envs

app.py

@@ -131,6 +131,7 @@ image_examples = [
      "object",
      11318446767408804497,
      "",
+     "turtle"
      ],
     
     ["__asset__/images/object/rose-1.jpg", 
@@ -138,6 +139,7 @@ image_examples = [
      "object",
      6854275249656120509,
      "",
+     "rose",
      ],
     
     ["__asset__/images/object/jellyfish-1.jpg", 
@@ -145,6 +147,7 @@ image_examples = [
      "object",
      17966188172968903484,
      "HelloObject",
+     "jellyfish"
      ],
     
     
@@ -153,6 +156,7 @@ image_examples = [
      "camera",
      7970487946960948963,
      "HelloObject",
+     "lush",
      ],
     
     ["__asset__/images/camera/tusun-1.jpg", 
@@ -160,6 +164,7 @@ image_examples = [
      "camera",
      996953226890228361,
      "TUSUN",
+     "tusun",
      ],
     
     ["__asset__/images/camera/painting-1.jpg", 
@@ -167,6 +172,7 @@ image_examples = [
      "camera",
      16867854766769816385,
      "",
+     "painting"
      ],
 ]
 
@@ -175,7 +181,7 @@ POINTS = {
     'turtle': "__asset__/trajs/object/turtle-1.json",
     'rose': "__asset__/trajs/object/rose-1.json",
     'jellyfish': "__asset__/trajs/object/jellyfish-1.json",
-    'lsuh': "__asset__/trajs/camera/lush-1.json",
+    'lush': "__asset__/trajs/camera/lush-1.json",
     'tusun': "__asset__/trajs/camera/tusun-1.json",
     'painting': "__asset__/trajs/camera/painting-1.json",
 }
@@ -184,7 +190,7 @@ IMAGE_PATH = {
     'turtle': "__asset__/images/object/turtle-1.jpg",
     'rose': "__asset__/images/object/rose-1.jpg",
     'jellyfish': "__asset__/images/object/jellyfish-1.jpg",
-    'lsuh': "__asset__/images/camera/lush-1.jpg",
+    'lush': "__asset__/images/camera/lush-1.jpg",
     'tusun': "__asset__/images/camera/tusun-1.jpg",
     'painting': "__asset__/images/camera/painting-1.jpg",
 }
@@ -277,9 +283,14 @@ class ImageConductor:
 
     @spaces.GPU(enable_queue=True, duration=200)
     @torch.no_grad()
-    def run(self, first_frame_path, tracking_points, prompt, drag_mode, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized):
+    def run(self, first_frame_path, tracking_points, prompt, drag_mode, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized, examples_type):
+        if examples_type != "":
+            ### for adapting high version gradio
+            first_frame_path = IMAGE_PATH[examples_type]
+            tracking_points = json.load(open(POINTS[examples_type]))
+            print("example first_frame_path", first_frame_path)
+            print("example tracking_points", tracking_points)
         
-
         original_width, original_height=384, 256
         if isinstance(tracking_points, list):
             input_all_points = tracking_points
@@ -581,6 +592,7 @@ with block as demo:
                         
                 with gr.Group():
                     personalized = gr.Dropdown(label="Personalized", choices=['HelloObject', 'TUSUN', ""], value="")
+                    examples_type = gr.Textbox(label="Examples Type (Ignore) ",  value="", visible=False)
 
         with gr.Column(scale=7):
             output_video = gr.Video(value=None, 
@@ -590,15 +602,15 @@ with block as demo:
             
             
     with gr.Row():
-        def process_example(input_image, prompt, drag_mode, seed, personalized, ): 
+        def process_example(input_image, prompt, drag_mode, seed, personalized, examples_type): 
 
-            return input_image, prompt, drag_mode, seed, personalized
+            return input_image, prompt, drag_mode, seed, personalized, examples_type
   
         example = gr.Examples(
             label="Input Example",
             examples=image_examples,
-            inputs=[input_image, prompt, drag_mode, seed, personalized],
-            outputs=[input_image, prompt, drag_mode, seed, personalized],
+            inputs=[input_image, prompt, drag_mode, seed, personalized, examples_type],
+            outputs=[input_image, prompt, drag_mode, seed, personalized, examples_type],
             fn=process_example,
             run_on_click=True,
             examples_per_page=10,
@@ -623,7 +635,7 @@ with block as demo:
     input_image.select(add_tracking_points, [tracking_points, first_frame_path, drag_mode], [tracking_points, input_image])
 
     run_button.click(ImageConductor_net.run, [first_frame_path, tracking_points, prompt, drag_mode, 
-                                              negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized], 
+                                              negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized, examples_type], 
                                               [output_image, output_video])
 
 # demo.launch(server_name="0.0.0.0", debug=True, server_port=12345)