Update app.py

app.py

@@ -107,14 +107,16 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
 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", safety_checker=None)
-    #vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",safety_checker=None).to(torch.bfloat16)
+    #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", 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', subfolder='scheduler',beta_schedule="scaled_linear",use_karras_sigmas=True)
-    sched = EulerAncestralDiscreteScheduler.from_config('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear",use_karras_sigmas=True)
-
-    #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0",torch_dtype=torch.float32)
+    sched = EulerAncestralDiscreteScheduler.from_config("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear")
+    #sched = EulerAncestralDiscreteScheduler.from_config('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)
+      #  sched = EulerAncestralDiscreteScheduler.from_config('ford442/RealVisXL_V5.0_BF16', beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
+    pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0")
+    
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",torch_dtype=torch.float32)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
@@ -127,7 +129,7 @@ def load_and_prepare_model(model_id):
      #  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),
-        vae=vae,
+        #vae=vae,
        # unet=pipeX.unet,
         scheduler = sched
       #  scheduler = EulerAncestralDiscreteScheduler.from_config(pipeX.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
@@ -137,8 +139,7 @@ def load_and_prepare_model(model_id):
     #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 = sched
-    #pipe.vae=vae
-    #pipe.vae=pipeX.vae
+    pipe.unet=pipeX.unet.to(torch.bfloat16)
    # pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     #pipe.to(dtype=torch.bfloat16)
     #pipe.unet = pipeX.unet
@@ -146,7 +147,9 @@ def load_and_prepare_model(model_id):
     #pipe.unet.to(torch.bfloat16)
 
     pipe.to(device)
-    pipe.to(torch.bfloat16)
+    pipe.vae=vae.to(torch.bfloat16)
+
+    #pipe.to(torch.bfloat16)
 
     #pipe.to(torch.device("cuda:0"))
     #pipe.vae.to(torch.bfloat16)