52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import time
|
|
|
|
import modal
|
|
import util
|
|
|
|
stub = modal.Stub("run-stable-diffusion-cli")
|
|
stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "SDXLTxt2Img.run_inference")
|
|
|
|
|
|
@stub.local_entrypoint()
|
|
def main(
|
|
prompt: str,
|
|
height: int = 1024,
|
|
width: int = 1024,
|
|
samples: int = 5,
|
|
seed: int = -1,
|
|
upscaler: str = "",
|
|
use_face_enhancer: str = "False",
|
|
output_format: str = "png",
|
|
):
|
|
"""
|
|
This function is the entrypoint for the Runway CLI.
|
|
The function pass the given prompt to StableDiffusion on Modal,
|
|
gets back a list of images and outputs images to local.
|
|
"""
|
|
directory = util.make_directory()
|
|
seed_generated = seed
|
|
for i in range(samples):
|
|
if seed == -1:
|
|
seed_generated = util.generate_seed()
|
|
start_time = time.time()
|
|
images = stub.run_inference.remote(
|
|
prompt=prompt,
|
|
height=height,
|
|
width=width,
|
|
seed=seed_generated,
|
|
upscaler=upscaler,
|
|
use_face_enhancer=use_face_enhancer == "True",
|
|
output_format=output_format,
|
|
)
|
|
util.save_images(directory, images, seed_generated, i, output_format)
|
|
total_time = time.time() - start_time
|
|
print(f"Sample {i} took {total_time:.3f}s ({(total_time)/len(images):.3f}s / image).")
|
|
|
|
prompts: dict[str, int | str] = {
|
|
"prompt": prompt,
|
|
"height": height,
|
|
"width": width,
|
|
"samples": samples,
|
|
}
|
|
util.save_prompts(prompts)
|