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Upload 4 files
Browse files- css.py +87 -0
- grutils.py +60 -0
- inference.py +96 -0
- utils.py +87 -0
css.py
ADDED
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css = """
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:root, .dark{
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--checkbox-label-gap: 0.25em 0.1em;
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--section-header-text-size: 12pt;
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--block-background-fill: transparent;
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}
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.block.padded:not(.gradio-accordion) {
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padding: 0 !important;
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}
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div.gradio-container{
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max-width: unset !important;
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}
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.compact{
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background: transparent !important;
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padding: 0 !important;
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}
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div.form{
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border-width: 0;
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box-shadow: none;
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background: transparent;
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overflow: visible;
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gap: 0.5em;
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}
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.block.gradio-dropdown,
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.block.gradio-slider,
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.block.gradio-checkbox,
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.block.gradio-textbox,
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.block.gradio-radio,
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.block.gradio-checkboxgroup,
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.block.gradio-number,
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.block.gradio-colorpicker {
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border-width: 0 !important;
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box-shadow: none !important;
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}
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.gradio-dropdown label span:not(.has-info),
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.gradio-textbox label span:not(.has-info),
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.gradio-number label span:not(.has-info)
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{
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margin-bottom: 0;
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}
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.gradio-dropdown ul.options{
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z-index: 3000;
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min-width: fit-content;
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max-width: inherit;
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white-space: nowrap;
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}
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.gradio-dropdown ul.options li.item {
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padding: 0.05em 0;
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}
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.gradio-dropdown ul.options li.item.selected {
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background-color: var(--neutral-100);
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}
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.dark .gradio-dropdown ul.options li.item.selected {
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background-color: var(--neutral-900);
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}
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.gradio-dropdown div.wrap.wrap.wrap.wrap{
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box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
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}
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.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
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flex-wrap: unset;
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}
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.gradio-dropdown .single-select{
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white-space: nowrap;
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overflow: hidden;
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}
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.gradio-dropdown .token-remove.remove-all.remove-all{
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display: none;
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}
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.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
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display: flex;
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}
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.gradio-slider input[type="number"]{
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width: 6em;
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}
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.block.gradio-checkbox {
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margin: 0.75em 1.5em 0 0;
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}
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.gradio-html div.wrap{
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height: 100%;
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}
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div.gradio-html.min{
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min-height: 0;
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}
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#model_dd {
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width: 16%;
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}
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"""
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grutils.py
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@@ -0,0 +1,60 @@
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import gradio as gr
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from utils import extract_data, remove_id_and_ext
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from prodiapy import Custom
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import os
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pipe = Custom(os.getenv('PRODIA_API_KEY'))
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model_list = pipe.constant("/sd/models")
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model_names = {}
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for model_name in model_list:
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name_without_ext = remove_id_and_ext(model_name)
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model_names[name_without_ext] = model_name
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def update_btn_start():
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return [
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gr.update(visible=False),
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gr.update(visible=True)
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]
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def update_btn_end():
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return [
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gr.update(visible=True),
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gr.update(visible=False)
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]
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def switch_to_t2i():
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return gr.Tabs.update(selected="t2i")
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def send_to_txt2img(image):
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try:
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text = image.info['parameters']
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data = extract_data(text)
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if data['model'] in model_names:
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model = gr.update(value=model_names[data['model']])
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else:
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model = gr.update()
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result = [
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gr.update(value=data['prompt']),
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gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update(),
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gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update(),
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gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update(),
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gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update(),
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gr.update(value=int(data['w'])) if data['w'] is not None else gr.update(),
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gr.update(value=int(data['h'])) if data['h'] is not None else gr.update(),
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gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update(),
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model
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]
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return result
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except Exception as e:
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print(e)
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return
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inference.py
ADDED
@@ -0,0 +1,96 @@
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from prodiapy import Custom
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from prodiapy.util import load
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from PIL import Image
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from threading import Thread
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from utils import image_to_base64
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import gradio as gr
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import gradio_user_history as gr_user_history
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pipe = Custom(os.getenv("PRODIA_API_KEY"))
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def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count, profile: gr.OAuthProfile | None):
