New examples for Depth guided T2V0

app_depth.py

@@ -7,20 +7,33 @@ on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR"
 def create_demo(model: Model):
 
     examples = [
-        ["__assets__/depth_videos/butterfly.mp4",
-            "white butterfly, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/deer.mp4",
-            "oil painting of a deer, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/fox.mp4",
-            "wild red fox is walking on the grass, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/girl_dancing.mp4",
-            "oil painting of a girl dancing close-up, masterpiece, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/girl_turning.mp4",
-            "oil painting of a beautiful girl, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/halloween.mp4",
-            "beautiful girl halloween style, a high-quality, detailed, and professional photo"],
-        ["__assets__/depth_videos/santa.mp4",
-            "a santa claus, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/girl_dancing.mp4",
+            "A stormtrooper, masterpiece, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/girl_dancing.mp4",
+            "Oil painting of a catwoman, masterpiece, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/girl_dancing.mp4",
+            "Oil painting of a girl dancing closed eyes, masterpiece, a high-quality, detailed, and professional photo"],
+
+        ["__assets__/depth_videos_depth/woman.mp4",
+            "A robot is dancing in the Sahara desert, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/woman.mp4",
+            "Wonder woman is dancing, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/woman.mp4",
+            "Oil painting of a girl dancing close-up, masterpiece, a high-quality, detailed, and professional photo"],
+
+        ["__assets__/depth_videos_depth/man.mp4",
+            "An astronaut is Dancing in space, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/man.mp4",
+            "Iron Man is dancing, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/man.mp4",
+            "Spiderman is Dancing, a high-quality, detailed, and professional photo"],
+
+        ["__assets__/depth_videos_depth/halloween.mp4",
+            "Beautiful blonde girl, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/halloween.mp4",
+            "Beautiful brunette girl, a high-quality, detailed, and professional photo"],
+        ["__assets__/depth_videos_depth/halloween.mp4",
+            "Beautiful red-haired girl, a high-quality, detailed, and professional photo"],
     ]
 
     with gr.Blocks() as demo:

gradio_utils.py

@@ -1,8 +1,6 @@
 import os
 
 # App Canny utils
-
-
 def edge_path_to_video_path(edge_path):
     video_path = edge_path
 
@@ -96,3 +94,21 @@ def logo_name_to_path(name):
     if name in logo_paths:
         return logo_paths[name]
     return name
+
+
+# App Depth utils
+def depth_path_to_video_path(edge_path):
+    video_path = edge_path
+
+    vid_name = edge_path.split("/")[-1]
+    if vid_name == "girl_dancing.mp4":
+        video_path = "__assets__/depth_videos_mp4/girl_dancing.mp4"
+    elif vid_name == "halloween.mp4":
+        video_path = "__assets__/depth_videos_mp4/halloween.mp4"
+    elif vid_name == "man.mp4":
+        video_path = "__assets__/depth_videos_mp4/man.mp4"
+    elif vid_name == "woman.mp4":
+        video_path = "__assets__/depth_videos_mp4/woman.mp4"
+
+    assert os.path.isfile(video_path)
+    return video_path

model.py

@@ -211,7 +211,7 @@ class Model:
                                  use_cf_attn=True,
                                  save_path=None):
         print("Module Depth")
-        video_path = gradio_utils.edge_path_to_video_path(video_path)
+        video_path = gradio_utils.depth_path_to_video_path(video_path)
         if self.model_type != ModelType.ControlNetDepth:
             controlnet = ControlNetModel.from_pretrained(
                 "lllyasviel/sd-controlnet-depth")