Update app.py

app.py

@@ -115,7 +115,7 @@ def load_and_prepare_model(model_id):
     #vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
     vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
     #vaeX = AutoencoderKL.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
-    unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
   #  vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
@@ -157,10 +157,10 @@ def load_and_prepare_model(model_id):
     pipe.to(device)
     pipe.to(torch.bfloat16)
     
-    #apply_hidiffusion(pipe)
+    apply_hidiffusion(pipe)
 
     #pipe.unet.set_default_attn_processor()
-    pipe.vae.set_default_attn_processor()
+    #pipe.vae.set_default_attn_processor()
     
     print(f'Pipeline: ')
     print(f'_optional_components: {pipe._optional_components}')