Update app.py

app.py

@@ -1,21 +1,32 @@
 import os
 import random
-
 import gradio as gr
 import PIL.Image
+from gradio_client import Client
 
-DESCRIPTION = "# SDXL"
+DESCRIPTION = "# SDXL Pixelart"
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
 ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
 
-
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     return seed
 
+def pixelate(input_file_path, pixel_size):
+    image = Image.open(input_file_path)
+    image = image.resize(
+        (image.size[0] // pixel_size, image.size[1] // pixel_size),
+        Image.NEAREST
+    )
+    image = image.resize(
+        (image.size[0] * pixel_size, image.size[1] * pixel_size),
+        Image.NEAREST
+    )
+    return image
+
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -33,8 +44,27 @@ def generate(
     num_inference_steps_refiner: int = 25,
     apply_refiner: bool = False,
 )
-    print("hello")
-    #return image
+    client = Client("hysts/SDXL")
+    result = client.predict(
+        prompt=prompt
+        negative_prompt=negative_prompt,
+        prompt_2=prompt_2,
+        negative_prompt_2=negative_prompt_2,
+        use_negative_prompt=use_negative_prompt,
+        use_prompt_2=use_prompt_2,
+        use_negative_prompt_2=use_negative_prompt_2,
+        seed=seed,
+        width=width,
+        height=height,
+        guidance_scale_base=guidance_scale_base,
+        guidance_scale_refiner=guidance_scale_refiner,
+        num_inference_steps_base=num_inference_steps_base,
+        num_inference_steps_refiner=num_inference_steps_refiner,
+        apply_refiner=apply_refiner,
+        api_name="/run"
+    )
+    image = pixelate(result, 16)
+    return image
 
 
 examples = [