Update app.py

app.py

@@ -115,7 +115,7 @@ def load_and_prepare_model(model_id):
     #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",torch_dtype=torch.float32)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         model_id,
-        torch_dtype=torch.bfloat16,
+        #torch_dtype=torch.bfloat16,
         add_watermarker=False,
        # use_safetensors=True,
      #  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),
@@ -132,12 +132,12 @@ def load_and_prepare_model(model_id):
     #pipe.vae=pipeX.vae
    # pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     #pipe.to(dtype=torch.bfloat16)
-    pipe.unet = pipeX.unet
+    #pipe.unet = pipeX.unet
     pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
-    pipe.unet.to(torch.bfloat16)
+    #pipe.unet.to(torch.bfloat16)
     pipe.to(device)
     #pipe.vae.to(torch.bfloat16)
-    #pipe.to(torch.bfloat16)
+    pipe.to(torch.bfloat16)
     #pipe.to(device, torch.bfloat16)
     del pipeX
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="dpmsolver++")
@@ -222,11 +222,10 @@ def generate_30(
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
         f.write(f"SPACE SETUP: \n")
-        f.write(f"Use Safetensors: no \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: stabilityai/sdxl-vae before cuda to bfloat with pipe \n")
-        f.write(f"Model UNET: stabilityai before cuda to bfloat with pipe \n")
+        f.write(f"Model VAE: default \n")
+        f.write(f"Model UNET: default \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -289,11 +288,10 @@ def generate_60(
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
         f.write(f"SPACE SETUP: \n")
-        f.write(f"Use Safetensors: no \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: stabilityai/sdxl-vae before cuda to bfloat with pipe \n")
-        f.write(f"Model UNET: stabilityai before cuda to bfloat with pipe \n")
+        f.write(f"Model VAE: default \n")
+        f.write(f"Model UNET: default \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -356,11 +354,10 @@ def generate_90(
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
         f.write(f"SPACE SETUP: \n")
-        f.write(f"Use Safetensors: no \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: stabilityai/sdxl-vae before cuda to bfloat with pipe \n")
-        f.write(f"Model UNET: stabilityai before cuda to bfloat with pipe \n")
+        f.write(f"Model VAE: default \n")
+        f.write(f"Model UNET: default \n")
     upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()