Update app.py

app.py

@@ -58,14 +58,14 @@ def update_selection(evt: gr.SelectData, width, height):
     )
 
 @spaces.GPU(duration=70)
-def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
     pipe.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
     
     with calculateDuration("Generating image"):
         # Generate image
         image = pipe(
-            prompt=f"{prompt} {trigger_word}",
+            prompt=prompt_mash,
             num_inference_steps=steps,
             guidance_scale=cfg_scale,
             width=width,
@@ -82,7 +82,16 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
     selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
     trigger_word = selected_lora["trigger_word"]
-
+    if(trigger_word):
+        if "trigger_position" in selected_lora:
+            if selected_lora["trigger_position"] == "prepend":
+                prompt_mash = f"{trigger_word} {prompt}"
+            else:
+                prompt_mash = f"{prompt} {trigger_word}"
+        else:
+            prompt_mash = f"{trigger_word} {prompt}"
+    else:
+        prompt_mash = prompt
     # Load LoRA weights
     with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
         if "weights" in selected_lora:
@@ -96,7 +105,7 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
         if randomize_seed:
             seed = random.randint(0, MAX_SEED)
     
-    image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
+    image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
     pipe.to("cpu")
     #pipe.unfuse_lora()
     pipe.unload_lora_weights()