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

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Merged
sayakpaul merged 6 commits into from
Jan 5, 2024
Merged

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

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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
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16 changes: 10 additions & 6 deletions examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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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