Update app.py

app.py

@@ -28,9 +28,9 @@ pipe.to("cuda")
 
 # Definição dos LoRA e Trigger Words
 lora_models = {
-    "vgn": {
-        "repo": "vcollos/vgn",
-        "weights": "vgn.safetensors",
+    "studio": {
+        "repo": "xey/sldr_flux_nsfw_v2-studio",
+        "weights": "sldr_flux_nsfw_v2-studio.safetensors",
         "trigger_word": ""  # Sem trigger word específica
     },
     "Nanda": {
@@ -79,7 +79,7 @@ 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(["vgn", "Nanda"], adapter_weights=[lora_scale_1, lora_scale_2])
+    pipe.set_adapters(["studio", "Nanda"], adapter_weights=[lora_scale_1, lora_scale_2])
 
     # Adiciona trigger words apenas se Nanda estiver ativado
     if lora_scale_2 > 0:
@@ -122,7 +122,7 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
 # Interface Gradio
 gr_theme = os.getenv("THEME")
 with gr.Blocks(theme=gr_theme) as app:
-    gr.Markdown("# vgn Image Generator")
+    gr.Markdown("# studio Image Generator")
     
     with gr.Row():
         with gr.Column(scale=2):
@@ -136,7 +136,7 @@ with gr.Blocks(theme=gr_theme) as app:
             seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=556215326)
 
             # Sliders para os pesos dos LoRAs
-            lora_scale_1 = gr.Slider(label="LoRA Scale (vgn)", minimum=0, maximum=1, step=0.01, value=0.5)
+            lora_scale_1 = gr.Slider(label="LoRA Scale (studio)", minimum=0, maximum=1, step=0.01, value=0.5)
             lora_scale_2 = gr.Slider(label="LoRA Scale (Nanda)", minimum=0, maximum=1, step=0.01, value=1)
 
         with gr.Column(scale=2):