Update app.py

app.py

@@ -34,7 +34,7 @@ import numpy as np
 import cv2
 import os
 import random
-
+from gradio_imageslider import ImageSlider
 
 style_list = [
     {
@@ -143,7 +143,7 @@ def preprocess_image(image: Image.Image,
     prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
     
     print("params:", prompt, negative_prompt, style_name, num_steps, guidance_scale, controlnet_conditioning_scale)
-    image = pipe_control(
+    output = pipe_control(
         prompt=prompt,
         negative_prompt=negative_prompt,
         image=image,
@@ -154,8 +154,8 @@ def preprocess_image(image: Image.Image,
         height=new_height).images[0]
     
         
-    processed_image = pipeline.preprocess_image(image)
-    return processed_image
+    processed_image = pipeline.preprocess_image(output)
+    return (image, processed_image)
 
 
 def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
@@ -254,7 +254,7 @@ def image_to_3d(
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     if not is_multiimage:
         outputs = pipeline.run(
-            image['composite'],
+            image[1]['composite'],
             seed=seed,
             formats=["gaussian", "mesh"],
             preprocess_image=False,
@@ -387,6 +387,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                     #image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
                     with gr.Column():    
                         image_prompt = gr.ImageMask(type="pil", image_mode="RGB", height=512, value={"background":Image.new("RGB", (512, 512), (255, 255, 255)), "layers":[Image.new("RGB", (512, 512), (255, 255, 255))], "composite":Image.new("RGB", (512, 512), (255, 255, 255))})
+                        image_prompt_processed = ImageSlider(type="pil", height=512)
                     with gr.Row():
                         sketch_btn = gr.Button("process sketch")
                         generate_btn = gr.Button("Generate 3D")
@@ -517,7 +518,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     ).then(
         preprocess_image,
         inputs=[image_prompt, prompt, negative_prompt, style, num_steps, guidance_scale, controlnet_conditioning_scale],
-        outputs=[image_prompt],
+        outputs=[image_prompt_processed],
     )
     multiimage_prompt.upload(
         preprocess_images,
@@ -531,7 +532,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         outputs=[seed],
     ).then(
         image_to_3d,
-        inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
+        inputs=[image_prompt_processed, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
         outputs=[output_buf, video_output],
     ).then(
         lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),