v3p4

app.py

@@ -13,8 +13,6 @@ from datetime import datetime
 from diffusers.models import AutoencoderKL
 from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
 
-# ... (keep the existing imports and configurations)
-
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -40,7 +38,6 @@ torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-
 def load_pipeline(model_name):
     vae = AutoencoderKL.from_pretrained(
         "madebyollin/sdxl-vae-fp16-fix",
@@ -66,8 +63,6 @@ def load_pipeline(model_name):
     pipe.to(device)
     return pipe
 
-
-# Add a new function to parse and validate JSON input
 def parse_json_parameters(json_str):
     try:
         params = json.loads(json_str)
@@ -81,7 +76,6 @@ def parse_json_parameters(json_str):
     except Exception as e:
         raise ValueError(f"Error parsing JSON: {str(e)}")
 
-# Modify the generate function to accept JSON parameters
 @spaces.GPU
 def generate(
     prompt: str,
@@ -206,7 +200,7 @@ generation_history = []
 
 # Function to update the history dropdown
 def update_history_dropdown():
-    return gr.Dropdown.update(choices=[f"{item['prompt']} ({item['timestamp']})" for item in generation_history])
+    return [f"{item['prompt']} ({item['timestamp']})" for item in generation_history]
 
 # Modify the generate function to add results to the history
 def generate_and_update_history(*args, **kwargs):
@@ -442,4 +436,4 @@ with gr.Blocks(css="style.css") as demo:
         outputs=[history_image, history_metadata],
     )
 
-demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)
+demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)