bump gradio native image slider

README.md

@@ -4,7 +4,7 @@ emoji: 🔍🕵️
 colorFrom: pink
 colorTo: pink
 sdk: gradio
-sdk_version: 5.26.0
+sdk_version: 5.27.0
 app_file: app.py
 pinned: false
 suggested_hardware: t4-medium

app.py

@@ -1,6 +1,5 @@
 import spaces
 import gradio as gr
-from gradio_imageslider import ImageSlider
 import torch
 
 torch.jit.script = lambda f: f
@@ -140,10 +139,9 @@ def predict(
     )
     print(f"Time taken: {time.time() - last_time}")
     return (
-        # (padded_image, images.images[0]),
         padded_image,
         anyline_image,
-        images.images[0],
+        (padded_image, images.images[0]),
     )
 
 
@@ -254,8 +252,7 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
             btn = gr.Button()
         with gr.Column(scale=2):
             with gr.Group():
-                output_image = gr.Image(type="pil", label="Output Image")
-                # image_slider = ImageSlider(position=0.5)
+                image_slider = gr.ImageSlider()
             with gr.Row():
                 padded_image = gr.Image(type="pil", label="Padded Image")
                 anyline_image = gr.Image(type="pil", label="Anyline Image")
@@ -273,7 +270,7 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
         guassian_sigma,
         intensity_threshold,
     ]
-    outputs = [padded_image, anyline_image, output_image]
+    outputs = [padded_image, anyline_image, image_slider]
     btn.click(lambda x: None, inputs=None, outputs=outputs).then(
         fn=predict, inputs=inputs, outputs=outputs
     )

requirements.txt

@@ -1,7 +1,7 @@
 diffusers>=0.25.0
 setuptools
 hidiffusion==0.1.10
-gradio==5.26.0
+gradio==5.27.0
 accelerate
 transformers
 torchvision
@@ -9,7 +9,6 @@ xformers
 accelerate
 invisible-watermark
 hf-transfer
-gradio_imageslider
 compel
 opencv-python
 numpy