Update app.py

app.py

@@ -121,11 +121,11 @@ def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
       #  pipeline.scheduler._step_index = pipeline.num_timesteps * 0.9
     return {"latents": callback_kwargs["latents"]}   
 
-
 def load_and_prepare_model():
     #vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(device=device, dtype=torch.bfloat16)
     #vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None, use_safetensors=False).to(device).to(torch.bfloat16) #.to(device=device, dtype=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)
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
+    #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)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
         #torch_dtype=torch.bfloat16,
@@ -164,8 +164,6 @@ def upload_to_ftp(filename):
     except Exception as e:
         print(f"FTP upload error: {e}")
 
-
-
 def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
     filename= f'rv_C_{timestamp}.txt'
     with open(filename, "w") as f: