app.py

import gradio as gr
from main import DreamboothApp

app = DreamboothApp(model_path="stable_diffusion_weights")

def train(instance_images, instance_prompt, num_class_images, max_train_steps):
    app.train(instance_data_dir="instance_data",
              class_data_dir="class_data",
              instance_prompt=instance_prompt,
              class_prompt="photo of a person",
              num_class_images=num_class_images,
              max_train_steps=max_train_steps)
    return "Training completed. Model is ready for inference."

def inference(prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale, seed):
    app.load_model()
    images = app.inference(prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale, seed)
    return images

with gr.Blocks() as demo:
    gr.Markdown("# Stable Diffusion Dreambooth")
    with gr.Tab("Training"):
        with gr.Row():
            instance_images = gr.File(label="Upload Instance Images (5-10 images recommended)", file_count="multiple")
            with gr.Column():
                instance_prompt = gr.Textbox(label="Instance Prompt", placeholder="Enter the prompt for your instance images")
                num_class_images = gr.Number(label="Number of Class Images", value=50)
                max_train_steps = gr.Number(label="Maximum Training Steps", value=800)
                train_button = gr.Button("Train Model")
        train_output = gr.Textbox(label="Training Output")
        train_button.click(train, inputs=[instance_images, instance_prompt, num_class_images, max_train_steps], outputs=train_output)

    with gr.Tab("Inference"):
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here (optional)")
                with gr.Row():
                    num_samples = gr.Number(label="Number of Samples", value=1)
                    guidance_scale = gr.Number(label="Guidance Scale", value=7.5)
                with gr.Row():
                    height = gr.Number(label="Height", value=512)
                    width = gr.Number(label="Width", value=512)
                num_inference_steps = gr.Slider(label="Steps", value=50)
                seed = gr.Number(label="Seed (optional)", value=0)
                generate_button = gr.Button("Generate Images")
            with gr.Column():
                gallery = gr.Gallery(label="Generated Images")
        generate_button.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale, seed], outputs=gallery)

demo.launch()