Update app.py

app.py

@@ -124,9 +124,9 @@ def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
 
 def load_and_prepare_model():
     #vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(device=device, dtype=torch.bfloat16)
-    vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None).to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
+    #vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None,token=HF_TOKEN).to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
     #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
-    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',token=HF_TOKEN)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
         #torch_dtype=torch.bfloat16,
@@ -134,7 +134,7 @@ def load_and_prepare_model():
       #  low_cpu_mem_usage = False,
         token=HF_TOKEN,
     )
-    pipe.vae = vaeRV #.to(torch.bfloat16)
+    #pipe.vae = vaeRV #.to(torch.bfloat16)
     pipe.scheduler = sched
     #pipe.vae.do_resize=False
     #pipe.vae.vae_scale_factor=8