diff --git a/README.md b/README.md
index e320343..c7b270e 100644
--- a/README.md
+++ b/README.md
@@ -129,7 +129,23 @@ entire_video = torch.cat((video, video_next), dim = 2) # (1, 3, 17 + 14, 256, 25
 # and so on...
 ```
 
-- [ ] todo, add a master sampler class that allows one to pass in all the text, how long each scene lasts, and stitch together the entire video
+Or just import the `make_video` function
+
+```python
+# ...
+
+entire_video, scenes = make_video(phenaki, texts = [
+    'a squirrel examines an acorn buried in the snow',
+    'a cat watches the squirrel from a frosted window sill',
+    'zoom out to show the entire living room, with the cat residing by the window sill'
+], num_frames = (17, 14, 14), prime_lengths = (5, 5))
+
+entire_video.shape # (1, 3, 17 + 14 + 14 = 45, 256, 256)
+
+# scenes - List[Tensor[3]] - video segment of each scene
+```
+
+That's it!
 
 ## Appreciation
 
diff --git a/phenaki_pytorch/__init__.py b/phenaki_pytorch/__init__.py
index 0028ac3..9e4dea4 100644
--- a/phenaki_pytorch/__init__.py
+++ b/phenaki_pytorch/__init__.py
@@ -1 +1 @@
-from phenaki_pytorch.phenaki_pytorch import Phenaki, CViViT, MaskGit, MaskGitTrainWrapper, TokenCritic, CriticTrainer
+from phenaki_pytorch.phenaki_pytorch import Phenaki, CViViT, MaskGit, MaskGitTrainWrapper, TokenCritic, CriticTrainer, make_video
diff --git a/phenaki_pytorch/phenaki_pytorch.py b/phenaki_pytorch/phenaki_pytorch.py
index 63279d5..b5ee0b0 100644
--- a/phenaki_pytorch/phenaki_pytorch.py
+++ b/phenaki_pytorch/phenaki_pytorch.py
@@ -24,6 +24,9 @@ def exists(val):
 def default(val, d):
     return val if exists(val) else d
 
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else (val,) * length
+
 # decorators
 
 def eval_decorator(fn):
@@ -1117,3 +1120,29 @@ def forward(
         )
 
         return loss
+
+# make video function
+
+def make_video(
+    phenaki: Phenaki,
+    texts: List[str],
+    num_frames,
+    prime_lengths
+):
+    num_scenes = len(texts)
+    num_frames = cast_tuple(num_frames, num_scenes)
+
+    prime_lengths = cast_tuple(prime_lengths, num_scenes - 1)
+    prime_lengths = (*prime_lengths, 0) # last scene needs no priming
+
+    entire_video = []
+    video_prime = None
+    scenes = []
+
+    for text, scene_num_frames, next_scene_prime_length in zip(texts, num_frames, prime_lengths):
+        video = phenaki.sample(text = text, prime_frames = video_prime, num_frames = scene_num_frames)
+        scenes.append(video)
+
+        video_prime = video[:, :, -next_scene_prime_length:]
+
+    return torch.cat(scenes, dim = 2), scenes
diff --git a/setup.py b/setup.py
index b601d4d..53ad272 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'phenaki-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.6',
+  version = '0.0.7',
   license='MIT',
   description = 'Phenaki - Pytorch',
   author = 'Phil Wang',