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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 = False if torch.cuda.is_available() else 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: 15rem;
}
#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")
with gr.Row():
mode_input = gr.Radio(['best', 'classic', 'fast'], label='Select mode', value='best')
flavor_input = gr.Slider(minimum=2, maximum=24, step=2, value=4, label='best mode max flavors')
submit_btn = gr.Button("Submit")
output_text = gr.Textbox(label="Description Output", elem_id="output-txt")
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=True)
loading_icon = gr.HTML(loading_icon_html, visible=True)
share_button = gr.Button("Share with Community", elem_id="share-btn", visible=True)
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=False, 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], concurrency_limit=10, api_name="clipi2")
share_button.click(None, [], [], js=share_js)
block.queue(max_size=32).launch(show_api=False)