69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from __future__ import annotations
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import logging
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import time
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import modal
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from domain import OutputDirectory, Prompts, Seed, StableDiffusionOutputManger
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from infrastructure import new_txt2img
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@modal.App("run-stable-diffusion-cli").local_entrypoint()
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def main(
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version: str,
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prompt: str,
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n_prompt: str,
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height: int = 1024,
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width: int = 1024,
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samples: int = 5,
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steps: int = 20,
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seed: int = -1,
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use_upscaler: str = "False",
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fix_by_controlnet_tile: str = "True",
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output_format: str = "png",
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) -> None:
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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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logging.basicConfig(
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level=logging.INFO,
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format="[%(levelname)s] %(asctime)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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logger = logging.getLogger("run-stable-diffusion-cli")
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output_directory = OutputDirectory()
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directory_path = output_directory.make_directory()
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logger.info("Made a directory: %s", directory_path)
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prompts = Prompts(prompt, n_prompt, height, width, samples, steps)
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sd_output_manager = StableDiffusionOutputManger(prompts, directory_path)
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txt2img = new_txt2img(
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version,
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prompts,
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output_format,
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use_upscaler=use_upscaler == "True",
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fix_by_controlnet_tile=fix_by_controlnet_tile == "True",
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)
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for sample_index in range(samples):
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start_time = time.time()
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new_seed = Seed(seed)
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images = txt2img.run_inference(new_seed)
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for generated_image_index, image_bytes in enumerate(images):
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saved_path = sd_output_manager.save_image(
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image_bytes,
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new_seed.value,
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sample_index,
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generated_image_index,
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output_format,
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)
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logger.info("Saved image to the: %s", saved_path)
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total_time = time.time() - start_time
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logger.info("Sample %s, took %ss (%ss / image).", sample_index, total_time, (total_time) / len(images))
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saved_prompts_path = sd_output_manager.save_prompts()
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logger.info("Saved prompts: %s", saved_prompts_path)
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