app.py

import logging
import gradio as gr
import numpy as np
import random

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

device = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_REPO_IDS = ["stable-diffusion-v1-5/stable-diffusion-v1-5", 
                  "black-forest-labs/FLUX.1-dev",
                  "black-forest-labs/FLUX.1-schnell",
                  "stabilityai/sdxl-turbo", 
                  "stabilityai/stable-diffusion-xl-base-1.0",]
DEFAULT_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

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


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    model_repo_ids = [DEFAULT_MODEL_REPO_ID],
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    images = []
    for model_repo_id in model_repo_ids:
        try:
            image = None
            pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
            pipe = pipe.to(device)
            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]
            images.append(image)
        except Exception as e:
            logging.error(f"Error generating image using model {model_repo_id}", exc_info=e)

    return images, seed


examples = [
    "Local Pizzeria perspective from the table with a pizza and a glass of wine in focus and the background is a bit blared. Style should be as if a customer took the picture using his phone.",
    "A butcher in a jungle, cold color palette, muted colors, detailed, 4k",
    "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 Gradio Template")

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=4,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")
        images = gr.Gallery(label="Generated Images")

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=2,
                placeholder="people faces, text ",
                visible=False,
            )

            model_repo_ids = gr.Dropdown(
                choices=MODEL_REPO_IDS,
                multiselect = True,
                value = [MODEL_REPO_IDS[0]]
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            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=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # 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=0.0,  # 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=2,  # 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=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            model_repo_ids
        ],
        outputs=[images, seed],
    )

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