app.py

import numpy as np
import random
import torch
import gradio as gr
from diffusers import DiffusionPipeline
from PIL import Image
import io

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
MAX_IMAGE_SIZE = 1024

def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    prompt = f"{prompt_part1} {color} colored plain {dress_type} with {design} design, {prompt_part5}"

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    try:
        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]
        print("Image generated successfully.")  # Debug: Confirm image generation
        return image
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

examples = [
    ["red", "t-shirt", "yellow stripes"],
    ["blue", "hoodie", "minimalist"],
    ["red", "sweatshirt", "geometric design"],
]

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

power_device = "GPU" if torch.cuda.is_available() else "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():
            prompt_part1 = gr.Textbox(value="a single", label="Prompt Part 1", show_label=False, interactive=False, container=False, elem_id="prompt_part1", visible=False)
            prompt_part2 = gr.Textbox(label="color", show_label=False, max_lines=1, placeholder="color (e.g., color category)", container=False)
            prompt_part3 = gr.Textbox(label="dress_type", show_label=False, max_lines=1, placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)", container=False)
            prompt_part4 = gr.Textbox(label="design", show_label=False, max_lines=1, placeholder="design", container=False)
            prompt_part5 = gr.Textbox(value="hanging on the plain grey wall", label="Prompt Part 5", show_label=False, interactive=False, container=False, elem_id="prompt_part5", visible=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.Textbox(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=512)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
            with gr.Row():
                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=12, step=1, value=2)
        
        gr.Examples(examples=examples, inputs=[prompt_part2, prompt_part3, prompt_part4])

    def run_infer():
        output_image = infer(
            prompt_part1.value,
            prompt_part2.value,
            prompt_part3.value,
            prompt_part4.value,
            prompt_part5.value,
            negative_prompt.value,
            seed.value,
            randomize_seed.value,
            width.value,
            height.value,
            guidance_scale.value,
            num_inference_steps.value
        )
        return output_image

    run_button.click(fn=run_infer, outputs=result)

demo.queue().launch()