Update app.py

app.py

@@ -127,7 +127,6 @@ def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
     return {"latents": callback_kwargs["latents"]}   
 
 def load_and_prepare_model():
-    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
     vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
     #vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None, use_safetensors=False).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")
@@ -140,14 +139,15 @@ def load_and_prepare_model():
         token = HF_TOKEN,
        # scheduler = sched,
     )
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
     pipe.vae = vaeXL #.to(torch.bfloat16)
     pipe.scheduler = sched
     #pipe.vae.do_resize=False
     #pipe.vae.vae_scale_factor=8
-   # pipe.to(device=device, dtype=torch.bfloat16)
-    pipe.to(device)
-    pipe.to(torch.bfloat16)
+    pipe.to(device=device, dtype=torch.bfloat16)
     pipe.vae.set_default_attn_processor()
+    #pipe.to(device)
+    #pipe.to(torch.bfloat16)
     print(f'init noise scale: {pipe.scheduler.init_noise_sigma}')
     pipe.watermark=None
     pipe.safety_checker=None