mirror of
https://github.com/pharmapsychotic/clip-interrogator-ext.git
synced 2026-02-06 00:10:05 +00:00
119 lines
5.0 KiB
Python
119 lines
5.0 KiB
Python
import gradio as gr
|
|
import open_clip
|
|
import clip_interrogator
|
|
from clip_interrogator import Config, Interrogator
|
|
|
|
from modules import devices, script_callbacks
|
|
|
|
__version__ = '0.0.1'
|
|
|
|
ci = None
|
|
|
|
def load(clip_model_name):
|
|
global ci
|
|
if ci is None:
|
|
print(f"Loading CLIP Interrogator {clip_interrogator.__version__}...")
|
|
ci = Interrogator(Config(device=devices.get_optimal_device(), clip_model_name=clip_model_name))
|
|
if clip_model_name != ci.config.clip_model_name:
|
|
ci.config.clip_model_name = clip_model_name
|
|
ci.load_clip_model()
|
|
ci.blip_model = ci.blip_model.to(devices.get_optimal_device())
|
|
ci.clip_model = ci.clip_model.to(devices.get_optimal_device())
|
|
|
|
def unload():
|
|
global ci
|
|
if ci is not None:
|
|
print("Offloading CLIP Interrogator...")
|
|
ci.blip_model = ci.blip_model.to("cpu")
|
|
ci.clip_model = ci.clip_model.to("cpu")
|
|
devices.torch_gc()
|
|
|
|
def get_models():
|
|
return ['/'.join(x) for x in open_clip.list_pretrained()]
|
|
|
|
def image_analysis(image, clip_model_name):
|
|
load(clip_model_name)
|
|
|
|
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, clip_model_name):
|
|
load(clip_model_name)
|
|
|
|
image = image.convert('RGB')
|
|
if mode == 'best':
|
|
return ci.interrogate(image)
|
|
elif mode == 'classic':
|
|
return ci.interrogate_classic(image)
|
|
elif mode == 'fast':
|
|
return ci.interrogate_fast(image)
|
|
elif mode == 'negative':
|
|
return ci.interrogate_negative(image)
|
|
|
|
def prompt_tab():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
image = gr.Image(type='pil', label="Image")
|
|
with gr.Column():
|
|
mode = gr.Radio(['best', 'fast', 'classic', 'negative'], label='Mode', value='best')
|
|
model = gr.Dropdown(get_models(), value='ViT-L-14/openai', label='CLIP Model')
|
|
prompt = gr.Textbox(label="Prompt")
|
|
with gr.Row():
|
|
button = gr.Button("Generate", variant='primary')
|
|
unload_button = gr.Button("Unload")
|
|
button.click(image_to_prompt, inputs=[image, mode, model], outputs=prompt)
|
|
unload_button.click(unload)
|
|
|
|
def analyze_tab():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
image = gr.Image(type='pil', label="Image")
|
|
model = gr.Dropdown(get_models(), value='ViT-L-14/openai', 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", variant='primary')
|
|
button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor])
|
|
|
|
def about_tab():
|
|
gr.Markdown("## 🕵️♂️ CLIP Interrogator 🕵️♂️")
|
|
gr.Markdown("*Want to figure out what a good prompt might be to create new images like an existing one? The CLIP Interrogator is here to get you answers!*")
|
|
gr.Markdown("## Notes")
|
|
gr.Markdown(
|
|
"* For best prompts with Stable Diffusion 1.* choose the **ViT-L-14/openai** model.\n"
|
|
"* For best prompts with Stable Diffusion 2.* choose the **ViT-H-14/laion2b_s32b_b79k** model.\n"
|
|
"* When you are done click the **Unload** button to free up memory."
|
|
)
|
|
gr.Markdown("## Github")
|
|
gr.Markdown("If you have any issues please visit [CLIP Interrogator on Github](https://github.com/pharmapsychotic/clip-interrogator) and drop a star if you like it!")
|
|
gr.Markdown(f"<br><br>CLIP Interrogator version: {clip_interrogator.__version__}<br>Extension version: {__version__}")
|
|
|
|
def add_tab():
|
|
with gr.Blocks(analytics_enabled=False) as ui:
|
|
with gr.Tab("Prompt"):
|
|
prompt_tab()
|
|
with gr.Tab("Analyze"):
|
|
analyze_tab()
|
|
with gr.Tab("About"):
|
|
about_tab()
|
|
|
|
return [(ui, "Interrogator", "interrogator")]
|
|
|
|
script_callbacks.on_ui_tabs(add_tab) |