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| #!/usr/bin/env python3 | |
| import gradio as gr | |
| import os | |
| from clip_interrogator import Config, Interrogator | |
| from huggingface_hub import hf_hub_download | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| MODELS = ['ViT-L (best for Stable Diffusion 1.*)', 'ViT-H (best for Stable Diffusion 2.*)'] | |
| # download preprocessed files | |
| PREPROCESS_FILES = [ | |
| 'ViT-H-14_laion2b_s32b_b79k_artists.pkl', | |
| 'ViT-H-14_laion2b_s32b_b79k_flavors.pkl', | |
| 'ViT-H-14_laion2b_s32b_b79k_mediums.pkl', | |
| 'ViT-H-14_laion2b_s32b_b79k_movements.pkl', | |
| 'ViT-H-14_laion2b_s32b_b79k_trendings.pkl', | |
| 'ViT-L-14_openai_artists.pkl', | |
| 'ViT-L-14_openai_flavors.pkl', | |
| 'ViT-L-14_openai_mediums.pkl', | |
| 'ViT-L-14_openai_movements.pkl', | |
| 'ViT-L-14_openai_trendings.pkl', | |
| ] | |
| print("Download preprocessed cache files...") | |
| for file in PREPROCESS_FILES: | |
| path = hf_hub_download(repo_id="pharma/ci-preprocess", filename=file, cache_dir="cache") | |
| cache_path = os.path.dirname(path) | |
| # load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14 | |
| config = Config(cache_path=cache_path, clip_model_path="cache", clip_model_name="ViT-L-14/openai") | |
| ci_vitl = Interrogator(config) | |
| ci_vitl.clip_model = ci_vitl.clip_model.to("cpu") | |
| # load ViT-H https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K | |
| config.blip_model = ci_vitl.blip_model | |
| config.clip_model_name = "ViT-H-14/laion2b_s32b_b79k" | |
| ci_vith = Interrogator(config) | |
| ci_vith.clip_model = ci_vith.clip_model.to("cpu") | |
| def image_analysis(image, clip_model_name): | |
| # move selected model to GPU and other model to CPU | |
| if clip_model_name == MODELS[0]: | |
| ci_vith.clip_model = ci_vith.clip_model.to("cpu") | |
| ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device) | |
| ci = ci_vitl | |
| else: | |
| ci_vitl.clip_model = ci_vitl.clip_model.to("cpu") | |
| ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device) | |
| ci = ci_vith | |
| image = image.convert('RGB') | |
| image_features = ci.image_to_features(image) | |
| top_mediums = ci.mediums.rank(image_features, 5) | |
| top_artists = ci.artists.rank(image_features, 5) | |
| top_movements = ci.movements.rank(image_features, 5) | |
| top_trendings = ci.trendings.rank(image_features, 5) | |
| top_flavors = ci.flavors.rank(image_features, 5) | |
| medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))} | |
| artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))} | |
| movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))} | |
| trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))} | |
| flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))} | |
| return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks | |
| def image_to_prompt(image, clip_model_name, mode): | |
| # move selected model to GPU and other model to CPU | |
| if clip_model_name == MODELS[0]: | |
| ci_vith.clip_model = ci_vith.clip_model.to("cpu") | |
| ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device) | |
| ci = ci_vitl | |
| else: | |
| ci_vitl.clip_model = ci_vitl.clip_model.to("cpu") | |
| ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device) | |
| ci = ci_vith | |
| ci.config.blip_num_beams = 64 | |
| ci.config.chunk_size = 2048 | |
| ci.config.flavor_intermediate_count = 2048 if clip_model_name == MODELS[0] else 1024 | |
| image = image.convert('RGB') | |
| if mode == 'best': | |
| prompt = ci.interrogate(image) | |
| elif mode == 'classic': | |
| prompt = ci.interrogate_classic(image) | |
| elif mode == 'fast': | |
| prompt = ci.interrogate_fast(image) | |
| elif mode == 'negative': | |
| prompt = ci.interrogate_negative(image) | |
| return prompt, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| TITLE = """ | |
| <div style="text-align: center; max-width: 650px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| CLIP Interrogator | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Want to figure out what a good prompt might be to create new images like an existing one?<br>The CLIP Interrogator is here to get you answers! | |
| </p> | |
| <p>You can skip the queue by duplicating this space and upgrading to gpu in settings: <a style='display:inline-block' href='https://huggingface.co/spaces/pharma/CLIP-Interrogator?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p> | |
| </div> | |
| """ | |
| ARTICLE = """ | |
| <div style="text-align: center; max-width: 650px; margin: 0 auto;"> | |
| <p> | |
| Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a> | |
| and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a> | |
| from pixabay.com | |
| </p> | |
| <p> | |
| Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb">Google Colab</a> | |
| </p> | |
| <p> | |
| Has this been helpful to you? Follow me on twitter | |
| <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a><br> | |
| and check out more tools at my | |
| <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a> | |
| </p> | |
| </div> | |
| """ | |
| CSS = """ | |
| #col-container {margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| .animate-spin { | |
| animation: spin 1s linear infinite; | |
| } | |
| @keyframes spin { | |
| from { transform: rotate(0deg); } | |
| to { transform: rotate(360deg); } | |
| } | |
| #share-btn-container { | |
| display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
| } | |
| #share-btn { | |
| all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| """ | |
| def analyze_tab(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| image = gr.Image(type='pil', label="Image") | |
| model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model') | |
| with gr.Row(): | |
| medium = gr.Label(label="Medium", num_top_classes=5) | |
| artist = gr.Label(label="Artist", num_top_classes=5) | |
| movement = gr.Label(label="Movement", num_top_classes=5) | |
| trending = gr.Label(label="Trending", num_top_classes=5) | |
| flavor = gr.Label(label="Flavor", num_top_classes=5) | |
| button = gr.Button("Analyze", api_name="image-analysis") | |
| button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor]) | |
| with gr.Blocks(css=CSS) as block: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(TITLE) | |
| with gr.Tab("Prompt"): | |
| with gr.Row(): | |
| input_image = gr.Image(type='pil', elem_id="input-img") | |
| with gr.Column(): | |
| input_model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model') | |
| input_mode = gr.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Mode') | |
| submit_btn = gr.Button("Submit", api_name="image-to-prompt") | |
| output_text = gr.Textbox(label="Output", elem_id="output-txt") | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon = gr.HTML(community_icon_html, visible=False) | |
| loading_icon = gr.HTML(loading_icon_html, visible=False) | |
| share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) | |
| examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']] | |
| ex = gr.Examples( | |
| examples=examples, | |
| fn=image_to_prompt, | |
| inputs=[input_image, input_model, input_mode], | |
| outputs=[output_text, share_button, community_icon, loading_icon], | |
| cache_examples=True, | |
| run_on_click=True | |
| ) | |
| ex.dataset.headers = [""] | |
| with gr.Tab("Analyze"): | |
| analyze_tab() | |
| gr.HTML(ARTICLE) | |
| submit_btn.click( | |
| fn=image_to_prompt, | |
| inputs=[input_image, input_model, input_mode], | |
| outputs=[output_text, share_button, community_icon, loading_icon] | |
| ) | |
| share_button.click(None, [], [], _js=share_js) | |
| block.queue(max_size=64).launch(show_api=False) | |