refiners/scripts/conversion/convert_fooocus_control_lora.py
2024-03-08 15:43:57 +01:00

349 lines
12 KiB
Python

import argparse
import logging
from logging import info
from pathlib import Path
from huggingface_hub import hf_hub_download # type: ignore
from torch import Tensor
from torch.nn import Parameter as TorchParameter
from refiners.fluxion.adapters.lora import Lora, LoraAdapter, auto_attach_loras
from refiners.fluxion.layers import Conv2d
from refiners.fluxion.layers.linear import Linear
from refiners.fluxion.utils import load_from_safetensors, save_to_safetensors
from refiners.foundationals.latent_diffusion.lora import SDLoraManager
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import (
ConditionEncoder,
ControlLora,
ControlLoraAdapter,
ZeroConvolution,
)
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
def sort_keys(key: str, /) -> tuple[str, int]:
"""Compute the score of a key, relatively to its suffix.
When used by [`sorted`][sorted], the keys will only be sorted "at the suffix level".
Args:
key: The key to sort.
Returns:
The padded suffix of the key.
The score of the key's suffix.
"""
if "time_embed" in key: # HACK: will place the "time_embed" layers at very start of the list
return ("", -2)
if "label_emb" in key: # HACK: will place the "label_emb" layers right after "time_embed"
return ("", -1)
if "proj_out" in key: # HACK: will place the "proj_out" layers at the end of each "transformer_blocks"
return (key.removesuffix("proj_out") + "transformer_blocks.99.ff.net.2", 10)
return SDLoraManager.sort_keys(key)
def load_lora_layers(
name: str,
state_dict: dict[str, Tensor],
control_lora: ControlLora,
) -> dict[str, Lora[Linear | Conv2d]]:
"""Load the LoRA layers from the state_dict into the ControlLora.
Args:
name: The name of the LoRA.
state_dict: The state_dict of the LoRA.
control_lora: The ControlLora to load the LoRA layers into.
"""
# filter from the state_dict the layers that will be used for the LoRA layers
lora_weights = {f"{key}.weight": value for key, value in state_dict.items() if ".up" in key or ".down" in key}
# move the tensors to the device and dtype of the ControlLora
lora_weights = {
key: value.to(
dtype=control_lora.dtype,
device=control_lora.device,
)
for key, value in lora_weights.items()
}
# load every LoRA layers from the filtered state_dict
lora_layers = Lora.from_dict(name, state_dict=lora_weights)
# sort all the LoRA's keys using the `sort_keys` method
lora_layers = {
key: lora_layers[key]
for key in sorted(
lora_layers.keys(),
key=sort_keys,
)
}
# auto-attach the LoRA layers to the U-Net
auto_attach_loras(lora_layers, control_lora, exclude=["ZeroConvolution", "ConditionEncoder"])
# eject all the LoRA adapters from the U-Net
# because we need each target path as if the adapter wasn't injected
for lora_layer in lora_layers.values():
lora_adapter = lora_layer.parent
assert isinstance(lora_adapter, LoraAdapter)
lora_adapter.eject()
return lora_layers
def load_condition_encoder(
state_dict: dict[str, Tensor],
control_lora: ControlLora,
) -> None:
"""Load the ConditionEncoder's Conv2d layers from the state_dict into the ControlLora.
Args:
state_dict: The state_dict of the ConditionEncoder.
control_lora: The control_lora to load the ConditionEncoder's Conv2d layers into.
