Update app.py

app.py

@@ -13,10 +13,11 @@ import numpy as np
 from PIL import Image
 import torch
 from diffusers import AutoencoderKL, StableDiffusionXLPipeline
-from diffusers import EulerAncestralDiscreteScheduler
+#from diffusers import EulerAncestralDiscreteScheduler
 from typing import Tuple
 import paramiko
 import datetime
+from diffusers import DPMSolverSDEScheduler
 
 torch.backends.cuda.matmul.allow_tf32 = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
@@ -83,7 +84,9 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 def load_and_prepare_model():
     vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-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 = DPMSolverSDEScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
+
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
         #torch_dtype=torch.bfloat16,
@@ -365,7 +368,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
                 minimum=10,
                 maximum=1000,
                 step=10,
-                value=170,
+                value=50,
             )
 
     gr.Examples(