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#!/usr/bin/env python3 | |
import gradio as gr | |
from clip_interrogator import Config, Interrogator | |
# MODELS = ['ViT-L (best for Stable Diffusion 1.*)', 'ViT-H (best for Stable Diffusion 2.*)'] | |
# MODELS = ['ViT-L (best for Stable Diffusion 1.*)',] | |
# load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14 | |
from PIL import Image | |
from clip_interrogator import Config, Interrogator | |
ci = Interrogator(Config(clip_model_name="ViT-L-14/openai")) | |
def image_analysis(image): | |
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, mode): | |
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 | |
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") | |
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], | |
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_mode = gr.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Mode') | |
submit_btn = gr.Button("Submit") | |
output_text = gr.Textbox(label="Output", elem_id="output-txt") | |
examples=[['example01.jpg', 'best'], ['example02.jpg', 'best']] | |
ex = gr.Examples( | |
examples=examples, | |
fn=image_to_prompt, | |
inputs=[input_image, input_mode], | |
outputs=[output_text], | |
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_mode], | |
outputs=[output_text] | |
) | |
block.queue(max_size=64).launch(show_api=False) |