Update app.py

app.py

@@ -257,15 +257,11 @@ def generate_30(
     num_inference_steps: int = 125,
     use_resolution_binning: bool = True, 
     denoise: float = 0.3,
-    vae_scale: int = 8,
+    lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     #torch.backends.cudnn.benchmark = False
-    #torch.cuda.empty_cache()
-    #gc.collect()
-    #global models
-    #pipe = models[model_choice]
-    pipe.vae.vae_scale_factor=vae_scale
+    pipe.set_adapters(["skin"], adapter_weights=[lora_scale])
     seed = int(randomize_seed_fn())
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -319,15 +315,11 @@ def generate_60(
     num_inference_steps: int = 250,
     use_resolution_binning: bool = True, 
     denoise: float = 0.3,
-    vae_scale: int = 8,
+    lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     #torch.backends.cudnn.benchmark = False
-    #torch.cuda.empty_cache()
-    #gc.collect()
-    #global models
-    #pipe = models[model_choice]
-    pipe.vae.vae_scale_factor=vae_scale
+    pipe.set_adapters(["skin"], adapter_weights=[lora_scale])
     seed = int(randomize_seed_fn())
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -381,15 +373,11 @@ def generate_90(
     num_inference_steps: int = 250,
     use_resolution_binning: bool = True, 
     denoise: float = 0.3,
-    vae_scale: int = 8,
+    lora_scale: float = 0.5,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     #torch.backends.cudnn.benchmark = False
-    #torch.cuda.empty_cache()
-    #gc.collect()
-    #global models
-    #pipe = models[model_choice]
-    pipe.vae.vae_scale_factor=vae_scale
+    pipe.set_adapters(["skin"], adapter_weights=[lora_scale])
     seed = int(randomize_seed_fn())
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -506,12 +494,12 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=0.01,
             value=0.3,
         )
-        vae_scale = gr.Slider(
-            label="VAE Scale Multiplier",
-            minimum=1,
-            maximum=16,
-            step=1,
-            value=8,
+        lora_scale = gr.Slider(
+            label="LORA Scale (Skin)",
+            minimum=0.0,
+            maximum=1.0,
+            step=0.01,
+            value=0.5,
         )
         with gr.Row():
             width = gr.Slider(
@@ -574,7 +562,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             denoise,
-            vae_scale,
+            lora_scale,
         ],
         outputs=[result],
     )
@@ -596,7 +584,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             denoise,
-            vae_scale,
+            lora_scale,
         ],
         outputs=[result],
     )
@@ -618,7 +606,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             denoise,
-            vae_scale,
+            lora_scale,
         ],
         outputs=[result],
     )