Update app.py

app.py

@@ -113,7 +113,9 @@ def load_and_prepare_model(model_id):
     #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
     #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
     #vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
-    vae = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    #vae = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,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
   #  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")
@@ -144,6 +146,8 @@ 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.scheduler = DPMSolverMultistepScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler', algorithm_type='sde-dpmsolver++')
+    pipe.vae = vaeX
+    pipe.unet = unetX
     pipe.vae.do_resize=False
     pipe.scheduler = sched
     #pipe.vae=vae.to(torch.bfloat16)
@@ -258,8 +262,8 @@ def generate_30(
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a custom before cuda \n")
-        f.write(f"Model VAE: sdxl_vae to bfloat before cuda \n")
-        f.write(f"Model UNET: default \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -327,8 +331,8 @@ def generate_60(
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a custom before cuda \n")
-        f.write(f"Model VAE: sdxl_vae to bfloat before cuda \n")
-        f.write(f"Model UNET: default \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -396,8 +400,8 @@ def generate_90(
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a custom before cuda \n")
-        f.write(f"Model VAE: sdxl_vae to bfloat before cuda \n")
-        f.write(f"Model UNET: default \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()