Update app.py

app.py

@@ -107,12 +107,13 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
 def load_and_prepare_model():
     #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
     vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False, low_cpu_mem_usage=False, torch_dtype=torch.float32, token=True) #.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=False)
     pipe = StableDiffusionXLPipeline.from_pretrained(
-        'ford442/RealVisXL_V5.0_BF16',
+        #'ford442/RealVisXL_V5.0_BF16',
        #'ford442/Juggernaut-XI-v11-fp32',
       #   'SG161222/RealVisXL_V5.0',
         #'John6666/uber-realistic-porn-merge-xl-urpmxl-v3-sdxl',
-        #torch_dtype=torch.bfloat16,
+        'John6666/uber-realistic-porn-merge-xl-urpmxl-v6final-sdxl',
         add_watermarker=False,
        # custom_pipeline="lpw_stable_diffusion_xl",
         #use_safetensors=True,
@@ -121,7 +122,6 @@ def load_and_prepare_model():
         text_encoder_2=None,
         vae=None,
     )
-
     pipe.vae=vaeX
     pipe.to(device=device, dtype=torch.bfloat16)
     pipe.vae.set_default_attn_processor()