app.py

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_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


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt1,
    prompt2,
    negative_prompt,
    seed,
    randomize_seed,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    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",
# ]

examples = [
    ["A dog cooking dinner in the kitchen", "An orange cat wearing sunglasses on a ship"],
]

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():
            prompt1 = gr.Text(
                label="Prompt_1",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt for the first image",
                container=False,
            )
        
        with gr.Row():
            prompt2 = gr.Text(
                label="Prompt_2",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt for the second image",
                container=False,
            )

        with gr.Row():
            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=False,
            )

            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():
                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=50,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt1, prompt2])
    gr.on(
        triggers=[run_button.click, prompt1.submit, prompt2.submit],
        fn=infer,
        inputs=[
            prompt1,
            prompt2,
            negative_prompt,
            seed,
            randomize_seed,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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