Update app.py

app.py

@@ -84,15 +84,15 @@ os.environ["SAFETENSORS_FAST_GPU"] = "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-text_encoder = CLIPTextModel.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='text_encoder', token=True)#.to(device=device, dtype=torch.bfloat16)
+text_encoder = CLIPTextModel.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='text_encoder')#.to(device=device, dtype=torch.bfloat16)
 text_encoder_2 = CLIPTextModelWithProjection.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='text_encoder_2',token=True)#.to(device=device, dtype=torch.bfloat16)
 def load_and_prepare_model():
     proc=Attention(query_dim=4, upcast_attention=True, upcast_softmax = True, processor = AttnProcessor2_0)
-    tokenizer_1 = CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='tokenizer', token=True)
-    tokenizer_2 = CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='tokenizer_2', token=True)
-    scheduler = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='scheduler', token=True)
-    vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", low_cpu_mem_usage=False, safety_checker=None, use_safetensors=False, torch_dtype=torch.float32, token=True) #.to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
-    unet = UNet2DConditionModel.from_pretrained("ford442/RealVisXL_V5.0_BF16", low_cpu_mem_usage=False, subfolder='unet', upcast_attention=True, attention_type='gated-text-image', token=True)
+    tokenizer_1 = CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='tokenizer', use_fast=True)
+    tokenizer_2 = CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, subfolder='tokenizer_2', use_fast=True)
+    scheduler = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_cpu_mem_usage=False, 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("ford442/RealVisXL_V5.0_BF16", low_cpu_mem_usage=False, subfolder='unet', upcast_attention=True, attention_type='gated')
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
         #torch_dtype=torch.bfloat16,