Updated README.md

Author: rosinality

README.md

@@ -35,18 +35,38 @@ For example, if you cloned repositories in ~/stylegan2 and downloaded stylegan2-
 
 > python convert_weight.py --repo ~/stylegan2 stylegan2-ffhq-config-f.pkl
 
-This will create converted stylegan2-ffhq-config-f.pt file.  
+This will create converted stylegan2-ffhq-config-f.pt file.
 
 ### Generate samples
 
-> python generate.py --sample N_FACES --pics N_PICS --ckpt PATH_CHECKPOINT  
+> python generate.py --sample N_FACES --pics N_PICS --ckpt PATH_CHECKPOINT
 
-You should change your size (--size 256 for example) if you train with another dimension.   
+You should change your size (--size 256 for example) if you train with another dimension.
 
 ### Project images to latent spaces
 
 > python projector.py --ckpt [CHECKPOINT] --size [GENERATOR_OUTPUT_SIZE] FILE1 FILE2 ...
 
+### Closed-Form Factorization (https://arxiv.org/abs/2007.06600)
+
+You can use `closed_form_factorization.py` and `apply_factor.py` to discover meaningful latent semantic factor or directions in unsupervised manner.
+
+First, you need to extract eigenvectors of weight matrices using `closed_form_factorization.py`
+
+> python closed_form_factorization.py [CHECKPOINT]
+
+This will create factor file that contains eigenvectors. (Default: factor.pt) And you can use `apply_factor.py` to test the meaning of extracted directions
+
+> python apply_factor.py -i [INDEX_OF_EIGENVECTOR] -d [DEGREE_OF_MOVE] -n [NUMBER_OF_SAMPLES] --ckpt [CHECKPOINT] [FACTOR_FILE]
+
+For example,
+
+> python apply_factor.py -i 19 -d 5 -n 10 --ckpt [CHECKPOINT] factor.pt
+
+Will generate 10 random samples, and samples generated from latents that moved along 19th eigenvector with size/degree +-5.
+
+![Sample of closed form factorization](factor_index-13_degree-5.0.png)
+
 ## Pretrained Checkpoints
 
 [Link](https://drive.google.com/open?id=1PQutd-JboOCOZqmd95XWxWrO8gGEvRcO)