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Make text-to-image SDXL LoRA Training Script torch.compile compatible #6556

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merged 1 commit into from
Jan 15, 2024
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Make text-to-image SDXL LoRA Training Script torch.compile compatible #6556

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43 changes: 26 additions & 17 deletions examples/text_to_image/train_text_to_image_lora_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
from diffusers.training_utils import compute_snr
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 @@ -460,13 +461,12 @@ def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
text_input_ids = text_input_ids_list[i]

prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False
)

# We are only ALWAYS interested in the pooled output of the final text encoder
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds = prompt_embeds[-1][-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
Expand Down Expand Up @@ -640,6 +640,11 @@ def main(args):
if param.requires_grad:
param.data = param.to(torch.float32)

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 @@ -650,13 +655,13 @@ def save_model_hook(models, weights, output_dir):
text_encoder_two_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_one))):
elif isinstance(model, type(unwrap_model(text_encoder_one))):
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))):
elif isinstance(model, type(unwrap_model(text_encoder_two))):
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
Expand All @@ -681,11 +686,11 @@ 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_one))):
elif isinstance(model, type(unwrap_model(text_encoder_one))):
text_encoder_one_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
elif isinstance(model, type(unwrap_model(text_encoder_two))):
text_encoder_two_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
Expand Down Expand Up @@ -1034,8 +1039,12 @@ def compute_time_ids(original_size, crops_coords_top_left):
)
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds, added_cond_kwargs=unet_added_conditions
).sample
noisy_model_input,
timesteps,
prompt_embeds,
added_cond_kwargs=unet_added_conditions,
return_dict=False,
)[0]

# Get the target for loss depending on the prediction type
if args.prediction_type is not None:
Expand Down Expand Up @@ -1128,9 +1137,9 @@ def compute_time_ids(original_size, crops_coords_top_left):
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
text_encoder=accelerator.unwrap_model(text_encoder_one),
text_encoder_2=accelerator.unwrap_model(text_encoder_two),
unet=accelerator.unwrap_model(unet),
text_encoder=unwrap_model(text_encoder_one),
text_encoder_2=unwrap_model(text_encoder_two),
unet=unwrap_model(unet),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
Expand Down Expand Up @@ -1169,12 +1178,12 @@ def compute_time_ids(original_size, crops_coords_top_left):
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unwrap_model(unet)
unet_lora_state_dict = 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_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_one = unwrap_model(text_encoder_one)
text_encoder_two = unwrap_model(text_encoder_two)

text_encoder_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_one))
text_encoder_2_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_two))
Expand Down