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"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
DEFAULT_IMAGE_SIZE = 512
MAX_IMAGE_SIZE = 1024

# Define the default parts of the prompt
DEFAULT_PREFIX = "a single"
DEFAULT_SUFFIX = "hanging on the grey wall"
CATEGORIES = ["T-shirt", "Sweatshirt", "Shirt", "Hoodie"]
EXAMPLES = [
    ["T-shirt", "floral pattern"],
    ["Sweatshirt", "abstract design"],
    ["Shirt", "geometric shapes"],
    ["Hoodie", "urban graffiti"],
]

def infer(category, design, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    prompt = f"{DEFAULT_PREFIX} {category} with {design} {DEFAULT_SUFFIX}"
    
    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

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        Currently running on {power_device}.
        """)
        
        with gr.Row():
            category = gr.Dropdown(label="Category", choices=CATEGORIES, value=CATEGORIES[0])
            design = gr.Text(
                label="Design/Graphic",
                show_label=True,
                max_lines=1,
                placeholder="Enter design or graphic",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        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():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=DEFAULT_IMAGE_SIZE,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=DEFAULT_IMAGE_SIZE,
                )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=12,
                    step=1,
                    value=50,
                )
        
        gr.Examples(
            examples=EXAMPLES,
            inputs=[category, design]
        )

    run_button.click(
        fn=infer,
        inputs=[category, design, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result]
    )

demo.queue().launch()