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import os.path
import ipdb
from peft import set_peft_model_state_dict,get_peft_model_state_dict
from diffusers import FluxPipeline
from diffusers.training_utils import cast_training_params
def save_model_hook(models, weights, output_dir,wanted_model, accelerator,adapter_names):
if accelerator.is_main_process:
transformer_lora_layers_to_save = None
for model in models:
if isinstance(model, type(accelerator.unwrap_model(wanted_model))):
transformer_lora_layers_to_save = {adapter_name: get_peft_model_state_dict(model,adapter_name=adapter_name) for adapter_name in adapter_names}
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
if weights:
weights.pop()
for adapter_name,lora in transformer_lora_layers_to_save.items():
FluxPipeline.save_lora_weights(
os.path.join(output_dir,adapter_name),
transformer_lora_layers=lora,
)
def load_model_hook(models, input_dir,wanted_model, accelerator,adapter_names):
transformer_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(accelerator.unwrap_model(wanted_model))):
transformer_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict_list = []
for adapter_name in adapter_names:
lora_path = os.path.join(input_dir,adapter_name)
lora_state_dict_list.append(FluxPipeline.lora_state_dict(lora_path))
transformer_lora_state_dict_list = []
for lora_state_dict in lora_state_dict_list:
transformer_lora_state_dict_list.append({
f'{k.replace("transformer.", "")}': v
for k, v in lora_state_dict.items()
if k.startswith("transformer.") and "lora" in k
})
incompatible_keys = [set_peft_model_state_dict(transformer_, transformer_lora_state_dict_list[i], adapter_name=adapter_name) for i,adapter_name in enumerate(adapter_names)]
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
accelerator.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Make sure the trainable params are in float32. This is again needed since the base models
# are in `weight_dtype`. More details:
# https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
if accelerator.mixed_precision == "fp16":
models = [transformer_]
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models) |