Update app.py

app.py

@@ -108,10 +108,10 @@ def load_and_prepare_model(model_id):
     model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
     dtype = model_dtypes.get(model_id, torch.bfloat16)  # Default to float32 if not found
     #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16,safety_checker=None)
-    vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",safety_checker=None)
+    #vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",safety_checker=None)
   #  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)
     #  vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None).to('cuda')
-    sched = EulerAncestralDiscreteScheduler.from_config('ford442/Juggernaut-XI-v11-fp32', beta_schedule="scaled_linear",use_karras_sigmas=True)
+    sched = EulerAncestralDiscreteScheduler.from_config('ford442/Juggernaut-XI-v11-fp32', subfolder='scheduler',beta_schedule="scaled_linear",use_karras_sigmas=True)
 
     #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0",torch_dtype=torch.float32)
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",torch_dtype=torch.float32)