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[LoRA] make LoRAs trained with peft loadable when peft isn't installed #6306

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merged 3 commits into from
Dec 27, 2023
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[LoRA] make LoRAs trained with peft loadable when peft isn't installed #6306

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82c881d
spit diffusers-native format from the get go.
sayakpaul Dec 24, 2023
c05ed5a
rejig the peft_to_diffusers mapping.
sayakpaul Dec 24, 2023
feb4bff
Merge branch 'main' into make-peft-sd-work-non-peft
sayakpaul Dec 25, 2023
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22 changes: 15 additions & 7 deletions examples/dreambooth/train_dreambooth_lora_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@
from diffusers.loaders import LoraLoaderMixin
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available


Expand Down Expand Up @@ -1019,11 +1019,15 @@ def save_model_hook(models, weights, output_dir):

for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_lora_layers_to_save = get_peft_model_state_dict(model)
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 = get_peft_model_state_dict(model)
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 = get_peft_model_state_dict(model)
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 Down Expand Up @@ -1615,13 +1619,17 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unet.to(torch.float32)
unet_lora_layers = get_peft_model_state_dict(unet)
unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))

if args.train_text_encoder:
text_encoder_one = accelerator.unwrap_model(text_encoder_one)
text_encoder_lora_layers = get_peft_model_state_dict(text_encoder_one.to(torch.float32))
text_encoder_lora_layers = convert_state_dict_to_diffusers(
get_peft_model_state_dict(text_encoder_one.to(torch.float32))
)
text_encoder_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_2_lora_layers = get_peft_model_state_dict(text_encoder_two.to(torch.float32))
text_encoder_2_lora_layers = convert_state_dict_to_diffusers(
get_peft_model_state_dict(text_encoder_two.to(torch.float32))
)
else:
text_encoder_lora_layers = None
text_encoder_2_lora_layers = None
Expand Down
8 changes: 8 additions & 0 deletions src/diffusers/utils/state_dict_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,6 +79,14 @@ class StateDictType(enum.Enum):
".v_proj.lora_A": ".v_proj.lora_linear_layer.down",
".out_proj.lora_B": ".out_proj.lora_linear_layer.up",
".out_proj.lora_A": ".out_proj.lora_linear_layer.down",
"to_k.lora_A": "to_k.lora.down",
"to_k.lora_B": "to_k.lora.up",
"to_q.lora_A": "to_q.lora.down",
"to_q.lora_B": "to_q.lora.up",
"to_v.lora_A": "to_v.lora.down",
"to_v.lora_B": "to_v.lora.up",
"to_out.0.lora_A": "to_out.0.lora.down",
"to_out.0.lora_B": "to_out.0.lora.up",
}

DIFFUSERS_OLD_TO_DIFFUSERS = {
Expand Down
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