Update app.py

app.py

@@ -35,6 +35,9 @@ pipe.to("cuda")
 #pipe.enable_model_cpu_offload()
 
 def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, steps, seed, progress=gr.Progress(track_tqdm=True)):
+    prompt = prompt
+    negative_prompt = negative_prompt
+    generator = torch.Generator(device="cuda").manual_seed(seed)
     
     if preprocessor == "canny":
 
@@ -52,11 +55,6 @@ def infer(use_custom_model, model_name, custom_lora_weight, image_in, prompt, ne
         # This is where you load your trained weights
         pipe.load_lora_weights(custom_model, use_auth_token=True)
     
-    prompt = prompt
-    negative_prompt = negative_prompt
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-
-    if use_custom_model:
         lora_scale=custom_lora_weight
 
         images = pipe(
@@ -123,4 +121,4 @@ with gr.Blocks(css=css) as demo:
         outputs = [result]
     )
 
-demo.queue().launch()
+demo.queue(max_size=12).launch()