thomaseding commited on
Commit
856d141
·
1 Parent(s): fbfd162

Add comparison image

Browse files
Files changed (3) hide show
  1. .gitattributes +1 -0
  2. README.md +2 -0
  3. comparison.png +3 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ *.png filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -4,4 +4,6 @@ license: creativeml-openrail-m
4
 
5
  Stable Diffusion 1.5 fine tuned VAE decoder for better pixel art generation by aliasing the output of the decoder.
6
 
 
 
7
  Fine tuning was done by training 50 thousand images for 1 epoch effective batch size 12. I preprocessed the images to quantize each 8x8 tile to its average color. On a RTX3090, this took about 4 hours to fine-tune. Used only MSE loss at 1e-5 learning rate. The training data set was just generated from other stable diffusion models, mostly cartoon-like images.
 
4
 
5
  Stable Diffusion 1.5 fine tuned VAE decoder for better pixel art generation by aliasing the output of the decoder.
6
 
7
+ ![comparison](https://huggingface.co/thomaseding/vae-teding-aliased-2024-03/resolve/main/comparison.png)
8
+
9
  Fine tuning was done by training 50 thousand images for 1 epoch effective batch size 12. I preprocessed the images to quantize each 8x8 tile to its average color. On a RTX3090, this took about 4 hours to fine-tune. Used only MSE loss at 1e-5 learning rate. The training data set was just generated from other stable diffusion models, mostly cartoon-like images.
comparison.png ADDED

Git LFS Details

  • SHA256: 07d957c664a97247cafac1dd470a4561f974b1214660039fe8960520cebb1b8f
  • Pointer size: 132 Bytes
  • Size of remote file: 4.72 MB