Update app.py

app.py

@@ -12,7 +12,7 @@ device = "cuda"
 dtype = torch.float16
 
 repo = "stabilityai/stable-diffusion-3-medium"
-pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16, revision="refs/pr/26",token=os.environ["TOKEN"]).to(device)
+t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16, revision="refs/pr/26",token=os.environ["TOKEN"]).to(device)
 
 model_id = "microsoft/Phi-3-medium-4k-instruct"
 model = AutoModelForCausalLM.from_pretrained(
@@ -23,7 +23,7 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-pipe = pipeline(
+upsampler = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
@@ -54,12 +54,12 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         {"role": "assistant", "content": "A gourmet scene in a high-end restaurant kitchen where a chef is presenting a plate of cooked beef testicles, garnished elegantly with herbs and spices. The chef, a middle-aged Caucasian man wearing a white chef's hat and coat, is inspecting the dish with a satisfied expression. The kitchen background is bustling with other chefs and kitchen staff, and the atmosphere is warm and inviting with hanging pots and pans, and a glowing, busy stove in the background. The focus is on the chef's proud presentation of this unusual but delicately prepared dish."},
         {"role": "user", "content": prompt},
     ]
-    output = pipe(messages, **generation_args)
+    output = upsampler(messages, **generation_args)
     upsampled_prompt=output[0]['generated_text']
 
     print(upsampled_prompt)
 
-    image = pipe(
+    image = t2i(
         prompt = upsampled_prompt, 
         negative_prompt = negative_prompt,
         guidance_scale = guidance_scale,