Update app.py

app.py

@@ -24,7 +24,6 @@ pipe = AutoPipelineForText2Image.from_pretrained(
   scheduler=scheduler,
   token=auth_token)
 
-pipe.to("cuda")
 pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4)
 
 state_dict = load_file("unaestheticXLv31.safetensors")
@@ -36,13 +35,13 @@ compel = Compel(tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, 
                 requires_pooled=[False, True])
 
+pipe=pipe.to("cuda")
+
 def error_str(error, title="Error"):
     return f"""#### {title}
             {error}"""  if error else ""
 
 def inference(prompt, guidance, steps, seed=0, neg_prompt="", disable_auto_prompt_correction=False):
-  global pipe
-    
   generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
 
   prompt,neg_prompt=auto_prompt_correction(prompt,neg_prompt,disable_auto_prompt_correction)
@@ -99,7 +98,6 @@ def auto_prompt_correction(prompt_ui,neg_prompt_ui,disable_auto_prompt_correctio
 def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
     conditioning, pooled = compel(prompt)
     neg_conditioning, neg_pooled = compel(neg_prompt)
-    
 
     result = pipe(
         prompt_embeds=conditioning,