app.py

import gradio as gr
import numpy as np
import random

#import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
import gc

from huggingface_hub import HfApi
# Создаем экземпляр API
api = HfApi()
# Ищем модели с ключевым словом "diffusers"
diff_model_names = [model.modelId for model in api.list_models(filter="diffusers")]


device = "cuda" if torch.cuda.is_available() else "cpu"
# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


model_names = [
    "stabilityai/sdxl-turbo",
    "CompVis/stable-diffusion-v1-4",
]

pipe = None

#@spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    global pipe
    if pipe is None:
        pipe = DiffusionPipeline.from_pretrained(model_id, dtype=torch_dtype)
        pipe = pipe.to(device)

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    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]

    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;
}
"""

def model_changed(selected_value):
    global pipe
    if pipe is not None:
        # Clear memory
        del pipe
        gc.collect()
        torch.cuda.empty_cache()
        pipe = None

    if selected_value not in model_names:
        model_names.append(selected_value)

    return gr.Dropdown(choices=model_names, value=selected_value)

def current_model_changed(selected_value):
    return gr.Label(label="Current model", value=f"{selected_value}")


with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        title = gr.Markdown(" # Text-to-Image Gradio Template")
        output = gr.Label(label="Current model", value=model_names[0])
        model_id = gr.Dropdown(model_names, value=model_names[0],
                               label="Select model", allow_custom_value=True)
        model_id.change(fn=model_changed, inputs=model_id, outputs=model_id)
        model_id.change(fn=current_model_changed, inputs=model_id, outputs=output)

        with gr.Row():

            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
                value='cute_animal',
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
                value='cat, dog',
           )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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