Update app.py

app.py

@@ -81,15 +81,15 @@ pipe = StableDiffusion3Pipeline.from_pretrained(
     #torch_dtype=torch.bfloat16,
     #use_safetensors=False,
 )
-text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True).to(device)
-text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True).to(device)
-text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True).to(device)
+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)
     
 pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/UltraReal.safetensors")
 
 pipe.to(device=device, dtype=torch.bfloat16)
 #pipe.to(device)
-pipe.vae=vaeX.to('cpu')
+pipe.vae=vaeX #.to('cpu')
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cpu'))
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -115,7 +115,6 @@ def infer_30(
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
     print('-- generating image --')
-    
     sd_image = pipe(
             prompt=prompt,
             prompt_2=prompt,