Delete main.py
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main.py
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import torch
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from diffusers import FluxPipeline
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) # can replace schnell with dev
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# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
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pipe.enable_sequential_cpu_offload()
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
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prompt = "A cat holding a sign that says hello world"
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out = pipe(
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prompt=prompt,
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guidance_scale=4,
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height=768,
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width=1024,
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num_inference_steps=4,
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max_sequence_length=256,
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).images[0]
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out.save("image.png")
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