Update app.py

app.py

@@ -107,7 +107,6 @@ def load_and_prepare_model(model_id):
     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).to('cuda')
    #  vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32,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')
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -115,15 +114,14 @@ def load_and_prepare_model(model_id):
      #  torch_dtype=torch.bfloat16,
         add_watermarker=False,
         use_safetensors=True,
-     #   vae=AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",repo_type='model',safety_checker=None),
+     #  vae=AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",repo_type='model',safety_checker=None),
         vae=AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",repo_type='model',safety_checker=None),
-        scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset =1)
-    ).to(device=device, dtype=torch.bfloat16)
-   # sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start =0.00085,beta_end =0.012,steps_offset =1,)
-   # pipe.scheduler=sched
+        #scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset =1)
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
     #pipe.to('cuda')
    # pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-    #pipe.to(device=device, dtype=torch.bfloat16)
+    pipe.to(device=device, dtype=torch.bfloat16)
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="dpmsolver++")
     #sched = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++")
     #sched = DDIMScheduler.from_config(pipe.scheduler.config)