Run Flux models on Google Colab

The FLUX tutorial uses the Diffusers library and the schnell variant, which can generate an image in four inference steps. Google Colab supplies a free T4 GPU, but the model is still too large to sit comfortably in GPU memory. The notebook therefore enables sequential CPU offload, moving model components onto the GPU only when each part is needed.

That memory strategy makes the run possible, not fast. The model download consumes significant storage, repeated transfers add latency, and the demonstrated image takes roughly ten to twelve minutes after setup. The output—a cat holding a Hello World sign—proves the path works on constrained hardware while making the cost visible in waiting time.

The experiment is useful precisely because it does not describe free compute as unlimited compute. Colab is a good place to test the pipeline and understand the memory controls before paying for stronger hardware. For repeated production generation, the transfer overhead and session limits would need a different deployment plan.