Update app.py

app.py

@@ -60,15 +60,17 @@ def get_pipe(model_id: str, lora_scale: float = 1.0):
         )
         pipe.fuse_lora(lora_scale=lora_scale)
         # После загрузки LoRA
-        print("Loaded LoRA weights:")
+        print("LoRa scale is", lora_scale)
+        print("LoRA layers in unet:")
         for name, param in pipe.unet.named_parameters():
             if "lora" in name.lower() and param.requires_grad:
-                print(f"LoRA layer: {name}, shape: {param.shape}")
+                print(f"Unet LoRA layer: {name}, shape: {param.shape}")
                 break
         print("LoRA layers in text_encoder:")
         for name, param in pipe.text_encoder.named_parameters():
             if "lora" in name:
                 print(f"Text Encoder LoRA: {name}, shape: {param.shape}")
+                break
     else:
         # Load a standard model without LoRA
         pipe = DiffusionPipeline.from_pretrained(
@@ -162,7 +164,7 @@ with gr.Blocks(css=css) as demo:
         # опциональный селектор scheduler
         scheduler_select = gr.Dropdown(
             label="Scheduler (optional)",
-            choices=["", "DDIM", "EulerAncestral", "PNDM"],
+            choices=["", "DDIM", "EulerAncestral", "PNDM", "DPMSMS"],
             value="",
         )
 
@@ -170,7 +172,7 @@ with gr.Blocks(css=css) as demo:
         lora_scale_slider = gr.Slider(
             label="LoRA Scale (Only for LoRA model)",
             minimum=0.0,
-            maximum=2.0,
+            maximum=3.0,
             step=0.1,
             value=0.7,
             visible=False,  # Initially hidden