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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 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 10

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Ramon Guthrie
Ramon Guthrie2 лет назад

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

Фото профиля Spacer
Spacer2 лет назад

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

Фото профиля Muyang Li
Muyang Li2 лет назад

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

Фото профиля Danny Ki
Danny Ki2 лет назад

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

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