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89 lines
3.1 KiB
89 lines
3.1 KiB
#!/usr/bin/env python3 |
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import argparse |
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import csv |
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import open_clip |
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import os |
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import requests |
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import torch |
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from PIL import Image |
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from clip_interrogator import Interrogator, Config |
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def inference(ci, image, mode): |
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image = image.convert('RGB') |
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if mode == 'best': |
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return ci.interrogate(image) |
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elif mode == 'classic': |
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return ci.interrogate_classic(image) |
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else: |
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return ci.interrogate_fast(image) |
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def main(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-c', '--clip', default='ViT-L-14/openai', help='name of CLIP model to use') |
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parser.add_argument('-f', '--folder', help='path to folder of images') |
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parser.add_argument('-i', '--image', help='image file or url') |
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parser.add_argument('-m', '--mode', default='best', help='best, classic, or fast') |
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args = parser.parse_args() |
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if not args.folder and not args.image: |
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parser.print_help() |
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exit(1) |
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if args.folder is not None and args.image is not None: |
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print("Specify a folder or batch processing or a single image, not both") |
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exit(1) |
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# validate clip model name |
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models = ['/'.join(x) for x in open_clip.list_pretrained()] |
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if args.clip not in models: |
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print(f"Could not find CLIP model {args.clip}!") |
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print(f" available models: {models}") |
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exit(1) |
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# select device |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if not torch.cuda.is_available(): |
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print("CUDA is not available, using CPU. Warning: this will be very slow!") |
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# generate a nice prompt |
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config = Config(device=device, clip_model_name=args.clip) |
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ci = Interrogator(config) |
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# process single image |
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if args.image is not None: |
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image_path = args.image |
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if str(image_path).startswith('http://') or str(image_path).startswith('https://'): |
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image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB') |
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else: |
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image = Image.open(image_path).convert('RGB') |
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if not image: |
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print(f'Error opening image {image_path}') |
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exit(1) |
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print(inference(ci, image, args.mode)) |
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# process folder of images |
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elif args.folder is not None: |
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if not os.path.exists(args.folder): |
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print(f'The folder {args.folder} does not exist!') |
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exit(1) |
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files = [f for f in os.listdir(args.folder) if f.endswith('.jpg') or f.endswith('.png')] |
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prompts = [] |
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for file in files: |
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image = Image.open(os.path.join(args.folder, file)).convert('RGB') |
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prompt = inference(ci, image, args.mode) |
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prompts.append(prompt) |
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print(prompt) |
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if len(prompts): |
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csv_path = os.path.join(args.folder, 'desc.csv') |
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with open(csv_path, 'w', encoding='utf-8', newline='') as f: |
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w = csv.writer(f, quoting=csv.QUOTE_MINIMAL) |
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w.writerow(['image', 'prompt']) |
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for file, prompt in zip(files, prompts): |
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w.writerow([file, prompt]) |
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print(f"\n\n\n\nGenerated {len(prompts)} and saved to {csv_path}, enjoy!") |
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if __name__ == "__main__": |
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main()
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