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| from share_btn import community_icon_html, loading_icon_html, share_js | |
| import os, subprocess | |
| import torch | |
| def setup(): | |
| install_cmds = [ | |
| ['pip', 'install', 'ftfy', 'gradio', 'regex', 'tqdm', 'transformers==4.21.2', 'timm', 'fairscale', 'requests'], | |
| ['pip', 'install', 'open_clip_torch'], | |
| ['pip', 'install', '-e', 'git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip'], | |
| ['git', 'clone', '-b', 'open-clip', 'https://github.com/pharmapsychotic/clip-interrogator.git'] | |
| ] | |
| for cmd in install_cmds: | |
| print(subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8')) | |
| setup() | |
| # download cache files | |
| print("Download preprocessed cache files...") | |
| CACHE_URLS = [ | |
| 'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_artists.pkl', | |
| 'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_flavors.pkl', | |
| 'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_mediums.pkl', | |
| 'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_movements.pkl', | |
| 'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_trendings.pkl', | |
| ] | |
| os.makedirs('cache', exist_ok=True) | |
| for url in CACHE_URLS: | |
| print(subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8')) | |
| import sys | |
| sys.path.append('src/blip') | |
| sys.path.append('clip-interrogator') | |
| import gradio as gr | |
| from clip_interrogator import Config, Interrogator | |
| config = Config() | |
| config.device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| config.blip_offload = True | |
| config.chunk_size = 2048 | |
| config.flavor_intermediate_count = 512 | |
| config.blip_num_beams = 64 | |
| ci = Interrogator(config) | |
| def inference(image, mode, best_max_flavors): | |
| image = image.convert('RGB') | |
| if mode == 'best': | |
| prompt_result = ci.interrogate(image, max_flavors=int(best_max_flavors)) | |
| print("mode best: " + prompt_result) | |
| return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| elif mode == 'classic': | |
| prompt_result = ci.interrogate_classic(image) | |
| print("mode classic: " + prompt_result) | |
| return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| else: | |
| prompt_result = ci.interrogate_fast(image) | |
| print("mode fast: " + prompt_result) | |
| return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| title = """ | |
| <div style="text-align: center; max-width: 500px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| margin-bottom: 10px; | |
| " | |
| > | |
| <h1 style="font-weight: 600; margin-bottom: 7px;"> | |
| CLIP Interrogator 2.1 | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;"> | |
| 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! | |
| <br />This version is specialized for producing nice prompts for use with Stable Diffusion 2.0 using the ViT-H-14 OpenCLIP model! | |
| </p> | |
| </div> | |
| """ | |
| article = """ | |
| <div style="text-align: center; max-width: 500px; margin: 0 auto;font-size: 94%;"> | |
| <p> | |
| Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/open-clip/clip_interrogator.ipynb">Google Colab</a> | |
| </p> | |
| <p> | |
| Has this been helpful to you? Follow Pharma on twitter | |
| <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a> | |
| and check out more tools at his | |
| <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a> | |
| </p> | |
| </div> | |
| """ | |
| css = ''' | |
| #col-container {max-width: 700px; 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; | |
| } | |
| ''' | |
| with gr.Blocks(css=css) as block: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| input_image = gr.Image(type='pil', elem_id="input-img") | |
| mode_input = gr.Radio(['best', 'classic', 'fast'], label='', value='best') | |
| flavor_input = gr.Number(value=4, label='best mode max flavors') | |
| submit_btn = gr.Button("Submit") | |
| 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=[['27E894C4-9375-48A1-A95D-CB2425416B4B.png', "best",4], ['DB362F56-BA98-4CA1-A999-A25AA94B723B.png',"fast",4]] | |
| ex = gr.Examples(examples=examples, fn=inference, inputs=[input_image, mode_input, flavor_input], outputs=[output_text, share_button, community_icon, loading_icon], cache_examples=True, run_on_click=True) | |
| ex.dataset.headers = [""] | |
| gr.HTML(article) | |
| submit_btn.click(fn=inference, inputs=[input_image,mode_input,flavor_input], outputs=[output_text, share_button, community_icon, loading_icon]) | |
| share_button.click(None, [], [], _js=share_js) | |
| block.queue(max_size=32,concurrency_count=20).launch(show_api=False) |