 年前

Nunchaku removes redundant memory access, allowing SVDQuant to work seamlessly with off-the-shelf LoRA by running it in a separate branch, without re-quantization. Our INT4 FLUX.1-dev model adapts to 5 distinct styles, matching the image quality of the original 16-bit version.6/7

Muyang Li 的头像
Muyang Li2 年前

Paper: Code: Demo: Website: Blog: Collaborate w/@syn7xavier @ZhekaiZhang @tianle_cai @xiuyu_l @jerry_gjx @xieenze_jr @chenlin_meng @junyanz89 @songhan_mit

Ramon Guthrie 的头像
Ramon Guthrie2 年前

The big question is does this support @ComfyUI, Forge and does this work with Loras and Controlnets?

Spacer 的头像
Spacer2 年前

Holy moly, 3x is massive! Would video models like Mochi be compatible with SVDQuant?

Muyang Li 的头像
Muyang Li2 年前

I think so since our method is general purpose. Will work on it.

Danny Ki 的头像
Danny Ki2 年前

Really 4-bit? What if numbers have feelings too?

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