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Update app.py
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app.py
CHANGED
@@ -1,56 +1,74 @@
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import gradio as gr
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import numpy as np
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import random
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# import spaces #[uncomment to use ZeroGPU]
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from diffusers import DiffusionPipeline
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float32
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pipe
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=False,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0, # Replace with defaults that work for your model
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=2, # Replace with defaults that work for your model
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)
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gr.Examples(examples=examples, inputs=[prompt])
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import DiffusionPipeline
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import re
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# Устройство и параметры загрузки модели
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device = "cuda" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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# Регулярное выражение для проверки корректности модели
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VALID_REPO_ID_REGEX = re.compile(r"^[a-zA-Z0-9._\-]+/[a-zA-Z0-9._\-]+$")
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def is_valid_repo_id(repo_id):
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return bool(VALID_REPO_ID_REGEX.match(repo_id)) and not repo_id.endswith(('-', '.'))
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# Изначально загружаем модель по умолчанию
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model_repo_id = "CompVis/stable-diffusion-v1-4"
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
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# Загружаем веса LoRA
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pipe.load_lora_weights("AnastasiaSh/sticker-cat-lora3")
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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def infer(
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model,
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prompt,
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negative_prompt,
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seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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global model_repo_id, pipe
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# Проверяем и загружаем новую модель, если она изменена
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if model != model_repo_id:
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if not is_valid_repo_id(model):
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raise gr.Error(f"Некорректный идентификатор модели: '{model}'. Проверьте название.")
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try:
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new_pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch_dtype).to(device)
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# Повторно загружаем LoRA для новой модели
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new_pipe.load_lora_weights("AnastasiaSh/sticker-cat-lora3")
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pipe = new_pipe
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model_repo_id = model
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except Exception as e:
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raise gr.Error(f"Не удалось загрузить модель '{model}'.\nОшибка: {e}")
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# Генератор случайных чисел для детерминированности
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generator = torch.Generator(device=device).manual_seed(seed)
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# Генерация изображения
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try:
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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).images[0]
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except Exception as e:
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raise gr.Error(f"Ошибка при генерации изображения: {e}")
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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("# Text-to-Image App")
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model = gr.Textbox(
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label="Model",
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value="CompVis/stable-diffusion-v1-4", # Значение по умолчанию
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interactive=True
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)
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=True,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=42,
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)
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=7.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=20,
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)
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run_button = gr.Button("Run", variant="primary")
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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gr.Examples(examples=examples, inputs=[prompt])
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run_button.click(
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infer,
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inputs=[
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model,
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prompt,
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negative_prompt,
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seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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],
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outputs=[result, seed],
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)
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if __name__ == "__main__":
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demo.launch()
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