Update app.py

app.py

@@ -114,12 +114,12 @@ def load_and_prepare_model(model_id):
     #vaeX = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
     #vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae', safety_checker=None, use_safetensors=False)   #  ,use_safetensors=True  FAILS
     #vaeX = AutoencoderKL.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
-    #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
   #  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)
     #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('SG161222/RealVisXL_V5.0', 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', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True,timestep_spacing="trailing")    #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('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)    #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",use_safetensors=True)
     
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -149,7 +149,7 @@ 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.scheduler = DPMSolverMultistepScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler', algorithm_type='sde-dpmsolver++')
     pipe.vae = vaeX.to(torch.bfloat16)
-    #pipe.unet = unetX
+    pipe.unet = unetX.to(torch.bfloat16)
     pipe.scheduler = sched
     pipe.vae.do_resize=False
     #pipe.vae.do_rescale=False
@@ -224,7 +224,7 @@ def uploadNote():
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a all_custom before cuda woth trailing \n")
         f.write(f"Model VAE: sdxl-vae  to bfloat with cuda then attn_proc \n")
-        f.write(f"Model UNET: default  to bfloat with cuda then attn_proc \n")
+        f.write(f"Model UNET: FP64  to bfloat with cuda then attn_proc \n")
         f.write(f"Model HiDiffusion OFF \n")
         f.write(f"Model do_resize OFF \n")
         f.write(f"Model vae_scale_factor 7 \n")