app.py

import gradio as gr
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler
import torch

# Загрузка модели
def load_model(model_id, scheduler_name):
    pipe = StableDiffusionPipeline.from_pretrained(model_id) #torch_dtype=torch.float16
    pipe.to("cpu") #gpu 

    # Установка шедулера
    if scheduler_name == "DPM":
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    elif scheduler_name == "Euler":
        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)

    return pipe

# Генерация изображения
def generate_image(
    model_id: str,
    prompt: str,
    negative_prompt: str,
    seed: int,
    guidance_scale: float,
    num_inference_steps: int,
    scheduler_name: str,
    height: int,
    width: int
):
    # Установка начального состояния (seed)
    generator = torch.manual_seed(seed)
    
    # Загрузка модели
    pipe = load_model(model_id, scheduler_name)

    # Генерация
    image = pipe(
        prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        height=height,
        width=width
    ).images[0]

    return image

# Интерфейс Gradio
with gr.Blocks() as demo:
    gr.Markdown("## Домашнее задание 3. Часть 1. Знакомство с Gradio и HuggingFace.")
    
    with gr.Row():
            model_id = gr.Textbox(label="Model ID", value="CompVis/stable-diffusion-v1-4")
            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
            seed = gr.Number(label="Seed", value=42, precision=0)
            guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7)
            num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=50, value=20)
            scheduler_name = gr.Dropdown(label='Sheduler', choices=["DPM", "Euler"], value="DPM")
            heigth = gr.Slider(label="Heigth", minimum=256, maximum=1024, step=64, value=512)
            width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=512)
            output = gr.Image(label="Generated Image")
            submit = gr.Button("Generate")

    def reset_inputs():
        return "", "", 42, 7, 20, "DPM", 512, 512, None

    submit.click(
        fn=generate_image,
        inputs=[model_id, prompt, negative_prompt, seed, guidance_scale, num_inference_steps, scheduler_name, heigth, width],
        outputs=output,
    )

    next_generation = gr.Button("Next generation")
    next_generation.click(
        fn=reset_inputs,
        inputs=[],
        outputs=[prompt, negative_prompt, seed, guidance_scale, num_inference_steps, scheduler_name, heigth, width],
    )

# Запуск
if __name__ == "__main__":
    demo.launch()