Update app.py

app.py

@@ -113,21 +113,23 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
 def load_and_prepare_model(model_id):
     model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
     dtype = model_dtypes.get(model_id, torch.bfloat16)  # Default to float32 if not found
-    vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16,safety_checker=None).to('cuda')
+   # vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16,safety_checker=None)
+    vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
+    sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         model_id,
-        torch_dtype=torch.bfloat16,
+      #  torch_dtype=torch.bfloat16,
         add_watermarker=False,
         use_safetensors=True,
         vae=vae,
+        scheduler=sched
     ).to('cuda')
     #pipe.to(device=device, dtype=torch.bfloat16)
     
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="dpmsolver++")
-    sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     #sched = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++")
     #sched = DDIMScheduler.from_config(pipe.scheduler.config)
-    pipe.scheduler=sched
+    #pipe.scheduler=sched
     return pipe
 
 # Preload and compile both models
@@ -223,7 +225,7 @@ def generate_60(
     num_inference_steps: int = 250,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
-    num_images: int = 1,  
+    num_images: int = 1,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     global models
@@ -240,6 +242,7 @@ def generate_60(
         "num_inference_steps": num_inference_steps,
         "generator": generator,
         "output_type": "pil",
+        "target_size": (width,height),
     }
     if use_resolution_binning:
         options["use_resolution_binning"] = True