Run FLUX.1-dev three times faster

Open-weight generative models and open-source tooling increasingly allow developers to self-host image, music, and text generation APIs, but competitive performance requires specialized inference optimization. Using Black Forest Labs’ FLUX.1-dev image model on Modal, the authors reduced average 1024×1024 image generation latency from roughly 6.75 seconds to under three seconds through a combination of PyTorch compilation, fused query-key-value attention projections, and channels-last memory layouts, which together delivered a 1.5× speedup. They then applied First Block Caching, an approximate technique that skips diffusion steps predicted to have minimal effect on the output, producing a further 2× speedup while accepting limited quality tradeoffs. Because compiler autotuning substantially increased startup times, they also used compiler artifact caches and Modal Memory Snapshots to reduce cold-start latency by about 30×. The resulting autoscaling service is presented as capable of matching proprietary image-generation APIs in speed while potentially offering greater control and competitive pricing.

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