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# app.py
import os
import gradio as gr
from huggingface_hub import HfFolder
def launch_gradio_app(fine_tune_model, load_model, generate_images, push_to_huggingface, repo_name):
with gr.Blocks() as demo:
gr.Markdown("# Dreambooth App")
with gr.Tab("Fine-tune Model"):
with gr.Row():
instance_images = gr.File(label="Instance Images", file_count="multiple")
class_images = gr.File(label="Class Images", file_count="multiple")
with gr.Row():
instance_prompt = gr.Textbox(label="Instance Prompt")
class_prompt = gr.Textbox(label="Class Prompt")
with gr.Row():
num_train_steps = gr.Number(label="Number of Training Steps", value=800)
fine_tune_button = gr.Button("Fine-tune Model")
with gr.Tab("Generate Images"):
with gr.Row():
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Prompt")
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="Number of Inference Steps", value=50, minimum=1, maximum=100)
generate_button = gr.Button("Generate Images")
output_images = gr.Gallery()
with gr.Tab("Push to Hugging Face"):
push_button = gr.Button("Push Model to Hugging Face")
huggingface_link = gr.Textbox(label="Hugging Face Model Link")
fine_tune_button.click(fine_tune_model, inputs=[instance_images, class_images, instance_prompt, class_prompt, num_train_steps], outputs=huggingface_link)
generate_button.click(generate_images, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=output_images)
push_button.click(push_to_huggingface, inputs=[HfFolder.path, repo_name], outputs=huggingface_link)
demo.launch() |