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Make Dreambooth SD LoRA Training Script torch.compile compatible #6534

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merged 1 commit into from
Jan 12, 2024
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Make Dreambooth SD LoRA Training Script torch.compile compatible #6534

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33 changes: 22 additions & 11 deletions examples/dreambooth/train_dreambooth_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,7 @@
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module


# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
Expand Down Expand Up @@ -647,6 +648,7 @@ def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_atte
prompt_embeds = text_encoder(
text_input_ids,
attention_mask=attention_mask,
return_dict=False,
)
prompt_embeds = prompt_embeds[0]

Expand Down Expand Up @@ -843,6 +845,11 @@ def main(args):
)
text_encoder.add_adapter(text_lora_config)

def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model

# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
Expand All @@ -852,9 +859,9 @@ def save_model_hook(models, weights, output_dir):
text_encoder_lora_layers_to_save = None

for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
if isinstance(model, type(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))):
elif isinstance(model, type(unwrap_model(text_encoder))):
text_encoder_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
Expand All @@ -877,9 +884,9 @@ def load_model_hook(models, input_dir):
while len(models) > 0:
model = models.pop()

if isinstance(model, type(accelerator.unwrap_model(unet))):
if isinstance(model, type(unwrap_model(unet))):
unet_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder))):
elif isinstance(model, type(unwrap_model(text_encoder))):
text_encoder_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
Expand Down Expand Up @@ -1118,7 +1125,7 @@ def compute_text_embeddings(prompt):
text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
)

if accelerator.unwrap_model(unet).config.in_channels == channels * 2:
if unwrap_model(unet).config.in_channels == channels * 2:
noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)

if args.class_labels_conditioning == "timesteps":
Expand All @@ -1128,8 +1135,12 @@ def compute_text_embeddings(prompt):

# Predict the noise residual
model_pred = unet(
noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels
).sample
noisy_model_input,
timesteps,
encoder_hidden_states,
class_labels=class_labels,
return_dict=False,
)[0]

# if model predicts variance, throw away the prediction. we will only train on the
# simplified training objective. This means that all schedulers using the fine tuned
Expand Down Expand Up @@ -1215,8 +1226,8 @@ def compute_text_embeddings(prompt):
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=None if args.pre_compute_text_embeddings else accelerator.unwrap_model(text_encoder),
unet=unwrap_model(unet),
text_encoder=None if args.pre_compute_text_embeddings else unwrap_model(text_encoder),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
Expand Down Expand Up @@ -1284,13 +1295,13 @@ def compute_text_embeddings(prompt):
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unwrap_model(unet)
unet = unet.to(torch.float32)

unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))

if args.train_text_encoder:
text_encoder = accelerator.unwrap_model(text_encoder)
text_encoder = unwrap_model(text_encoder)
text_encoder_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder))
else:
text_encoder_state_dict = None
Expand Down