Create app.py

app.py

@@ -0,0 +1,51 @@
+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()