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#!/usr/bin/env python3 |
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import argparse |
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import open_clip |
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import torch |
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from clip_interrogator import Config, Interrogator |
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try: |
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import gradio as gr |
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except ImportError: |
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print("Gradio is not installed, please install it with 'pip install gradio'") |
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exit(1) |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-s', '--share', action='store_true', help='Create a public link') |
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args = parser.parse_args() |
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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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ci = Interrogator(Config(cache_path="cache", clip_model_path="cache")) |
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def image_analysis(image, clip_model_name): |
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if clip_model_name != ci.config.clip_model_name: |
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ci.config.clip_model_name = clip_model_name |
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ci.load_clip_model() |
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image = image.convert('RGB') |
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image_features = ci.image_to_features(image) |
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top_mediums = ci.mediums.rank(image_features, 5) |
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top_artists = ci.artists.rank(image_features, 5) |
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top_movements = ci.movements.rank(image_features, 5) |
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top_trendings = ci.trendings.rank(image_features, 5) |
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top_flavors = ci.flavors.rank(image_features, 5) |
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medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))} |
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artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))} |
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movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))} |
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trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))} |
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flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))} |
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return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks |
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def image_to_prompt(image, mode, clip_model_name): |
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if clip_model_name != ci.config.clip_model_name: |
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ci.config.clip_model_name = clip_model_name |
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ci.load_clip_model() |
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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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elif mode == 'fast': |
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return ci.interrogate_fast(image) |
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elif mode == 'negative': |
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return ci.interrogate_negative(image) |
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models = ['/'.join(x) for x in open_clip.list_pretrained()] |
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def prompt_tab(): |
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with gr.Column(): |
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with gr.Row(): |
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image = gr.Image(type='pil', label="Image") |
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with gr.Column(): |
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mode = gr.Radio(['best', 'fast', 'classic', 'negative'], label='Mode', value='best') |
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model = gr.Dropdown(models, value='ViT-L-14/openai', label='CLIP Model') |
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prompt = gr.Textbox(label="Prompt") |
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button = gr.Button("Generate prompt") |
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button.click(image_to_prompt, inputs=[image, mode, model], outputs=prompt) |
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def analyze_tab(): |
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with gr.Column(): |
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with gr.Row(): |
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image = gr.Image(type='pil', label="Image") |
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model = gr.Dropdown(models, value='ViT-L-14/openai', label='CLIP Model') |
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with gr.Row(): |
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medium = gr.Label(label="Medium", num_top_classes=5) |
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artist = gr.Label(label="Artist", num_top_classes=5) |
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movement = gr.Label(label="Movement", num_top_classes=5) |
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trending = gr.Label(label="Trending", num_top_classes=5) |
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flavor = gr.Label(label="Flavor", num_top_classes=5) |
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button = gr.Button("Analyze") |
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button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor]) |
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with gr.Blocks() as ui: |
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gr.Markdown("# <center>🕵️♂️ CLIP Interrogator 🕵️♂️</center>") |
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with gr.Tab("Prompt"): |
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prompt_tab() |
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with gr.Tab("Analyze"): |
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analyze_tab() |
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ui.launch(show_api=False, debug=True, share=args.share)
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