Update app.py

app.py

@@ -28,7 +28,7 @@ from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInst
 from controlnet_aux import ZoeDetector
 
 from compel import Compel, ReturnedEmbeddingsType
-from gradio_imageslider import ImageSlider
+#from gradio_imageslider import ImageSlider
 
 with open("sdxl_loras.json", "r") as file:
     data = json.load(file)
@@ -205,13 +205,13 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 del lora_model
                 gc.collect()
 
-def run_lora(images, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
+def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
     global last_lora, last_merged, last_fused, pipe
-    print("Images:", images)
-    print("Face image", images[0])
-    face_image = images[0]
+    #print("Images:", images)
+    #print("Face image", images[0])
+    #face_image = images[0]
     face_image = center_crop_image_as_square(face_image)
-    print("Cropped image:", face_image)
+    #print("Cropped image:", face_image)
     face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
     face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
     face_emb = face_info['embedding']
@@ -301,7 +301,7 @@ def run_lora(images, prompt, negative, lora_scale, selected_state, face_strength
     ).images[0]
     last_lora = repo_name
     gc.collect()
-    return (face_image, image), gr.update(visible=True)
+    return image, gr.update(visible=True)
 
 def shuffle_gallery(sdxl_loras):
     random.shuffle(sdxl_loras)
@@ -328,7 +328,7 @@ with gr.Blocks(css="custom.css") as demo:
     with gr.Row(elem_id="main_app"):
         with gr.Column(scale=2):
             with gr.Group(elem_id="gallery_box"):
-                photo = ImageSlider(label="Upload a picture of yourself", interactive=True, type="pil")
+                photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil")
                 selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
                 order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
                 #new_gallery = gr.Gallery(