allow lora to be loaded w/o loading eveyr time

app.py

@@ -163,6 +163,9 @@ def make_canny_condition(image, min_val=100, max_val=200, w_bilateral=True):
 
 default_negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
 
+# Global variable to track the currently loaded LoRA
+current_lora_name = None
+
 # Load face detection and recognition package
 app = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
 app.prepare(ctx_id=0, det_size=(640, 640))
@@ -229,11 +232,9 @@ Loras_dict = {
     "":"",
     "Vangogh_Vanilla": "bold, dramatic brush strokes, vibrant colors, swirling patterns, intense, emotionally charged paintings of",
     "Avatar_internlm": "2d anime sketch avatar of",
-    # "Tomer_Hanuka_V3": "Fluid lines",
-    "Storyboards": "Illustration style for storyboarding",
-    "3D_illustration": "3D object illustration, abstract",
-    # "beetl_general_death_style_v2": "a pale, dead, unnatural color face with dark circles around the eyes",
-    "Characters": "gaming vector Art"
+    "Storyboards": "Illustration style for storyboarding.",
+    "3D_illustration": "3D object illustration, abstract.",
+    "Characters": "gaming vector Art."
     }
 
 lora_names = Loras_dict.keys()
@@ -302,22 +303,47 @@ def generate_image(image_path, prompt, num_steps, guidance_scale, seed, num_imag
                     
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    if lora_name != "":
-        lora_path = os.path.join(lora_base_path, lora_name, "pytorch_lora_weights.safetensors")
-        pipe.load_lora_weights(lora_path)
-        pipe.fuse_lora(lora_scale)  
-        pipe.enable_lora()
+    # if lora_name != "":
+    #     lora_path = os.path.join(lora_base_path, lora_name, "pytorch_lora_weights.safetensors")
+    #     pipe.load_lora_weights(lora_path)
+    #     pipe.fuse_lora(lora_scale)  
+    #     pipe.enable_lora()
+
+    #     lora_prefix = Loras_dict[lora_name]
+
+    #     prompt = f"{lora_prefix} {prompt}"
+
+    #     print("Using LoRA: ", lora_name)
+
 
-        lora_prefix = Loras_dict[lora_name]
+    if lora_name != current_lora_name:  # Check if LoRA needs to be changed
+        if current_lora_name is not None:  # If a LoRA is already loaded, unload it
+            pipe.disable_lora()
+            pipe.unfuse_lora()
+            pipe.unload_lora_weights()
+            print(f"Unloaded LoRA: {current_lora_name}")
 
-        prompt = f"{lora_prefix} {prompt}"
+        if lora_name != "":  # Load the new LoRA if specified
+            lora_path = os.path.join(lora_base_path, lora_name, "pytorch_lora_weights.safetensors")
+            pipe.load_lora_weights(lora_path)
+            pipe.fuse_lora(lora_scale)  
+            pipe.enable_lora()
 
-        print("Using LoRA: ", lora_name)
-        
+            lora_prefix = Loras_dict[lora_name]
+
+            print(f"Loaded new LoRA: {lora_name}")
+
+        # Update the current LoRA name
+        current_lora_name = lora_name
+
+    if lora_name != "":
+        full_prompt = f"{lora_prefix} {prompt}"
+    else:
+        full_prompt = prompt
 
     print("Start inference...")    
     images = pipe(
-        prompt = prompt,
+        prompt = full_prompt,
         negative_prompt = default_negative_prompt,
         image_embeds = face_emb,
         image = [face_kps, canny_img] if canny_scale>0.0 else face_kps,
@@ -332,10 +358,10 @@ def generate_image(image_path, prompt, num_steps, guidance_scale, seed, num_imag
         num_images_per_prompt=num_images,
     ).images #[0]
 
-    if lora_name != "":
-        pipe.disable_lora()
-        pipe.unfuse_lora()
-        pipe.unload_lora_weights()
+    # if lora_name != "":
+    #     pipe.disable_lora()
+    #     pipe.unfuse_lora()
+    #     pipe.unload_lora_weights()
 
     gc.collect()
     torch.cuda.empty_cache()