63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import time
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import modal
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import util
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app = modal.App("run-stable-diffusion-cli")
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app.run_inference = modal.Function.from_name("stable-diffusion-cli", "SD15.run_txt2img_inference")
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@app.local_entrypoint()
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def main(
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prompt: str,
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n_prompt: str,
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height: int = 512,
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width: int = 512,
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samples: int = 5,
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batch_size: int = 1,
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steps: int = 20,
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seed: int = -1,
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upscaler: str = "",
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use_face_enhancer: str = "False",
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fix_by_controlnet_tile: str = "False",
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output_format: str = "png",
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):
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"""
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This function is the entrypoint for the Runway CLI.
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The function pass the given prompt to StableDiffusion on Modal,
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gets back a list of images and outputs images to local.
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"""
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directory = util.make_directory()
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seed_generated = seed
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for i in range(samples):
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if seed == -1:
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seed_generated = util.generate_seed()
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start_time = time.time()
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images = app.run_inference.remote(
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prompt=prompt,
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n_prompt=n_prompt,
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height=height,
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width=width,
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batch_size=batch_size,
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steps=steps,
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seed=seed_generated,
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upscaler=upscaler,
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use_face_enhancer=use_face_enhancer == "True",
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fix_by_controlnet_tile=fix_by_controlnet_tile == "True",
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output_format=output_format,
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)
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util.save_images(directory, images, seed_generated, i, output_format)
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total_time = time.time() - start_time
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print(f"Sample {i} took {total_time:.3f}s ({(total_time)/len(images):.3f}s / image).")
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prompts: dict[str, int | str] = {
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"prompt": prompt,
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"n_prompt": n_prompt,
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"height": height,
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"width": width,
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"samples": samples,
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"batch_size": batch_size,
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"steps": steps,
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}
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util.save_prompts(prompts)
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