Update app.py

app.py

@@ -207,6 +207,24 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 del lora_model
                 gc.collect()
 @spaces.GPU
+def generate_image(conditioning, pooled, negative_conditioning, negative_pooled, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale):
+    image = pipe(
+        prompt_embeds=conditioning,
+        pooled_prompt_embeds=pooled,
+        negative_prompt_embeds=negative_conditioning,
+        negative_pooled_prompt_embeds=negative_pooled,
+        width=1024,
+        height=1024,
+        image_embeds=face_emb,
+        image=face_image,
+        strength=1-image_strength,
+        control_image=images,
+        num_inference_steps=20,
+        guidance_scale = guidance_scale,
+        controlnet_conditioning_scale=[face_strength, depth_control_scale],
+    ).images[0]
+    return image
+    
 def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
     global last_lora, last_merged, last_fused, pipe
 
@@ -254,7 +272,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     weight_name = sdxl_loras[selected_state.index]["weights"]
     
     full_path_lora = state_dicts[repo_name]["saved_name"]
-    #loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
+    loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
     cross_attention_kwargs = None
     print("Last LoRA: ", last_lora)
     print("Current LoRA: ", repo_name)
@@ -263,7 +281,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         if(last_fused):
             pipe.unfuse_lora()
         pipe.unload_lora_weights()
-        pipe.load_lora_weights(full_path_lora)
+        pipe.load_lora_weights(loaded_state_dict)
         pipe.fuse_lora(lora_scale)
         last_fused = True
         is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
@@ -287,22 +305,8 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         negative_conditioning, negative_pooled = compel(negative)
     else:
         negative_conditioning, negative_pooled = None, None
-        
-    image = pipe(
-        prompt_embeds=conditioning,
-        pooled_prompt_embeds=pooled,
-        negative_prompt_embeds=negative_conditioning,
-        negative_pooled_prompt_embeds=negative_pooled,
-        width=1024,
-        height=1024,
-        image_embeds=face_emb,
-        image=face_image,
-        strength=1-image_strength,
-        control_image=images,
-        num_inference_steps=20,
-        guidance_scale = guidance_scale,
-        controlnet_conditioning_scale=[face_strength, depth_control_scale],
-    ).images[0]
+
+    image = generate_image(conditioning, pooled, negative_conditioning, negative_pooled, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale)
     last_lora = repo_name
     gc.collect()
     return image, gr.update(visible=True)