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from __future__ import annotations |
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import argparse |
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import functools |
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import os |
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import pathlib |
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import sys |
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from typing import Callable |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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from io import BytesIO |
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from fastai.vision import * |
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from fastai.utils.mem import * |
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from fastai.vision import load_learner |
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from core import FeatureLoss |
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import torchvision.transforms as T |
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ORIGINAL_REPO_URL = 'https://github.com/vijishmadhavan/ArtLine' |
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TITLE = 'vijishmadhavan/ArtLine' |
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DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}. |
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""" |
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ARTICLE = """ |
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""" |
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MODEL_REPO = 'hylee/artline_model' |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--device', type=str, default='cpu') |
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parser.add_argument('--theme', type=str) |
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parser.add_argument('--live', action='store_true') |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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parser.add_argument('--allow-flagging', type=str, default='never') |
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parser.add_argument('--allow-screenshot', action='store_true') |
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return parser.parse_args() |
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def load_model(): |
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dir = 'model' |
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name = 'ArtLine_650.pkl' |
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model_path = huggingface_hub.hf_hub_download(MODEL_REPO, |
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name, |
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cache_dir=dir, |
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force_filename=name) |
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return model_path |
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def run( |
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image, |
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learn, |
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) -> tuple[PIL.Image.Image]: |
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img = PIL.Image.open(image.name) |
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img_t = T.ToTensor()(img) |
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img_fast = Image(img_t) |
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p, img_hr, b = learn.predict(img_fast) |
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r = np.uint8(np.clip(image2np(img_hr), 0, 1) * 255) |
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return PIL.Image.fromarray(r) |
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learn = None |
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def main(): |
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gr.close_all() |
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args = parse_args() |
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model_path = load_model() |
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def load_pkl(self) -> Any: |
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global learn |
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path = Path("model") |
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learn = load_learner(path, 'ArtLine_650.pkl') |
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PklLoader = type('PklLoader', (), {"load_pkl": load_pkl}) |
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pl = PklLoader() |
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pl.load_pkl() |
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func = functools.partial(run, learn=learn) |
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func = functools.update_wrapper(func, run) |
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gr.Interface( |
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func, |
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[ |
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gr.inputs.Image(type='file', label='Input Image'), |
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], |
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[ |
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gr.outputs.Image( |
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type='pil', |
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label='Result'), |
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], |
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theme=args.theme, |
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title=TITLE, |
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description=DESCRIPTION, |
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article=ARTICLE, |
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allow_screenshot=args.allow_screenshot, |
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allow_flagging=args.allow_flagging, |
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live=args.live, |
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).launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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