VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-09 06:45:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix typos

Browse Source
This commit is contained in:
Kohya S
2023-03-01 21:01:10 +09:00
parent 8abb8645ae
commit ed19a92bbe
5 changed files with 8 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
fine_tune.py
View File

@@ -62,7 +62,7 @@ def train(args):
return return
if cache_latents: if cache_latents:
assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# acceleratorを準備する # acceleratorを準備する
print("prepare accelerator") print("prepare accelerator")

6
library/train_util.py
View File

@@ -547,7 +547,7 @@ class BaseDataset(torch.utils.data.Dataset):
assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}" assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
return image return image
def is_latent_cachable(self): def is_latent_cacheable(self):
return all([not subset.color_aug and not subset.random_crop for subset in self.subsets]) return all([not subset.color_aug and not subset.random_crop for subset in self.subsets])
def cache_latents(self, vae): def cache_latents(self, vae):
@@ -1062,8 +1062,8 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
for dataset in self.datasets: for dataset in self.datasets:
dataset.cache_latents(vae) dataset.cache_latents(vae)
def is_latent_cachable(self) -> bool: def is_latent_cacheable(self) -> bool:
return all([dataset.is_latent_cachable() for dataset in self.datasets]) return all([dataset.is_latent_cacheable() for dataset in self.datasets])
def set_current_epoch(self, epoch): def set_current_epoch(self, epoch):
for dataset in self.datasets: for dataset in self.datasets:

2
train_db.py
View File

@@ -62,7 +62,7 @@ def train(args):
return return
if cache_latents: if cache_latents:
assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# acceleratorを準備する # acceleratorを準備する
print("prepare accelerator") print("prepare accelerator")

4
train_network.py
View File

@@ -99,7 +99,7 @@ def train(args):
return return
if cache_latents: if cache_latents:
assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# acceleratorを準備する # acceleratorを準備する
print("prepare accelerator") print("prepare accelerator")
@@ -341,7 +341,7 @@ def train(args):
metadata["ss_datasets"] = json.dumps(datasets_metadata) metadata["ss_datasets"] = json.dumps(datasets_metadata)
else: else:
# conserving backward compatiblity when using train_dataset_dir and reg_dataset_dir # conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
assert len(train_dataset_group.datasets) == 1, f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。" assert len(train_dataset_group.datasets) == 1, f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。"
dataset = train_dataset_group.datasets[0] dataset = train_dataset_group.datasets[0]

2
train_textual_inversion.py
View File

@@ -197,7 +197,7 @@ def train(args):
return return
if cache_latents: if cache_latents:
assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# モデルに xformers とか memory efficient attention を組み込む # モデルに xformers とか memory efficient attention を組み込む
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers) train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)