updated for value

app.py

@@ -29,7 +29,7 @@ if not torch.cuda.is_available():
 
 MAX_SEED = np.iinfo(np.int32).max
 CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2024"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
 ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0")#
@@ -85,9 +85,9 @@ def generate(
     width: int = 1024,
     height: int = 1024,
     guidance_scale_base: float = 5.0,
-    guidance_scale_refiner: float = 5.0,
-    num_inference_steps_base: int = 25,
-    num_inference_steps_refiner: int = 25,
+    guidance_scale_refiner: float = 7.0,
+    num_inference_steps_base: int = 60,
+    num_inference_steps_refiner: int = 35,
     apply_refiner: bool = False,
     progress=gr.Progress(track_tqdm=True),
 ) -> PIL.Image.Image:
@@ -234,14 +234,14 @@ with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
                 minimum=1,
                 maximum=20,
                 step=0.1,
-                value=5.0,
+                value=7.5,
             )
             num_inference_steps_refiner = gr.Slider(
                 label="Number of inference steps for refiner",
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=25,
+                value=30,
             )
 
     gr.Examples(