Update app.py

app.py

@@ -118,10 +118,10 @@ model_repo='John6666/uber-realistic-porn-merge-xl-urpmxl-v6final-sdxl'
 
 rv='ford442/RealVisXL_V5.0_BF16'
     
-#text_encoder = CLIPTextModel.from_pretrained(rv, subfolder='text_encoder')#.to(device=device, dtype=torch.bfloat16)
-#text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_repo, subfolder='text_encoder_2')#.to(device=device, dtype=torch.bfloat16)
-#tokenizer_1 = CLIPTokenizer.from_pretrained(rv, subfolder='tokenizer')
-#tokenizer_2 = CLIPTokenizer.from_pretrained(model_repo, subfolder='tokenizer_2')
+text_encoder = CLIPTextModel.from_pretrained(rv, subfolder='text_encoder')#.to(device=device, dtype=torch.bfloat16)
+text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_repo, subfolder='text_encoder_2')#.to(device=device, dtype=torch.bfloat16)
+tokenizer_1 = CLIPTokenizer.from_pretrained(rv, subfolder='tokenizer')
+tokenizer_2 = CLIPTokenizer.from_pretrained(model_repo, subfolder='tokenizer_2')
 #scheduler = EulerAncestralDiscreteScheduler.from_pretrained(rv, subfolder='scheduler')
 vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", low_cpu_mem_usage=False, safety_checker=None, use_safetensors=False, torch_dtype=torch.float32) #.to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
 #unet = UNet2DConditionModel.from_pretrained(model_repo, low_cpu_mem_usage=False, subfolder='unet', upcast_attention=True, attention_type='gated-text-image')