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SVDQuant: 4-Bit Quantization Powers 12B Flux on a 16GB 4090 GPU with 3x Speedup
A new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU.
However, scaling brings challenges: these models become computationally heavy, demanding high memory and longer processing times, making them prohibitive for real-time applications. The figure above shows visual examples of our INT4 FLUX.1-dev model with LoRAs applied in five distinct styles— Realism, Ghibsky Illustration, Anime, Children Sketch, and Yarn Art. A new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU.
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