fix width and height

app.py

@@ -8,6 +8,7 @@ from kolors.models.tokenization_chatglm import ChatGLMTokenizer
 from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
 import gradio as gr
 import numpy as np
+from PIL import Image
 
 device = "cuda"
 ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-Inpainting")
@@ -34,18 +35,17 @@ MAX_IMAGE_SIZE = 1024
 @spaces.GPU
 def infer(prompt, 
           image,
-        #   mask_image,
           negative_prompt = "", 
           seed = 0, 
           randomize_seed = False, 
-          width = 1024, 
-          height = 1024, 
           guidance_scale = 5.0, 
           num_inference_steps = 25
           ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
+    pil_image = Image.open(image)
+    width, height = pil_image.size
     result = pipe(
         prompt = prompt,
         image = image['background'],
@@ -108,21 +108,6 @@ with gr.Blocks(css=css) as Kolors:
                     value=0,
                 )
                 randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                with gr.Row():
-                    width = gr.Slider(
-                        label="Width",
-                        minimum=256,
-                        maximum=MAX_IMAGE_SIZE,
-                        step=32,
-                        value=1024,
-                    )
-                    height = gr.Slider(
-                        label="Height",
-                        minimum=256,
-                        maximum=MAX_IMAGE_SIZE,
-                        step=32,
-                        value=1024,
-                    )
                 with gr.Row():
                     guidance_scale = gr.Slider(
                         label="Guidance scale",
@@ -154,7 +139,7 @@ with gr.Blocks(css=css) as Kolors:
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, image, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
         outputs = [result]
     )