Update app.py

app.py

@@ -75,11 +75,11 @@ def upload_to_ftp(filename):
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     #vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", use_safetensors=True, subfolder='vae',token=True)
-vaeX=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-fp32", safety_checker=None, use_safetensors=True, subfolder='vae', low_cpu_mem_usage=False, torch_dtype=torch.float32, token=True)
+#vaeX=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-fp32", safety_checker=None, use_safetensors=True, subfolder='vae', low_cpu_mem_usage=False, torch_dtype=torch.float32, token=True)
  
 pipe = StableDiffusion3Pipeline.from_single_file(
     "https://huggingface.co/1inkus/sd35-large-UltraReal-bf16-DDUF/blob/main/sd3-bf16-large.dduf",
-    vae=None,
+    #vae=None,
     #tokenizer_3=T5TokenizerFast.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", add_prefix_space=False, use_fast=True, subfolder="tokenizer_3"),
     use_safetensors=True,
     #dtype=torch.bfloat16,
@@ -113,7 +113,7 @@ pipe = StableDiffusion3Pipeline.from_single_file(
 #pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/UltraReal.safetensors")
 
 #pipe.to(device=device) #, dtype=torch.bfloat16)
-pipe.vae=vaeX #.to('cpu')
+#pipe.vae=vaeX #.to('cpu')
 
 pipe.to(device)
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cuda'))