Update app.py

app.py

@@ -86,9 +86,9 @@ pipe.to(device=device, dtype=torch.bfloat16)
 #pipe.to(device)
 pipe.vae=vaeX.to('cpu')
 
-text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', torch_dtype=torch.bfloat16, token=True).to(device)
-text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2', torch_dtype=torch.bfloat16,token=True).to(device)
-text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3', torch_dtype=torch.bfloat16,token=True).to(device)
+text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True).to(device=device, dtype=torch.bfloat16)
+text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)
+text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True).to(device=device, dtype=torch.bfloat16)
     
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to('cpu') #.to(device)