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import gradio as gr
import numpy as np
import torch
from diffusers import DiffusionPipeline
import re
# Устройство и параметры загрузки модели
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
# Регулярное выражение для проверки корректности модели
VALID_REPO_ID_REGEX = re.compile(r"^[a-zA-Z0-9._\-]+/[a-zA-Z0-9._\-]+$")
def is_valid_repo_id(repo_id):
return bool(VALID_REPO_ID_REGEX.match(repo_id)) and not repo_id.endswith(('-', '.'))
# Изначально загружаем модель по умолчанию
model_repo_id = "CompVis/stable-diffusion-v1-4"
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
# --- Загрузка LoRA (1) ---
pipe.load_lora_weights("AnastasiaSh/sticker-cat-lora3")
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(
model,
prompt,
negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
progress=gr.Progress(track_tqdm=True),
):
global model_repo_id, pipe
# Проверяем и загружаем новую модель, если она изменена
if model != model_repo_id:
if not is_valid_repo_id(model):
raise gr.Error(f"Некорректный идентификатор модели: '{model}'. Проверьте название.")
try:
new_pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch_dtype).to(device)
# --- Загрузка LoRA (2) ---
new_pipe.load_lora_weights("AnastasiaSh/sticker-cat-lora3")
pipe = new_pipe
model_repo_id = model
except Exception as e:
raise gr.Error(f"Не удалось загрузить модель '{model}'.\nОшибка: {e}")
# Генератор случайных чисел для детерминированности
generator = torch.Generator(device=device).manual_seed(seed)
# Генерация изображения
try:
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
except Exception as e:
raise gr.Error(f"Ошибка при генерации изображения: {e}")
return image, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("# Text-to-Image App")
model = gr.Textbox(
label="Model",
value="CompVis/stable-diffusion-v1-4", # Значение по умолчанию
interactive=True
)
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=20,
)
run_button = gr.Button("Run", variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
gr.Examples(examples=examples, inputs=[prompt])
run_button.click(
infer,
inputs=[
model,
prompt,
negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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