Update app.py

app.py

@@ -9,7 +9,7 @@ from diffusers.utils import load_image
 from PIL import Image
 import requests
 import transformers
-from transformers import T5TokenizerFast, T5EncoderModel
+from transformers import AutoTokenizer, T5EncoderModel
 from translatepy import Translator
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
@@ -55,7 +55,7 @@ text_encoder_3 = T5EncoderModel.from_pretrained(
     torch_dtype=torch.float16,
 )
 
-tokenizer_3 = T5TokenizerFast.from_pretrained(
+tokenizer_3 = AutoTokenizer.from_pretrained(
     repo, 
     subfolder="tokenizer_3",
     torch_dtype=torch.float16,
@@ -63,8 +63,8 @@ tokenizer_3 = T5TokenizerFast.from_pretrained(
 
 # Ensure model and scheduler are initialized in GPU-enabled function
 if torch.cuda.is_available():
-    pipe = StableDiffusion3Pipeline.from_pretrained(repo, vae=vae, transformer=transformer, tokenizer_3=tokenizer_3, text_encoder_3=text_encoder_3, torch_dtype=torch.float16).to("cuda")
-    pipe2 = StableDiffusion3Img2ImgPipeline.from_pretrained(repo, vae=vae, transformer=transformer, tokenizer=tokenizer_3, text_encoder=text_encoder_3, torch_dtype=torch.float16).to("cuda")
+    pipe = StableDiffusion3Pipeline.from_pretrained(repo, vae=vae, transformer=transformer, tokenizer=tokenizer_3, text_encoder_3=text_encoder_3, torch_dtype=torch.float16).to("cuda")
+    pipe2 = StableDiffusion3Img2ImgPipeline.from_pretrained(repo, vae=vae, transformer=transformer, tokenizer=tokenizer_3, text_encoder_3=text_encoder_3, torch_dtype=torch.float16).to("cuda")
 
 pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe2.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)