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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 preprocesed file
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 inference(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)
else:
prompt = ci.interrogate_fast(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 some ideas for your next remodel?<br>The Remodel Dreamer will create a prompt of your current home or room to create some amazing spaces!
</p>
</div>
"""
ARTICLE = """
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
</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")
input_model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
input_mode = gr.Radio(['best', 'fast'], value='best', label='Mode')
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=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
ex = gr.Examples(
examples=examples,
fn=inference,
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 = [""]
gr.HTML(ARTICLE)
submit_btn.click(
fn=inference,
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=32).launch(show_api=False)
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