Change default num_steps

app.py

@@ -9,7 +9,7 @@ import gradio as gr
 import numpy as np
 import PIL.Image
 import torch
-from diffusers import DiffusionPipeline
+from diffusers import AutoencoderKL, DiffusionPipeline
 
 DESCRIPTION = "# SD-XL"
 if not torch.cuda.is_available():
@@ -24,8 +24,10 @@ ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 if torch.cuda.is_available():
+    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
     pipe = DiffusionPipeline.from_pretrained(
         "stabilityai/stable-diffusion-xl-base-1.0",
+        vae=vae,
         torch_dtype=torch.float16,
         use_safetensors=True,
         variant="fp16",
@@ -33,6 +35,7 @@ if torch.cuda.is_available():
     if ENABLE_REFINER:
         refiner = DiffusionPipeline.from_pretrained(
             "stabilityai/stable-diffusion-xl-refiner-1.0",
+            vae=vae,
             torch_dtype=torch.float16,
             use_safetensors=True,
             variant="fp16",
@@ -75,8 +78,8 @@ def generate(
     height: int = 1024,
     guidance_scale_base: float = 5.0,
     guidance_scale_refiner: float = 5.0,
-    num_inference_steps_base: int = 50,
-    num_inference_steps_refiner: int = 50,
+    num_inference_steps_base: int = 25,
+    num_inference_steps_refiner: int = 25,
     apply_refiner: bool = False,
 ) -> PIL.Image.Image:
     generator = torch.Generator().manual_seed(seed)
@@ -211,7 +214,7 @@ with gr.Blocks(css="style.css") as demo:
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=50,
+                value=25,
             )
         with gr.Row(visible=False) as refiner_params:
             guidance_scale_refiner = gr.Slider(
@@ -226,7 +229,7 @@ with gr.Blocks(css="style.css") as demo:
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=50,
+                value=25,
             )
 
     gr.Examples(