Update app.py

app.py

@@ -34,10 +34,10 @@ lora_models = {
         "weights": "lora.safetensors",
         "trigger_word": ""  # Sem trigger word específica
     },
-    "VitorCollos": {
-        "repo": "vcollos/VitorCollos",
-        "weights": "Vitor.safetensors",
-        "trigger_word": "A photo of Vitor,"
+    "vgnCollos": {
+        "repo": "vcollos/vgn",
+        "weights": "vgn.safetensors",
+        "trigger_word": "A photo of vgn,"
     }
 }
 
@@ -78,11 +78,11 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
     # Aplica os dois LoRAs combinados
-    pipe.set_adapters(["AndroFlux", "VitorCollos"], adapter_weights=[lora_scale_1, lora_scale_2])
+    pipe.set_adapters(["AndroFlux", "vgnCollos"], adapter_weights=[lora_scale_1, lora_scale_2])
 
-    # Adiciona trigger words apenas se VitorCollos estiver ativado
+    # Adiciona trigger words apenas se vgnCollos estiver ativado
     if lora_scale_2 > 0:
-        prompt = f"{lora_models['VitorCollos']['trigger_word']} {prompt}"
+        prompt = f"{lora_models['vgnCollos']['trigger_word']} {prompt}"
 
     # Gera a imagem
     image = pipe(
@@ -141,7 +141,7 @@ with gr.Blocks(theme=gr_theme) as app:
 
             # Sliders para os pesos dos LoRAs
             lora_scale_1 = gr.Slider(label="LoRA Scale (AndroFlux)", minimum=0, maximum=1, step=0.01, value=0.1)
-            lora_scale_2 = gr.Slider(label="LoRA Scale (VitorCollos)", minimum=0, maximum=1, step=0.01, value=1)
+            lora_scale_2 = gr.Slider(label="LoRA Scale (vgnCollos)", minimum=0, maximum=1, step=0.01, value=1)
 
         with gr.Column(scale=2):
             result = gr.Image(label="Generated Image")