Upload app.py

app.py

@@ -105,18 +105,6 @@ def download_file(url, directory=None):
     
     return filepath
 
-def get_lora_weights(lora_repo, weight_name=None):
-    try:
-        # Download the weights from Hugging Face Hub
-        file_path = hf_hub_download(
-            repo_id=lora_repo, 
-            filename=weight_name if weight_name else "pytorch_model.bin"
-        )
-        return file_path
-    except Exception as e:
-        print(f"Failed to fetch weights for {lora_repo}: {e}")
-        raise
-
 def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
     selected_index = evt.index
     selected_indices = selected_indices or []
@@ -243,7 +231,7 @@ def remove_lora_2(selected_indices, loras_state):
 
 def remove_lora_3(selected_indices, loras_state):
     if len(selected_indices) >= 3:
-        selected_indices.pop(2)
+        selected_indices.pop(1)
     selected_info_1 = "Select a Celebrity as LoRA 1"
     selected_info_2 = "Select a LoRA 2"
     selected_info_3 = "Select a LoRA 3"
@@ -279,7 +267,7 @@ def remove_lora_3(selected_indices, loras_state):
 
 def remove_lora_4(selected_indices, loras_state):
     if len(selected_indices) >= 4:
-        selected_indices.pop(3)
+        selected_indices.pop(1)
     selected_info_1 = "Select a Celebrity as LoRA 1"
     selected_info_2 = "Select a LoRA 2"
     selected_info_3 = "Select a LoRA 3"
@@ -463,29 +451,12 @@ def generate_image(prompt, steps, seed, cfg_scale, width, height, progress):
             yield img, seed, f"Generated image {img} with seed {seed}"
     return img
 
-@spaces.GPU(duration=85)
+@spaces.GPU(duration=75)
 def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
     if not selected_indices:
         raise gr.Error("You must select at least one LoRA before proceeding.")
 
     selected_loras = [loras_state[idx] for idx in selected_indices]
-    
-    # Debugging snippet: Inspect LoRAs before loading
-    for idx, lora in enumerate(selected_loras):
-        print(f"Inspecting LoRA {idx + 1}: {lora['title']}")
-        try:
-            lora_weights_path = get_lora_weights(lora['repo'], lora.get("weights"))
-            print(f"LoRA weights fetched from: {lora_weights_path}")
-            lora_weights = torch.load(lora_weights_path, weights_only=True) #lora_weights = torch.load(lora_weights_path)
-            print(f"LoRA Parameter Keys for {lora['title']}: {list(lora_weights.keys())}")
-        except Exception as e:
-            print(f"Error loading LoRA weights for {lora['title']} from {lora['repo']}: {e}")
-            raise gr.Error(f"Failed to load LoRA weights for {lora['title']}.")
-
-    # Print the selected LoRAs
-    print("Running with the following LoRAs:")
-    for lora in selected_loras:
-        print(f"- {lora['title']} from {lora['repo']} with scale {lora_scale_1 if selected_loras.index(lora) == 0 else lora_scale_2}")
 
     # Build the prompt with trigger words
     prepends = []
@@ -499,7 +470,7 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
                 appends.append(trigger_word)
     prompt_mash = " ".join(prepends + [prompt] + appends)
     print("Prompt Mash: ", prompt_mash)
-    print(":--Seed--:", seed)
+    print("--Seed--:", seed)
 
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
@@ -513,22 +484,20 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
     with calculateDuration("Loading LoRA weights"):
         for idx, lora in enumerate(selected_loras):
             lora_name = f"lora_{idx}"
-            print(f"Loading LoRA: {lora['title']} with adapter name: {lora_name}")
-            lora_weights_path = get_lora_weights(lora['repo'], lora.get("weights"))
+            lora_names.append(lora_name)
+            print(f"Lora Name: {lora_name}")
+            lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
             pipe.load_lora_weights(
-                lora_weights_path,
+                lora['repo'],
+                weight_name=lora.get("weights"),
                 low_cpu_mem_usage=True,
                 adapter_name=lora_name,
-                merge_and_unload=True,
             )
-        
+        print("Base Model:", base_model)
+        print("Loaded LoRAs:", selected_indices)
         print("Adapter weights:", lora_weights)
-    try:
+        
         pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-    except Exception as e:
-        print(f"Error while setting adapters: {e}")
-        raise
-#pipe.set_adapters(lora_names, adapter_weights=lora_weights)
 
     # Set random seed if required
     if randomize_seed:
@@ -543,7 +512,7 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
         progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
         yield image, seed, gr.update(value=progress_bar, visible=True)
 
-run_lora.zerogpu = False
+run_lora.zerogpu = True
 
 def get_huggingface_safetensors(link):
     split_link = link.split("/")
@@ -734,8 +703,7 @@ with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(128, 256)
     gallery.select(
         update_selection,
         inputs=[selected_indices, loras_state, width, height],
-        outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, width, height, lora_image_1, lora_image_2, lora_image_3, lora_image_4]
-    )
+        outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, width, height, lora_image_1, lora_image_2, lora_image_3, lora_image_4])
     remove_button_1.click(
         remove_lora_1,
         inputs=[selected_indices, loras_state],