chore: fix some comments (#355)

docs/report_02.md

@@ -111,7 +111,7 @@ As we get one step closer to the replication of Sora, we find many limitations f
 - **Lack of time consistency**: we find the model cannot generate videos with high time consistency. We think the problem is due to the lack of training FLOPs. We plan to collect more data and continue training the model to improve the time consistency.
 - **Bad human generation**: We find the model cannot generate high-quality human videos. We think the problem is due to the lack of human data. We plan to collect more human data and continue training the model to improve the human generation.
 - **Low aesthetic score**: we find the model's aesthetic score is not high. The problem is due to the lack of aesthetic score filtering, which is not conducted due to IO bottleneck. We plan to filter the data by aesthetic score and finetuning the model to improve the aesthetic score.
-- **Worse quality for longer video genration**: we find with a same prompt, the longer video has worse quality. This means the image quality is not equally adapted to different lengths of sequences.
+- **Worse quality for longer video generation**: we find with a same prompt, the longer video has worse quality. This means the image quality is not equally adapted to different lengths of sequences.
 
 > - **Algorithm & Acceleration**: Zangwei Zheng, Xiangyu Peng, Shenggui Li, Hongxing Liu, Yukun Zhou, Tianyi Li
 > - **Data Collection & Pipeline**: Xiangyu Peng, Zangwei Zheng, Chenhui Shen, Tom Young, Junjie Wang, Chenfeng Yu

gradio/README.md

@@ -12,7 +12,7 @@ We assume that you have already installed `opensora` based on the instructions g
 pip install gradio spaces
 ```
 
-2. Afterwards, you can use the following command to launch different models. Remeber to launch the command in the project root directory instead of the `gradio` folder.
+2. Afterwards, you can use the following command to launch different models. Remember to launch the command in the project root directory instead of the `gradio` folder.
 
 ```bash
 # run the default model v1-HQ-16x256x256

tools/caption/README.md

@@ -6,7 +6,7 @@ Human labeling of videos is expensive and time-consuming. We adopt powerful imag
 
 ## LLaVA Captioning
 
-We extract three frames from the video for captioning. With batch inference, we can achieve 10 times speedup. With approximatly 720p resolution and 1 frames, the speed is 2~3 videos/s on 8 GPUs. If we resize the smaller side to 336, the speed can be 8 videos/s. In Open-Sora v1.1, to lower the cost, we use the 7B model.
+We extract three frames from the video for captioning. With batch inference, we can achieve 10 times speedup. With approximately 720p resolution and 1 frames, the speed is 2~3 videos/s on 8 GPUs. If we resize the smaller side to 336, the speed can be 8 videos/s. In Open-Sora v1.1, to lower the cost, we use the 7B model.
 
 ### Requirement
 

tools/frame_interpolation/utils/flow_utils.py

@@ -23,7 +23,7 @@ def make_colorwheel():
         Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
         URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
     Code follows the original C++ source code of Daniel Scharstein.
-    Code follows the the Matlab source code of Deqing Sun.
+    Code follows the Matlab source code of Deqing Sun.
     Returns:
         np.ndarray: Color wheel
     """