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Running
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Zero
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# ruff: noqa
from diffusers.utils import (
convert_unet_state_dict_to_peft,
get_peft_kwargs,
is_peft_version,
get_adapter_name,
logging,
)
logger = logging.get_logger(__name__)
# patching inject_adapter_in_model and load_peft_state_dict with low_cpu_mem_usage=True until it's merged into diffusers
def load_lora_into_transformer(
cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None
):
"""
This will load the LoRA layers specified in `state_dict` into `transformer`.
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
network_alphas (`Dict[str, float]`):
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
transformer (`SD3Transformer2DModel`):
The Transformer model to load the LoRA layers into.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
"""
from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
keys = list(state_dict.keys())
transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
state_dict = {
k.replace(f"{cls.transformer_name}.", ""): v
for k, v in state_dict.items()
if k in transformer_keys
}
if len(state_dict.keys()) > 0:
# check with first key if is not in peft format
first_key = next(iter(state_dict.keys()))
if "lora_A" not in first_key:
state_dict = convert_unet_state_dict_to_peft(state_dict)
if adapter_name in getattr(transformer, "peft_config", {}):
raise ValueError(
f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
)
rank = {}
for key, val in state_dict.items():
if "lora_B" in key:
rank[key] = val.shape[1]
if network_alphas is not None and len(network_alphas) >= 1:
prefix = cls.transformer_name
alpha_keys = [
k
for k in network_alphas.keys()
if k.startswith(prefix) and k.split(".")[0] == prefix
]
network_alphas = {
k.replace(f"{prefix}.", ""): v
for k, v in network_alphas.items()
if k in alpha_keys
}
lora_config_kwargs = get_peft_kwargs(
rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict
)
if "use_dora" in lora_config_kwargs:
if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
raise ValueError(
"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
)
else:
lora_config_kwargs.pop("use_dora")
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(transformer)
# In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
# otherwise loading LoRA weights will lead to an error
is_model_cpu_offload, is_sequential_cpu_offload = (
cls._optionally_disable_offloading(_pipeline)
)
inject_adapter_in_model(
lora_config, transformer, adapter_name=adapter_name, low_cpu_mem_usage=True
)
incompatible_keys = set_peft_model_state_dict(
transformer, state_dict, adapter_name, low_cpu_mem_usage=True
)
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
# Unsafe code /> |