fix import order and deprecate for CVX 2B models

Author: LittleNyima

inference/ddim_inversion.py

@@ -3,8 +3,17 @@
 a video reconstruction based on a provided prompt. It utilizes the CogVideoX pipeline to
 process video frames, apply the DDIM inverse scheduler, and produce an output video.
 
+**Please notice that this script is based on the CogVideoX 5B model, and would not generate
+a good result for 2B variants.**
+
 Usage:
-    python script.py --model-path /path/to/model --prompt "a prompt" --video-path /path/to/video.mp4 --output-path /path/to/output
+    python ddim_inversion.py
+        --model-path /path/to/model
+        --prompt "a prompt"
+        --video-path /path/to/video.mp4
+        --output-path /path/to/output
+
+For more details about the cli arguments, please run `python ddim_inversion.py --help`.
 
 Author:
     LittleNyima <littlenyima[at]163[dot]com>
@@ -15,7 +24,6 @@
 import os
 from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, cast
 
-import decord
 import torch
 import torch.nn.functional as F
 import torchvision.transforms as T
@@ -27,6 +35,10 @@
 from diffusers.schedulers import CogVideoXDDIMScheduler, DDIMInverseScheduler
 from diffusers.utils import export_to_video
 
+# Must import after torch because this can sometimes lead to a nasty segmentation fault, or stack smashing error.
+# Very few bug reports but it happens. Look in decord Github issues for more relevant information.
+import decord  # isort: skip
+
 
 class DDIMInversionArguments(TypedDict):
     model_path: str
@@ -399,6 +411,8 @@ def ddim_inversion(
     device: torch.device,
 ):
     pipeline: CogVideoXPipeline = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device=device)
+    if not pipeline.transformer.config.use_rotary_positional_embeddings:
+        raise NotImplementedError("This script supports CogVideoX 5B model only.")
     video_frames = get_video_frames(
         video_path=video_path,
         width=width,