app.py

import gradio as gr
from diffusers import DiffusionPipeline
import torch

# Load the pipeline with optimizations for CPU
pipeline = DiffusionPipeline.from_pretrained(
    "John6666/t-ponynai3-v6-sdxl",
    torch_dtype=torch.float16,  # Use FP16 precision if supported
    safety_checker=None,        # Disable safety checker for faster performance
).to("cpu")

# Enable attention slicing for memory management
pipeline.enable_attention_slicing()

def generate_image(prompt, negative_prompt, progress=gr.Progress()):
    num_inference_steps = 20  # Set number of inference steps
    
    # Track progress for each step
    for i in range(num_inference_steps):
        progress(i / num_inference_steps)  # Update progress bar
        # Perform generation step by step (simulate the process)
        image = pipeline(prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps).images[0]
    
    return image

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Text-to-Image Generator with John6666/t-ponynai3-v6-sdxl models")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Enter your prompt", placeholder="Describe the image you want to generate")
            negative_prompt = gr.Textbox(label="Enter negative prompt", placeholder="Describe what you want to avoid")
            generate_button = gr.Button("Generate")
        
        with gr.Column():
            output_image = gr.Image(label="Generated Image")
    
    # Add the progress bar component and connect it with the generate_image function
    generate_button.click(fn=generate_image, inputs=[prompt, negative_prompt], outputs=output_image)

# Launch the Gradio app
demo.launch()