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import os
import gradio as gr
from model import models
from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
change_model, warm_model, get_model_info_md, loaded_models,
get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en)
max_images = 8
MAX_SEED = 2**32-1
load_models(models)
css = """
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""
def set_token(token):
os.environ["HF_TOKEN"] = token # Сохранение токена в переменной окружения
return f"✅ Токен сохранен!"
# Основной интерфейс Gradio
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
with gr.Tab("Token Input"): # Новая вкладка для ввода токена
gr.Markdown("# 🔑 Введите ваш HF_TOKEN")
with gr.Row():
token_input = gr.Textbox(label="HF_TOKEN", type="password", placeholder="Введите ваш токен...")
save_button = gr.Button("Сохранить")
output_text = gr.Textbox(label="Статус", interactive=False)
save_button.click(set_token, inputs=[token_input], outputs=[output_text])
with gr.Tab("Image Generator"):
with gr.Row():
with gr.Column(scale=10):
with gr.Group():
prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
with gr.Accordion("Advanced options", open=False):
neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
with gr.Row():
width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
with gr.Row():
cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
with gr.Row():
positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
with gr.Row():
image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
trans_prompt = gr.Button(value="Translate 📝", variant="secondary", size="sm", scale=2)
clear_prompt = gr.Button(value="Clear 🗑️", variant="secondary", size="sm", scale=1)
with gr.Row():
run_button = gr.Button("Generate Image", variant="primary", scale=8)
random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
stop_button = gr.Button('Stop', interactive=False, variant="stop", scale=1)
with gr.Group():
model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
with gr.Column(scale=10):
with gr.Group():
with gr.Row():
output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
show_download_button=True, show_share_button=False, show_label=False,
interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(max_images)]
with gr.Group():
results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
container=True, format="png", object_fit="cover", columns=2, rows=2)
image_files = gr.Files(label="Download", interactive=False)
clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
with gr.Column():
examples = gr.Examples(
examples = [
["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
["kafuu chino, 1girl, solo"],
["1girl"],
["beautiful sunset"],
],
inputs=[prompt],
cache_examples=False,
)
with gr.Tab("PNG Info"):
def extract_exif_data(image):
if image is None: return ""
try:
metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
for key in metadata_keys:
if key in image.info:
return image.info[key]
return str(image.info)
except Exception as e:
return f"Error extracting metadata: {str(e)}"
with gr.Row():
with gr.Column():
image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
with gr.Column():
result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
image_metadata.change(
fn=extract_exif_data,
inputs=[image_metadata],
outputs=[result_metadata],
)
gr.Markdown(
f"""This demo was created in reference to the following demos.<br>
[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood),
[Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL),
[Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
"""
)
gr.DuplicateButton(value="Duplicate Space")
gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")
gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
model_name.change(change_model, [model_name], [model_info], queue=True, show_api=True)\
.success(warm_model, [model_name], None, queue=True, show_api=True)
for i, o in enumerate(output):
img_i = gr.Number(i, visible=False)
image_num.change(lambda i, n: gr.update(visible=(i < n)), [img_i, image_num], o, show_api=True)
gen_event = gr.on(triggers=[run_button.click, prompt.submit],
fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
positive_prefix, positive_suffix, negative_prefix, negative_suffix],
outputs=[o], queue=True, show_api=False)
gen_event2 = gr.on(triggers=[random_button.click],
fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
positive_prefix, positive_suffix, negative_prefix, negative_suffix],
outputs=[o], queue=True, show_api=False)
o.change(save_gallery, [o, results], [results, image_files], show_api=False)
stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)
clear_prompt.click(lambda: (None, None), None, [prompt, neg_prompt], queue=True, show_api=True)
clear_results.click(lambda: (None, None), None, [results, image_files], queue=True, show_api=True)
recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
[positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=True, show_api=True)
seed_rand.click(randomize_seed, None, [seed], queue=True, show_api=True)
trans_prompt.click(translate_to_en, [prompt], [prompt], queue=True, show_api=True)\
.then(translate_to_en, [neg_prompt], [neg_prompt], queue=True, show_api=True)
demo.queue(default_concurrency_limit=240, max_size=240)
demo.launch(max_threads=400, ssr_mode=True)
# https://github.com/gradio-app/gradio/issues/6339
demo.queue(concurrency_count=50)
demo.launch()
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