Update app.py

app.py

@@ -112,20 +112,22 @@ 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",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_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear")
+    sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1)
+    #sched = EulerAncestralDiscreteScheduler()
     #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("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
     
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",torch_dtype=torch.float32)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
     #    'ford442/Juggernaut-XI-v11-fp32',
+        #'SG161222/RealVisXL_V5.0',
         #torch_dtype=torch.bfloat16,
         add_watermarker=False,
        # custom_pipeline="lpw_stable_diffusion_xl",
         #use_safetensors=True,
-        use_auth_token=HF_TOKEN,
+      #  use_auth_token=HF_TOKEN,
      #  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),
@@ -138,8 +140,10 @@ def load_and_prepare_model(model_id):
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear",use_karras_sigmas=True, algorithm_type="dpmsolver++")
     #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.watermark=None
+    pipe.safety_checker=None
     #pipe.scheduler = sched
-    pipe.unet=pipeX.unet.to(torch.bfloat16)
+    pipe.unet=pipeX.unet
    # pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     #pipe.to(dtype=torch.bfloat16)
     #pipe.unet = pipeX.unet
@@ -147,7 +151,7 @@ def load_and_prepare_model(model_id):
     #pipe.unet.to(torch.bfloat16)
 
     pipe.to(device)
-    pipe.vae=vae.to(torch.bfloat16)
+    pipe.to(torch.bfloat16)
 
     #pipe.to(torch.bfloat16)