Update app.py

app.py

@@ -116,8 +116,8 @@ def load_and_prepare_model(model_id):
     pipe = StableDiffusionXLPipeline.from_pretrained(
         model_id,
         #torch_dtype=torch.bfloat16,
-        add_watermarker=False,
-        use_safetensors=True,
+       # 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("stabilityai/sdxl-vae",repo_type='model',safety_checker=None, torch_dtype=torch.float32),
        # vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16",repo_type='model',safety_checker=None),
@@ -135,12 +135,11 @@ def load_and_prepare_model(model_id):
     #pipe.unet = pipeX.unet
     pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
     #pipe.unet.to(torch.bfloat16)
-    #pipe.to(device)
-    #pipe.to(torch.bfloat16)
-
+    pipe.to(torch.bfloat16)
+    pipe.to(device)
     #pipe.to(torch.device("cuda:0"))
     #pipe.vae.to(torch.bfloat16)
-    pipe.to(device, torch.bfloat16)
+    #pipe.to(device, torch.bfloat16)
     #del pipeX
     #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++")