app.py

import gradio as gr
import numpy as np
import random

from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

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

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

    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
    return pipe.to(device)

# Initialize with default model
pipe = load_pipeline("CompVis/stable-diffusion-v1-4")

available_models = [
    "CompVis/stable-diffusion-v1-4",
    "runwayml/stable-diffusion-v1-5",
    "stabilityai/stable-diffusion-2-1",
    "prompthero/openjourney",
    
]

def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=None,
):
    global pipe

    if model_id:
        pipe = load_pipeline(model_id)

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

    # Ensure width and height are divisible by 8
    width = max(256, (width // 8) * 8)
    height = max(256, (height // 8) * 8)

    # Set default value if num_inference_steps is None
    if num_inference_steps is None:
        num_inference_steps = 20

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

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=int(num_inference_steps),  # Ensure it's an integer
        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;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template with Model Selection")

        model_id = gr.Dropdown(
            label="Model Selection",
            choices=available_models,
            value="CompVis/stable-diffusion-v1-4",
        )

        prompt = gr.Text(
            label="Prompt",
            show_label=True,
            max_lines=1,
            placeholder="Enter your prompt",
        )

        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
        )

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

        guidance_scale = gr.Slider(
            label="Guidance scale",
            minimum=0.0,
            maximum=20.0,
            step=0.1,
            value=7.0,
        )

        num_inference_steps = gr.Slider(
            label="Number of inference steps",
            minimum=1,
            maximum=100,
            step=1,
            value=20,
        )

        with gr.Row():
            width = gr.Slider(
                label="Width",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=8,
                value=512,
            )

            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=8,
                value=512,
            )

        run_button = gr.Button("Run", scale=0, variant="primary")
        result = gr.Image(label="Result", show_label=False)

        gr.Examples(examples=examples, inputs=[prompt])

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

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