Update app.py

app.py

@@ -1,14 +1,15 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+from diffusers import AuraFlowPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = AuraFlowPipeline.from_pretrained("AuraDiffusion/auradiffusion-v0.1a0",
+                                             torch_dtype=torch.float16)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
 else: 
@@ -103,7 +104,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=1024,
                 )
                 
                 height = gr.Slider(
@@ -111,7 +112,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=1024,
                 )
             
             with gr.Row():
@@ -121,15 +122,15 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,
+                    value=5.0,
                 )
                 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=50,
                     step=1,
-                    value=2,
+                    value=50,
                 )
         
         gr.Examples(