Skip to content

[bug fix] fixes #6444 - checkpointing save issue in advanced dreambooth lora sdxl script #6464

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 6 commits into from
Jan 5, 2024
Merged

[bug fix] fixes #6444 - checkpointing save issue in advanced dreambooth lora sdxl script #6464

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
a85fc37
unwrap text encoder when saving hook only for full text encoder tuning
linoytsaban Jan 5, 2024
6189a09
unwrap text encoder when saving hook only for full text encoder tuning
linoytsaban Jan 5, 2024
1d973fa
save embeddings in each checkpoint as well
linoytsaban Jan 5, 2024
aabe1fe
save embeddings in each checkpoint as well
linoytsaban Jan 5, 2024
21b7a1a
save embeddings in each checkpoint as well
linoytsaban Jan 5, 2024
b7fc8af
Update examples/advanced_diffusion_training/train_dreambooth_lora_sdx…
linoytsaban Jan 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 10 additions & 6 deletions examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1316,13 +1316,15 @@ def save_model_hook(models, weights, output_dir):
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
if args.train_text_encoder:
text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
if args.train_text_encoder:
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
else:
raise ValueError(f"unexpected save model: {model.__class__}")

Expand All @@ -1335,6 +1337,8 @@ def save_model_hook(models, weights, output_dir):
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
)
if args.train_text_encoder_ti:
embedding_handler.save_embeddings(f"{output_dir}/{args.output_dir}_emb.safetensors")

def load_model_hook(models, input_dir):
unet_ = None
Expand Down