Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
+import spaces
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "ZennyKenny/flux_lora_natalie-diffusion"
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
@@ -21,7 +21,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU
 def infer(
     prompt,
     negative_prompt,
@@ -38,6 +38,9 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
+    # Append "XTON" to the prompt
+    prompt = "XTON " + prompt
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -51,10 +54,11 @@ def infer(
     return image, seed
 
 
+
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A man walking in a forest",
+    "A viking ship sailing on a river at night",
+    "A woman dancing in a tavern",
 ]
 
 css = """
@@ -64,7 +68,7 @@ css = """
 }
 """
 
-with gr.Blocks(css=css) as demo:
+with gr.Blocks(css=css) as natalie_diffusion:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
@@ -151,4 +155,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    natalie_diffusion.launch()