khanon commited on
Commit
6e1c7b7
1 Parent(s): 1350f17

adds loss values to most LoRAs

Browse files
chise/README.md CHANGED
@@ -31,6 +31,7 @@ For her swimsuit alt: `side ponytail, swimsuit, striped bikini, see-through, see
31
  - 9 repeats for swimsuit outfit
32
  - 3 batch size, 4 epochs
33
  - `(76*9 + 43*15) / 3 * 4` = 1772 steps
 
34
  - 832x832 training resolution
35
  - `constant_with_warmup` scheduler
36
  - Initially tagged with WD1.4 Convnext-v2 model, then heavily edited.
 
31
  - 9 repeats for swimsuit outfit
32
  - 3 batch size, 4 epochs
33
  - `(76*9 + 43*15) / 3 * 4` = 1772 steps
34
+ - 0.0837 loss
35
  - 832x832 training resolution
36
  - `constant_with_warmup` scheduler
37
  - Initially tagged with WD1.4 Convnext-v2 model, then heavily edited.
koharu/README.md CHANGED
@@ -36,6 +36,7 @@ Some of her swimsuits are in the training data, too.
36
  - 5 repeats for flustered expression outfit
37
  - 3 batch size, 7 epochs
38
  - `(170*4 + 13*5) / 3 * 7` = 1739 steps
 
39
  - 832x832 training resolution
40
  - `constant_with_warmup` scheduler
41
  - Initially tagged using scraped Danbooru tags, then heavily edited.
 
36
  - 5 repeats for flustered expression outfit
37
  - 3 batch size, 7 epochs
38
  - `(170*4 + 13*5) / 3 * 7` = 1739 steps
39
+ - 0.078 loss
40
  - 832x832 training resolution
41
  - `constant_with_warmup` scheduler
42
  - Initially tagged using scraped Danbooru tags, then heavily edited.
mari/README.md CHANGED
@@ -38,6 +38,7 @@ For her swimsuit: `black one-piece swimsuit, frilled swimsuit, frills, paw print
38
  - 3 batch size, 7 epochs
39
  - `(253*3) / 3 * 7` = 1771 steps
40
  - Increased steps for stronger fit due to larger, more varied dataset.
 
41
  - 768x768 training resolution
42
  - `constant_with_warmup` scheduler
43
  - Initially tagged with WD1.4 Convnext model, then heavily edited.
 
38
  - 3 batch size, 7 epochs
39
  - `(253*3) / 3 * 7` = 1771 steps
40
  - Increased steps for stronger fit due to larger, more varied dataset.
41
+ - 0.0823 loss
42
  - 768x768 training resolution
43
  - `constant_with_warmup` scheduler
44
  - Initially tagged with WD1.4 Convnext model, then heavily edited.
reisa/README.md CHANGED
@@ -32,6 +32,7 @@ For her normal outfit: `school uniform, grey shirt, blue jacket, pleated skirt,
32
  - 13 repeats
33
  - 3 batch size, 4 epochs
34
  - `(89*13) / 3 * 4` = 1543 steps
 
35
  - 768x768 training resolution
36
  - `constant_with_warmup` scheduler instead of `cosine`
37
  - Initially tagged with WD1.4 Convnextv2 model. Tags were edited for accuracy and improved outfit consistency. Tags for facial expressions, camera angle, and image composition were added.
 
32
  - 13 repeats
33
  - 3 batch size, 4 epochs
34
  - `(89*13) / 3 * 4` = 1543 steps
35
+ - 0.0743 loss
36
  - 768x768 training resolution
37
  - `constant_with_warmup` scheduler instead of `cosine`
38
  - Initially tagged with WD1.4 Convnextv2 model. Tags were edited for accuracy and improved outfit consistency. Tags for facial expressions, camera angle, and image composition were added.
seia/README.md CHANGED
@@ -31,6 +31,7 @@ For her expressions: `expressionless` / `light smile` / `parted lips`
31
  - 13 repeats
32
  - 3 batch size, 4 epochs
33
  - `(96 * 13) / 3 * 4` = 1664 steps
 
34
  - Initially tagged with WD1.4 swin-v2 model. Tags pruned/edited for consistency.
35
  - `constant_with_warmup` scheduler
36
  - 1.5e-5 text encoder LR
 
31
  - 13 repeats
32
  - 3 batch size, 4 epochs
33
  - `(96 * 13) / 3 * 4` = 1664 steps
34
+ - 0.0764 loss
35
  - Initially tagged with WD1.4 swin-v2 model. Tags pruned/edited for consistency.
36
  - `constant_with_warmup` scheduler
37
  - 1.5e-5 text encoder LR
shizuko/README.md CHANGED
@@ -41,6 +41,7 @@ Her shotgun sling tends to show up on her normal outfit and is a little hard to
41
  - 8 repeats for swimsuit outfit
42
  - 3 batch size, 4 epochs
43
  - `(88*8 + 37*11) / 3 * 4` = 1482 steps
 
44
  - Due to a change in the kohya GUI script, a few of my previous LoRAs (Mari, Michiru, Reisa, Sora, Chise) were accidentally trained without my painstakingly pruned tags. This is probably why they seem overfit to the characters' outfits, though the results were surprisingly good considering there were literally no tags.
45
  - Once I found this issue, I figured since I'd have to "re-learn" some of my settings anyway to account for proper captions, I may as well experiment with batch/dim/alpha/LR valuee. Unfortunately, no conclusive results. Ended up going back to tried and true settings from several LoRAs ago.
46
  - `constant_with_warmup` scheduler instead of `cosine` since it seems to train in fewer steps, at the cost of being more finnicky
 
41
  - 8 repeats for swimsuit outfit
42
  - 3 batch size, 4 epochs
43
  - `(88*8 + 37*11) / 3 * 4` = 1482 steps
44
+ - 0.0958 loss
45
  - Due to a change in the kohya GUI script, a few of my previous LoRAs (Mari, Michiru, Reisa, Sora, Chise) were accidentally trained without my painstakingly pruned tags. This is probably why they seem overfit to the characters' outfits, though the results were surprisingly good considering there were literally no tags.
46
  - Once I found this issue, I figured since I'd have to "re-learn" some of my settings anyway to account for proper captions, I may as well experiment with batch/dim/alpha/LR valuee. Unfortunately, no conclusive results. Ended up going back to tried and true settings from several LoRAs ago.
47
  - `constant_with_warmup` scheduler instead of `cosine` since it seems to train in fewer steps, at the cost of being more finnicky
sora/README.md CHANGED
@@ -33,6 +33,7 @@ Weight 1 is suggested.
33
  - 9 repeats
34
  - 3 batch size, 4 epochs
35
  - `(128*9) / 3 * 4` = 1536 steps
 
36
  - 768x768 training resolution
37
  - `constant_with_warmup` scheduler instead of `cosine`
38
  - Initially tagged with WD1.4 Convnextv2 model. Tags minimally pruned/edited.
 
33
  - 9 repeats
34
  - 3 batch size, 4 epochs
35
  - `(128*9) / 3 * 4` = 1536 steps
36
+ - 0.0791 loss
37
  - 768x768 training resolution
38
  - `constant_with_warmup` scheduler instead of `cosine`
39
  - Initially tagged with WD1.4 Convnextv2 model. Tags minimally pruned/edited.