Update app.py

app.py

@@ -14,7 +14,7 @@ with open('loras.json', 'r') as f:
     loras = json.load(f)
 
 # Initialize the base model
-base_model = "stabilityai/stable-diffusion-3.5-large-turbo"
+base_model = "ariG23498/sd-3.5-merged"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 
 MAX_SEED = 2**32-1
@@ -71,7 +71,7 @@ def infer(prompt, negative_prompt, trigger_word, steps, seed, cfg_scale, width,
             width=width,
             height=height,
             generator=generator,
-            joint_attention_kwargs={"scale": lora_scale},
+            lora_scale=lora_scale,
         ).images[0]
     return image
 
@@ -87,8 +87,10 @@ def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, randomiz
     with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
         if "weights" in selected_lora:
             pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
+            pipe.fuse_lora(lora_scale=1.0)
         else:
             pipe.load_lora_weights(lora_path)
+            pipe.fuse_lora(lora_scale=1.0)
         
     # Set random seed for reproducibility
     with calculateDuration("Randomizing seed"):
@@ -149,8 +151,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
         with gr.Accordion("Advanced Settings", open=True):
             with gr.Column():
                 with gr.Row():
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.5, value=4)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=4)
+                    cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.5, value=1.5)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=8)
                 
                 with gr.Row():
                     width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)