#!/usr/bin/env python3 import gradio as gr from clip_interrogator import Config, Interrogator 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.*)'] # load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14 config = Config(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 ci = ci_vitl 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 = ci_vitl 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]) examples=[['example01.jpg', MODELS[0]], ['example02.jpg', MODELS[0]]] ex = gr.Examples( examples=examples, fn=image_analysis, inputs=[input_image, input_model], outputs=[medium, artist, movement, trending, flavor], cache_examples=True, run_on_click=True ) ex.dataset.headers = [""] 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)