"""
# filter from the state_dict the layers that will be used for the ConditionEncoder
condition_encoder_tensors = {key: value for key, value in state_dict.items() if "input_hint_block" in key}
# move the tensors to the device and dtype of the ControlLora
condition_encoder_tensors = {
key: value.to(
dtype=control_lora.dtype,
device=control_lora.device,
)
for key, value in condition_encoder_tensors.items()
}
# find the ConditionEncoder's Conv2d layers
condition_encoder_layer = control_lora.ensure_find(ConditionEncoder)
condition_encoder_conv2ds = list(condition_encoder_layer.layers(Conv2d))
# replace the Conv2d layers' weights and biases with the ones from the state_dict
for i, layer in enumerate(condition_encoder_conv2ds):
layer.weight = TorchParameter(condition_encoder_tensors[f"input_hint_block.{i*2}.weight"])
layer.bias = TorchParameter(condition_encoder_tensors[f"input_hint_block.{i*2}.bias"])
def load_zero_convolutions(
state_dict: dict[str, Tensor],
control_lora: ControlLora,
) -> None:
"""Load the ZeroConvolution's Conv2d layers from the state_dict into the ControlLora.
Args:
state_dict: The state_dict of the ZeroConvolution.
control_lora: The ControlLora to load the ZeroConvolution's Conv2d layers into.
"""
# filter from the state_dict the layers that will be used for the ZeroConvolution layers
zero_convolution_tensors = {key: value for key, value in state_dict.items() if "zero_convs" in key}
n = len(zero_convolution_tensors) // 2
zero_convolution_tensors[f"zero_convs.{n}.0.weight"] = state_dict["middle_block_out.0.weight"]
zero_convolution_tensors[f"zero_convs.{n}.0.bias"] = state_dict["middle_block_out.0.bias"]
# move the tensors to the device and dtype of the ControlLora
zero_convolution_tensors = {
key: value.to(
dtype=control_lora.dtype,
device=control_lora.device,
)
for key, value in zero_convolution_tensors.items()
}
# find the ZeroConvolution's Conv2d layers
zero_convolution_layers = list(control_lora.layers(ZeroConvolution))
zero_convolution_conv2ds = [layer.ensure_find(Conv2d) for layer in zero_convolution_layers]
# replace the Conv2d layers' weights and biases with the ones from the state_dict
for i, layer in enumerate(zero_convolution_conv2ds):
layer.weight = TorchParameter(zero_convolution_tensors[f"zero_convs.{i}.0.weight"])
layer.bias = TorchParameter(zero_convolution_tensors[f"zero_convs.{i}.0.bias"])
def simplify_key(key: str, prefix: str, index: int | None = None) -> str:
"""Simplify a key by stripping everything to the left of the prefix.
Also optionally add a zero-padded index to the prefix.
Example:
>>> simplify_key("foo.bar.ControlLora.something", "ControlLora", 1)
"ControlLora_01.something"
>>> simplify_key("foo.bar.ControlLora.DownBlocks.something", "ControlLora")
"ControlLora.DownBlocks.something"
Args:
key: The key to simplify.
prefix: The prefix to remove.
index: The index to add.
"""
_, right = key.split(prefix, maxsplit=1)
if index:
return f"{prefix}_{index:02d}{right}"
else:
return f"{prefix}{right}"
def convert_lora_layers(
lora_layers: dict[str, Lora[Linear | Conv2d]],
control_lora: ControlLora,
refiners_state_dict: dict[str, Tensor],
) -> None:
"""Convert the LoRA layers to the refiners format.
Args:
lora_layers: The LoRA layers to convert.
control_lora: The ControlLora to convert the LoRA layers from.
refiners_state_dict: The refiners state dict to update with the converted LoRA layers.
"""
for lora_layer in lora_layers.values():
# get the adapter associated with the LoRA layer
lora_adapter = lora_layer.parent
assert isinstance(lora_adapter, LoraAdapter)
# get the path of the adapter's target in the ControlLora
target = lora_adapter.target
path = target.get_path(parent=control_lora.ensure_find_parent(target))
state_dict = {
f"{path}.down": lora_layer.down.weight,
f"{path}.up": lora_layer.up.weight,
}
state_dict = {simplify_key(key, "ControlLora."): param for key, param in state_dict.items()}
refiners_state_dict.update(state_dict)
def convert_zero_convolutions(
control_lora: ControlLora,
refiners_state_dict: dict[str, Tensor],
) -> None:
"""Convert the ZeroConvolution layers to the refiners format.
