Update app.py

app.py

@@ -111,7 +111,7 @@ def check_sleep_time():
         
         return sleep_time_value, gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
     
-def train_dreambooth_blora_sdxl(instance_data_dir, b_lora_trained_folder, instance_prompt, class_prompt, max_train_steps, checkpoint_steps):
+def train_dreambooth_blora_sdxl(instance_data_dir, b_lora_trained_folder, instance_prompt, max_train_steps, checkpoint_steps):
     
     script_filename = "train_dreambooth_b-lora_sdxl.py"  # Assuming it's in the same folder
 
@@ -123,8 +123,8 @@ def train_dreambooth_blora_sdxl(instance_data_dir, b_lora_trained_folder, instan
         f"--instance_data_dir={instance_data_dir}",
         f"--output_dir={b_lora_trained_folder}",
         f"--instance_prompt='{instance_prompt}'",
-        f"--class_prompt={class_prompt}",
-        #f"--validation_prompt=a teddy bear in {instance_prompt} style",
+        #f"--class_prompt={class_prompt}",
+        f"--validation_prompt={instance_prompt} in {instance_prompt} style",
         "--num_validation_images=1",
         "--validation_epochs=500",
         "--resolution=1024",
@@ -168,7 +168,7 @@ def clear_directory(directory_path):
     else:
         print(f'The directory {directory_path} does not exist.')
 
-def main(image_path, b_lora_trained_folder, instance_prompt, class_prompt, training_type, training_steps):
+def main(image_path, b_lora_trained_folder, instance_prompt, training_type, training_steps):
 
     if is_shared_ui:
         raise gr.Error("This Space only works in duplicated instances")
@@ -207,7 +207,7 @@ def main(image_path, b_lora_trained_folder, instance_prompt, class_prompt, train
 
     max_train_steps = training_steps
     
-    train_dreambooth_blora_sdxl(local_dir, b_lora_trained_folder, instance_prompt, class_prompt, max_train_steps, checkpoint_steps)
+    train_dreambooth_blora_sdxl(local_dir, b_lora_trained_folder, instance_prompt, max_train_steps, checkpoint_steps)
     
     your_username = api.whoami(token=hf_token)["name"]
 
@@ -342,8 +342,8 @@ with gr.Blocks(css=css) as demo:
                     training_type = gr.Radio(label="Training type", choices=["style", "concept"], value="style", visible=False)
                     b_lora_name = gr.Textbox(label="Name your B-LoRa model", placeholder="b_lora_trained_folder", visible=False)
                     with gr.Row():
-                        instance_prompt = gr.Textbox(label="Create instance prompt", placeholder="A [v42] <class_prompt>", visible=False)
-                        class_prompt = gr.Textbox(label="Specify class prompt", placeholder="style | person | dog ", visible=False)
+                        instance_prompt = gr.Textbox(label="Create instance prompt", placeholder="A [v42]", visible=False)
+                        #class_prompt = gr.Textbox(label="Specify class prompt", placeholder="style | person | dog ", visible=False)
                     training_steps = gr.Number(label="Training steps", value=1000, interactive=False, visible=False)
                     checkpoint_step = gr.Number(label="checkpoint step", visible=False, value=500)
 
@@ -362,7 +362,7 @@ with gr.Blocks(css=css) as demo:
     demo.load(
         fn = check_sleep_time,
         inputs = None, 
-        outputs = [current_sleep_time, sleep_time_message, b_lora_name, instance_prompt, class_prompt, training_type, training_steps, train_btn]
+        outputs = [current_sleep_time, sleep_time_message, b_lora_name, instance_prompt, training_type, training_steps, train_btn]
     )
     
     training_type.change(
@@ -373,7 +373,7 @@ with gr.Blocks(css=css) as demo:
     
     train_btn.click(
         fn = main,
-        inputs = [image, b_lora_name, instance_prompt, class_prompt, training_type, training_steps],
+        inputs = [image, b_lora_name, instance_prompt, training_type, training_steps],
         outputs = [status]
     )