diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
index d754f53..2084d1a 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -20,11 +20,12 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
- pip install flake8 yapf isort
+ pip install codespell flake8 isort yapf
# modify the folders accordingly
- name: Lint
run: |
+ codespell
flake8 .
isort --check-only --diff realesrgan/ scripts/ inference_realesrgan.py setup.py
yapf -r -d realesrgan/ scripts/ inference_realesrgan.py setup.py
diff --git a/Training.md b/Training.md
index 389fdfc..1e65d2f 100644
--- a/Training.md
+++ b/Training.md
@@ -32,7 +32,7 @@ Here are steps for data preparation.
#### Step 1: [Optional] Generate multi-scale images
For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales.
-You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to geneate multi-scale images.
+You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to generate multi-scale images.
Note that this step can be omitted if you just want to have a fast try.
```bash
diff --git a/docs/anime_model.md b/docs/anime_model.md
index b2e68e7..928f74c 100644
--- a/docs/anime_model.md
+++ b/docs/anime_model.md
@@ -6,7 +6,7 @@
- [PyTorch Inference](#PyTorch-Inference)
- [ncnn Executable File](#ncnn-Executable-File)
- [Comparisons with waifu2x](#Comparisons-with-waifu2x)
-- [Comparions with Sliding Bars](#Comparions-with-Sliding-Bars)
+- [Comparisons with Sliding Bars](#Comparions-with-Sliding-Bars)
diff --git a/inference_realesrgan.py b/inference_realesrgan.py
index 57ec0ce..644ecf0 100644
--- a/inference_realesrgan.py
+++ b/inference_realesrgan.py
@@ -81,10 +81,10 @@ def main():
h, w = img.shape[0:2]
if max(h, w) > 1000 and args.netscale == 4:
import warnings
- warnings.warn('The input image is large, try X2 model for better performace.')
+ warnings.warn('The input image is large, try X2 model for better performance.')
if max(h, w) < 500 and args.netscale == 2:
import warnings
- warnings.warn('The input image is small, try X4 model for better performace.')
+ warnings.warn('The input image is small, try X4 model for better performance.')
try:
if args.face_enhance: