Upload app.py with huggingface_hub

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
-import spaces 
+import spaces
 from diffusers import StableDiffusionXLPipeline
 import numpy as np
 import math
-import torch 
+import torch
 import random
 
 from gradio_imageslider import ImageSlider
@@ -15,7 +15,8 @@ theme = gr.themes.Base(
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
-    torch_dtype=torch.float16
+    torch_dtype=torch.float16,
+    trust_remote_code=True
 )
 
 device="cuda"
@@ -28,7 +29,7 @@ def run(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_lay
     print(f"Initial seed for prompt `{prompt}`", seed)
     if(randomize_seed):
         seed = random.randint(0, 9007199254740991)
-    
+
     if not prompt and not negative_prompt:
         guidance_scale = 0.0
     pipe.unfuse_lora()
@@ -38,8 +39,8 @@ def run(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_lay
         pipe.fuse_lora(lora_scale=0.9)
     print(f"Seed before sending to generator for prompt: `{prompt}`", seed)
     generator = torch.Generator(device="cuda").manual_seed(seed)
-    image_pag = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]    
-    
+    image_pag = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
+
     generator = torch.Generator(device="cuda").manual_seed(seed)
     image_normal = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=25).images[0]
     print(f"Seed at the end of generation for prompt: `{prompt}`", seed)
@@ -80,4 +81,4 @@ with gr.Blocks(css=css, theme=theme) as demo:
         outputs=[output, seed],
     )
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)