Update app.py

app.py

@@ -89,7 +89,7 @@ tokenizer_1 = CLIPTokenizer.from_pretrained('ford442/RealVisXL_V5.0_BF16', low_c
 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', token=True)
+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)
 
 def load_and_prepare_model():
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -102,11 +102,13 @@ def load_and_prepare_model():
         tokenizer=None,
         tokenizer_2=None,
         scheduler=None,
+        unet=None,
         vae=None,
     )
     pipe.scheduler=scheduler
     pipe.tokenizer=tokenizer_1
     pipe.tokenizer_2=tokenizer_2
+    pipe.unet=unet
     #pipe.vae.do_resize=False
     #pipe.vae.vae_scale_factor=8
     #pipe.to(device)