Update app.py

app.py

@@ -26,21 +26,6 @@ ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1"
 ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-models = ["runwayml/stable-diffusion-v1-5",
-"stabilityai/stable-diffusion-xl-base-1.0",
-"stablediffusionapi/juggernaut-xl-v8",
-"emilianJR/epiCRealism",
-"SG161222/Realistic_Vision_V5.1_noVAE",
-"cagliostrolab/animagine-xl-3.0",
-"misri/cyberrealistic_v41BackToBasics",
-"malcolmrey/serenity",
-"SG161222/RealVisXL_V3.0",
-"stablediffusionapi/realistic-stock-photo-v2",
-"stablediffusionapi/pixel-art-diffusion-xl",
-"playgroundai/playground-v2-1024px-aesthetic",
-"dataautogpt3/ProteusV0.3",
-"stablediffusionapi/disney-pixar-cartoon",
-"RunDiffusion/Juggernaut-XL-Lightning"]  
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -67,7 +52,7 @@ def generate(
     use_vae: bool = False,
     use_lora: bool = False,
     apply_refiner: bool = False,
-    dropdown_model = 'cagliostrolab/animagine-xl-3.0',
+    model = 'cagliostrolab/animagine-xl-3.0',
     vaecall = 'stabilityai/sd-vae-ft-mse',
     lora = 'amazonaws-la/juliette',
     lora_scale: float = 0.7,
@@ -75,15 +60,15 @@ def generate(
     if torch.cuda.is_available():
 
         if not use_vae:  
-            pipe = DiffusionPipeline.from_pretrained(dropdown_model, torch_dtype=torch.float16)
+            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
             
         if use_vae:
             vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
-            pipe = DiffusionPipeline.from_pretrained(dropdown_model, vae=vae, torch_dtype=torch.float16)
+            pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
             
         if use_lora:
             pipe.load_lora_weights(lora)
-            pipe.fuse_lora(lora_scale=0.7)
+            pipe.fuse_lora(lora_scale)
                                    
         if ENABLE_CPU_OFFLOAD:
             pipe.enable_model_cpu_offload()
@@ -155,7 +140,7 @@ with gr.Blocks(css="style.css") as demo:
         visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
     )
     with gr.Group():
-        dropdown_model = gr.Dropdown(label='Model', value='cagliostrolab/animagine-xl-3.0', choices=models)
+        model = gr.Text(label='Model')
         vaecall = gr.Text(label='VAE')
         lora = gr.Text(label='LoRA')
         lora_scale = gr.Slider(
@@ -340,7 +325,7 @@ with gr.Blocks(css="style.css") as demo:
             use_vae,
             use_lora,
             apply_refiner,
-            dropdown_model,
+            model,
             vaecall,
             lora,
             lora_scale,