Args:
control_lora: The ControlLora to convert the ZeroConvolution layers from.
refiners_state_dict: The refiners state dict to update with the converted ZeroConvolution layers.
"""
zero_convolution_layers = list(control_lora.layers(ZeroConvolution))
for i, zero_convolution_layer in enumerate(zero_convolution_layers):
state_dict = zero_convolution_layer.state_dict()
path = zero_convolution_layer.get_path()
state_dict = {f"{path}.{key}": param for key, param in state_dict.items()}
state_dict = {simplify_key(key, "ZeroConvolution", i + 1): param for key, param in state_dict.items()}
refiners_state_dict.update(state_dict)
def convert_condition_encoder(
control_lora: ControlLora,
refiners_state_dict: dict[str, Tensor],
) -> None:
"""Convert the ConditionEncoder to the refiners format.
Args:
control_lora: The ControlLora to convert the ConditionEncoder from.
refiners_state_dict: The refiners state dict to update with the converted ConditionEncoder.
"""
condition_encoder_layer = control_lora.ensure_find(ConditionEncoder)
path = condition_encoder_layer.get_path()
state_dict = condition_encoder_layer.state_dict()
state_dict = {f"{path}.{key}": param for key, param in state_dict.items()}
state_dict = {simplify_key(key, "ConditionEncoder"): param for key, param in state_dict.items()}
refiners_state_dict.update(state_dict)
def convert(
name: str,
state_dict_path: Path,
output_path: Path,
) -> None:
sdxl = StableDiffusion_XL()
info("Stable Diffusion XL model initialized")
fooocus_state_dict = load_from_safetensors(state_dict_path)
info(f"Fooocus weights loaded from: {state_dict_path}")
control_lora_adapter = ControlLoraAdapter(target=sdxl.unet, name=name).inject()
control_lora = control_lora_adapter.control_lora
info("ControlLoraAdapter initialized")
lora_layers = load_lora_layers(name, fooocus_state_dict, control_lora)
info("LoRA layers loaded")
load_zero_convolutions(fooocus_state_dict, control_lora)
info("ZeroConvolution layers loaded")
load_condition_encoder(fooocus_state_dict, control_lora)
info("ConditionEncoder loaded")
refiners_state_dict: dict[str, Tensor] = {}
convert_lora_layers(lora_layers, control_lora, refiners_state_dict)
info("LoRA layers converted to refiners format")
convert_zero_convolutions(control_lora, refiners_state_dict)
info("ZeroConvolution layers converted to refiners format")
convert_condition_encoder(control_lora, refiners_state_dict)
info("ConditionEncoder converted to refiners format")
output_path.parent.mkdir(parents=True, exist_ok=True)
save_to_safetensors(path=output_path, tensors=refiners_state_dict)
info(f"Converted ControlLora state dict saved to disk at: {output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Convert ControlLora (from Fooocus) weights to refiners.",
)
parser.add_argument(
"--from",
type=Path,
dest="source_path",
default="lllyasviel/misc:control-lora-canny-rank128.safetensors",
help="Path to the state_dict of the ControlLora, or a Hugging Face model ID.",
)
parser.add_argument(
"--to",
type=Path,
dest="output_path",
help=(
"Path to save the converted model (extension will be .safetensors)."
"If not specified, the output path will be the source path with the extension changed to .safetensors."
),
)
parser.add_argument(
"--verbose",
action="store_true",
dest="verbose",
default=False,
help="Use this flag to print verbose output during conversion.",
)
args = parser.parse_args()
if args.verbose:
logging.basicConfig(
level=logging.INFO,
format="%(levelname)s: %(message)s",
)
if not args.source_path.exists():
repo_id, filename = str(args.source_path).split(":")
args.source_path = Path(
hf_hub_download(
repo_id=repo_id,
filename=filename,
)
)
if args.output_path is None:
args.output_path = Path(f"refiners_{args.source_path.stem}.safetensors")
convert(
name=args.source_path.stem,
state_dict_path=args.source_path,
output_path=args.output_path,
)