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total_images = []
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threads = []
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def generate_one_image():
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result = pipe.create(
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"/sd/generate",
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prompt=prompt,
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negative_prompt=negative_prompt,
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model=model,
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steps=steps,
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cfg_scale=cfg_scale,
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sampler=sampler,
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width=width,
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height=height,
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seed=seed
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)
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job = pipe.wait_for(result)
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total_images.append(job['imageUrl'])
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for x in range(batch_count):
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t = Thread(target=generate_one_image)
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threads.append(t)
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t.start()
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for t in threads:
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t.join()
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for image in total_images:
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gr_user_history.save_image(label=prompt, image=Image.open(load(image)), profile=profile)
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return gr.update(value=total_images, preview=False)
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def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed,
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batch_count):
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if input_image is None:
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return
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total_images = []
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threads = []
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def generate_one_image():
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result = pipe.create(
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"/sd/transform",
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imageData=image_to_base64(input_image),
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denoising_strength=denoising,
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prompt=prompt,
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negative_prompt=negative_prompt,
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model=model,
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steps=steps,
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cfg_scale=cfg_scale,
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sampler=sampler,
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width=width,
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height=height,
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seed=seed
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)
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job = pipe.wait_for(result)
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total_images.append(job['imageUrl'])
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for x in range(batch_count):
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t = Thread(target=generate_one_image)
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threads.append(t)
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t.start()
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for t in threads:
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t.join()
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return gr.update(value=total_images, preview=False)
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def upscale(image, scale, profile: gr.OAuthProfile | None):
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if image is None:
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return
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job = pipe.create(
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'/upscale',
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imageData=image_to_base64(image),
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resize=scale
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)
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image = pipe.wait_for(job)['imageUrl']
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gr_user_history.save_image(label=f'upscale by {scale}', image=Image.open(load(image)), profile=profile)
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return image
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utils.py
ADDED
@@ -0,0 +1,87 @@
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import re
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import base64
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import html
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from io import BytesIO
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def image_to_base64(image):
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# Convert the image to bytes
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buffered = BytesIO()
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image.save(buffered, format="PNG") # You can change format to PNG if needed
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# Encode the bytes to base64
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img_str = base64.b64encode(buffered.getvalue())
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return img_str.decode('utf-8') # Convert bytes to string
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def remove_id_and_ext(text):
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text = re.sub(r'\[.*\]$', '', text)
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extension = text[-12:].strip()
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if extension == "safetensors":
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text = text[:-13]
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elif extension == "ckpt":
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text = text[:-4]
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return text
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def extract_data(text):
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results = {}
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patterns = {
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'prompt': r'(.*)',
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'negative_prompt': r'Negative prompt: (.*)',
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'steps': r'Steps: (\d+),',
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'seed': r'Seed: (\d+),',
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'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
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'model': r'Model:\s*([^\s,]+)',
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'cfg_scale': r'CFG scale:\s*([\d\.]+)',
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'size': r'Size:\s*([0-9]+x[0-9]+)'
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}
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for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
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match = re.search(patterns[key], text)
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if match:
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results[key] = match.group(1)
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else:
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results[key] = None
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if results['size'] is not None:
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w, h = results['size'].split("x")
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results['w'] = w
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results['h'] = h
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else:
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results['w'] = None
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results['h'] = None
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return results
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def place_lora(current_prompt, lora_name):
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pattern = r"<lora:" + lora_name + r":.*?>"
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58 |
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59 |
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if re.search(pattern, current_prompt):
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yield re.sub(pattern, "", current_prompt)
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else:
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yield current_prompt + " <lora:" + lora_name + ":1> "
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63 |
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64 |
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def plaintext_to_html(text, classname=None):
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66 |
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content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
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67 |
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68 |
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return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
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69 |
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70 |
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71 |
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def get_exif_data(image):
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72 |
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items = image.info
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73 |
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74 |
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info = ''
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75 |
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for key, text in items.items():
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76 |
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info += f"""
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77 |
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<div>
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78 |
+
<p><b>{plaintext_to_html(str(key))}</b></p>
|
79 |
+
<p>{plaintext_to_html(str(text))}</p>
|
80 |
+
</div>
|
81 |
+
""".strip() + "\n"
|
82 |
+
|
83 |
+
if len(info) == 0:
|
84 |
+
message = "Nothing found in the image."
|
85 |
+
info = f"<div><p>{message}<p></div>"
|
86 |
+
|
87 |
+
return info
|