Update app.py

app.py

@@ -11,8 +11,6 @@ from huggingface_hub import login
 import time
 from datetime import datetime
 from io import BytesIO
-# from diffusers.models.attention_processor import AttentionProcessor
-from diffusers.models.attention_processor import AttnProcessor2_0
 import torch.nn.functional as F
 import time
 import boto3
@@ -102,41 +100,42 @@ def upload_image_to_r2(image, account_id, access_key, secret_key, bucket_name):
 
 def run_lora(prompt, lora_strings_json,  cfg_scale, steps, randomize_seed, seed, width, height, upload_to_r2, account_id, access_key, secret_key, bucket, progress=gr.Progress(track_tqdm=True)):
     print("run_lora", prompt, lora_strings_json, cfg_scale, steps, width, height)
-    
+    gr.Info("Starting process")
     # Load LoRA weights
+    lora_configs = None
     if lora_strings_json:
         try:
             lora_configs = json.loads(lora_strings_json)
         except:
-            lora_configs = None
+            gr.Warning("Parse lora config json failed")
             print("parse lora config json failed")
             
-    if lora_configs:
-        with calculateDuration("Loading LoRA weights"):
-            active_adapters = pipe.get_active_adapters()
-            print("get_active_adapters", active_adapters)
-            adapter_names = []
-            adapter_weights = []
-            for lora_info in lora_configs:
-                lora_repo = lora_info.get("repo")
-                weights = lora_info.get("weights")
-                adapter_name = lora_info.get("adapter_name")
-                adapter_weight = lora_info.get("adapter_weight")
-                if adapter_name in active_adapters:
-                    print(f"Adapter '{adapter_name}' is already loaded, skipping.")
-                    continue
-                if lora_repo and weights and adapter_name:
-                    # load lora
-                    try:
-                        pipe.load_lora_weights(lora_repo, weight_name=weights, adapter_name=adapter_name)
-                    except ValueError as e:
-                        print(f"Error loading LoRA adapter: {e}")
+        if lora_configs:
+            with calculateDuration("Loading LoRA weights"):
+                active_adapters = pipe.get_active_adapters()
+                print("get_active_adapters", active_adapters)
+                adapter_names = []
+                adapter_weights = []
+                for lora_info in lora_configs:
+                    lora_repo = lora_info.get("repo")
+                    weights = lora_info.get("weights")
+                    adapter_name = lora_info.get("adapter_name")
+                    adapter_weight = lora_info.get("adapter_weight")
+                    if adapter_name in active_adapters:
+                        print(f"Adapter '{adapter_name}' is already loaded, skipping.")
                         continue
-                    adapter_names.append(adapter_name)
-                    adapter_weights.append(adapter_weight)
-            # set lora weights
-            if len(adapter_names) > 0:
-                pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
+                    if lora_repo and weights and adapter_name:
+                        # load lora
+                        try:
+                            pipe.load_lora_weights(lora_repo, weight_name=weights, adapter_name=adapter_name)
+                        except ValueError as e:
+                            print(f"Error loading LoRA adapter: {e}")
+                            continue
+                        adapter_names.append(adapter_name)
+                        adapter_weights.append(adapter_weight)
+                # set lora weights
+                if len(adapter_names) > 0:
+                    pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
     
     # Set random seed for reproducibility
     if randomize_seed:
@@ -150,6 +149,7 @@ def run_lora(prompt, lora_strings_json,  cfg_scale, steps, randomize_seed, seed,
         final_image = generate_image(prompt, steps, seed, cfg_scale, width, height, progress)
     except Exception as e:
         error_message =  str(e)
+        gr.Error(error_message)
         print("Run error", e)
         final_image = None
         
@@ -162,7 +162,8 @@ def run_lora(prompt, lora_strings_json,  cfg_scale, steps, randomize_seed, seed,
             result = {"status": "success", "message": "Image generated but not uploaded"}
     else:
         result = {"status": "failed", "message": error_message}
-        
+
+    gr.Info("Completed！")
     progress(100, "Completed!")
 
     return final_image, seed, json.dumps(result)