app.py

import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline

# Check device
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace with your fine-tuned LoRA/QLoRA model

torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Load pipeline
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 = 512  # Icons are smaller in size

def generate_icon(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    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 = [
    "Flat design shopping cart icon, blue background",
    "Minimalist envelope icon, black and white",
    "3D gradient camera lens icon, detailed"
]

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("# 🌟 Iconica: AI Icon Generator")
        
        with gr.Row():
            prompt = gr.Text(
                label="Icon Description",
                show_label=False,
                max_lines=1,
                placeholder="Enter your icon description",
                container=False,
            )
            run_button = gr.Button("Generate", scale=0, variant="primary")
        
        result = gr.Image(label="Generated Icon", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative Prompt",
                max_lines=1,
                placeholder="Avoid certain features",
                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=128,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=256,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=128,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=256,
                )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=5.0,
                )
                num_inference_steps = gr.Slider(
                    label="Inference Steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,
                )
        
        gr.Examples(examples=examples, inputs=[prompt])
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=generate_icon,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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