Update app.py

app.py

@@ -270,7 +270,7 @@ def remove_custom_lora(selected_indices, current_loras, gallery):
         lora_image_2
     )
 
-@spaces.GPU(duration=75)
+@spaces.GPU()
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
     print("Generating image...")
     pipe.to("cuda")
@@ -290,7 +290,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
         ):
             yield img
 
-@spaces.GPU(duration=75)
+@spaces.GPU()
 def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
     pipe_i2i.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
@@ -309,6 +309,7 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
     ).images[0]
     return final_image
 
+@spaces.GPU(duration=75)
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, 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.")
@@ -340,20 +341,20 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
         for idx, lora in enumerate(selected_loras):
             lora_name = f"lora_{idx}"
             lora_names.append(lora_name)
+            print(f"Lora Name: {lora_name}")
             lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
             lora_path = lora['repo']
             weight_name = lora.get("weights")
             print(f"Lora Path: {lora_path}")
-            if image_input is not None:
-                if weight_name:
-                    pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
-                else:
-                    pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
-            else:
-                if weight_name:
-                    pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
-                else:
-                    pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
+            pipe_to_use = pipe_i2i if image_input is not None else pipe
+            pipe_to_use.load_lora_weights(
+                lora_path, 
+                weight_name=weight_name if weight_name else None,
+                low_cpu_mem_usage=True,
+                adapter_name=lora_name
+            )
+#            if image_input is not None: pipe_i2i = pipe_to_use
+#            else: pipe = pipe_to_use
         print("Loaded LoRAs:", lora_names)
         print("Adapter weights:", lora_weights)
         if image_input is not None:
@@ -463,7 +464,7 @@ css = '''
 #component-11{align-self: stretch;}
 '''
 
-with gr.Blocks(css=css, delete_cache=(60, 3600)) as app:
+with gr.Blocks(css=css, delete_cache=(60, 60)) as app:
     title = gr.HTML(
         """<h1><img src="https://i.imgur.com/wMh2Oek.png" alt="LoRA"> LoRA Lab [beta]</h1><br><span style="
     margin-top: -25px !important;
@@ -515,18 +516,20 @@ with gr.Blocks(css=css, delete_cache=(60, 3600)) as app:
                 label="Or pick from the LoRA Explorer gallery",
                 allow_preview=False,
                 columns=5,
-                elem_id="gallery"
+                elem_id="gallery",
+                show_share_button=False,
+                interactive=False
             )
         with gr.Column():
             progress_bar = gr.Markdown(elem_id="progress", visible=False)
-            result = gr.Image(label="Generated Image", interactive=False)
+            result = gr.Image(label="Generated Image", interactive=False, show_share_button=False)
             with gr.Accordion("History", open=False):
                 history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
 
     with gr.Row():
         with gr.Accordion("Advanced Settings", open=False):
             with gr.Row():
-                input_image = gr.Image(label="Input image", type="filepath")
+                input_image = gr.Image(label="Input image", type="filepath", show_share_button=False)
                 image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
             with gr.Column():
                 with gr.Row():
@@ -575,7 +578,7 @@ with gr.Blocks(css=css, delete_cache=(60, 3600)) as app:
         fn=run_lora,
         inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
         outputs=[result, seed, progress_bar]
-    ).then(  # Update the history gallery
+    ).then(
         fn=lambda x, history: update_history(x, history),
         inputs=[result, history_gallery],
         outputs=history_gallery,