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app.py

@@ -110,7 +110,9 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
     
     image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
     pipe.to("cpu")
-    if selected_index is not None or is_valid_lora(lora_json): pipe.unload_lora_weights()
+    if selected_index is not None:
+        pipe.unload_lora_weights()
+        if is_valid_lora(lora_json): pipe.unfuse_lora()
     clear_cache()
     return image, seed  
 
@@ -211,7 +213,9 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_width=True, css=css) as app:
                             lora_search_civitai_json = gr.JSON(value={}, visible=False)
                             lora_search_civitai_desc = gr.Markdown(value="", visible=False)
                             lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
-                            lora_download = gr.Button("Get and set LoRA")
+                            with gr.Row():
+                                lora_slot = gr.Slider(label="Set to LoRA slot", minimum=1, maximum=num_loras+1, step=1, value=1)
+                                lora_download = gr.Button("Get and set LoRA")
 
     gallery.select(
         update_selection,
@@ -247,8 +251,8 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_width=True, css=css) as app:
     gr.on(
         triggers=[lora_download.click, lora_download_url.submit],
         fn=download_my_lora,
-        inputs=[lora_download_url, lora_repo[0]],
-        outputs=[lora_repo[0]],
+        inputs=[lora_download_url, lora_repo[int(lora_slot.value - 1)]],
+        outputs=[lora_repo[int(lora_slot.value - 1)]],
         scroll_to_output=True,
         queue=True,
         show_api=False,

mod.py

@@ -134,7 +134,7 @@ def fuse_loras(pipe, lorajson: list[dict]):
         w_list.append(d["scale"])
     if not a_list: return
     pipe.set_adapters(a_list, adapter_weights=w_list)
-    pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
+    #pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
     #pipe.unload_lora_weights()