Update app.py

app.py

@@ -115,7 +115,7 @@ def load_and_prepare_model():
     #vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae')   #  ,use_safetensors=True  FAILS
     #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
     #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
     #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear")
     #sched = DPMSolverSDEScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
     #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
@@ -141,7 +141,7 @@ def load_and_prepare_model():
       #  scheduler = EulerAncestralDiscreteScheduler.from_config(pipeX.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
         #scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset =1)
     )
-    sched = EulerAncestralDiscreteScheduler.from_pretrained('SG161222/RealVisXL_V5.0',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear") #, beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained('SG161222/RealVisXL_V5.0',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear") #, beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
     #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
     #pipe.vae = AsymmetricAutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS