mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-24 15:18:46 +00:00
b933fabf31
It is implicitly defined by the underlying cross-attention layer. This also makes it consistent with SDXL.
123 lines
5.1 KiB
Python
123 lines
5.1 KiB
Python
import argparse
|
|
from functools import partial
|
|
from torch import Tensor
|
|
from refiners.fluxion.utils import (
|
|
load_from_safetensors,
|
|
load_metadata_from_safetensors,
|
|
save_to_safetensors,
|
|
)
|
|
from convert_diffusers_unet import setup_converter as convert_unet, Args as UnetConversionArgs
|
|
from convert_transformers_clip_text_model import (
|
|
setup_converter as convert_text_encoder,
|
|
Args as TextEncoderConversionArgs,
|
|
)
|
|
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
|
|
from refiners.foundationals.latent_diffusion import SD1UNet
|
|
from refiners.foundationals.latent_diffusion.lora import LoraTarget
|
|
import refiners.fluxion.layers as fl
|
|
|
|
|
|
def get_unet_mapping(source_path: str) -> dict[str, str]:
|
|
args = UnetConversionArgs(source_path=source_path, verbose=False)
|
|
return convert_unet(args=args).get_mapping()
|
|
|
|
|
|
def get_text_encoder_mapping(source_path: str) -> dict[str, str]:
|
|
args = TextEncoderConversionArgs(source_path=source_path, subfolder="text_encoder", verbose=False)
|
|
return convert_text_encoder(
|
|
args=args,
|
|
).get_mapping()
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description="Converts a refiner's LoRA weights to SD-WebUI's LoRA weights")
|
|
parser.add_argument(
|
|
"-i",
|
|
"--input-file",
|
|
type=str,
|
|
required=True,
|
|
help="Path to the input file with refiner's LoRA weights (safetensors format)",
|
|
)
|
|
parser.add_argument(
|
|
"-o",
|
|
"--output-file",
|
|
type=str,
|
|
default="sdwebui_loras.safetensors",
|
|
help="Path to the output file with sd-webui's LoRA weights (safetensors format)",
|
|
)
|
|
parser.add_argument(
|
|
"--sd15",
|
|
type=str,
|
|
required=False,
|
|
default="runwayml/stable-diffusion-v1-5",
|
|
help="Path (preferred) or repository ID of Stable Diffusion 1.5 model (Hugging Face diffusers format)",
|
|
)
|
|
args = parser.parse_args()
|
|
metadata = load_metadata_from_safetensors(path=args.input_file)
|
|
assert metadata is not None, f"Could not load metadata from {args.input_file}"
|
|
tensors = load_from_safetensors(path=args.input_file)
|
|
|
|
state_dict: dict[str, Tensor] = {}
|
|
|
|
for meta_key, meta_value in metadata.items():
|
|
match meta_key:
|
|
case "unet_targets":
|
|
model = SD1UNet(in_channels=4)
|
|
create_mapping = partial(get_unet_mapping, source_path=args.sd15)
|
|
key_prefix = "unet."
|
|
lora_prefix = "lora_unet_"
|
|
case "text_encoder_targets":
|
|
model = CLIPTextEncoderL()
|
|
create_mapping = partial(get_text_encoder_mapping, source_path=args.sd15)
|
|
key_prefix = "text_encoder."
|
|
lora_prefix = "lora_te_"
|
|
case "lda_targets":
|
|
raise ValueError("SD-WebUI does not support LoRA for the auto-encoder")
|
|
case _:
|
|
raise ValueError(f"Unexpected key in checkpoint metadata: {meta_key}")
|
|
|
|
submodule_to_key: dict[fl.Module, str] = {}
|
|
for name, submodule in model.named_modules():
|
|
submodule_to_key[submodule] = name
|
|
|
|
# SD-WebUI expects LoRA state dicts with keys derived from the diffusers format, e.g.:
|
|
#
|
|
# lora_unet_down_blocks_0_attentions_0_proj_in.alpha
|
|
# lora_unet_down_blocks_0_attentions_0_proj_in.lora_down.weight
|
|
# lora_unet_down_blocks_0_attentions_0_proj_in.lora_up.weight
|
|
# ...
|
|
#
|
|
# Internally SD-WebUI has some logic[1] to convert such keys into the CompVis format. See
|
|
# `convert_diffusers_name_to_compvis` for more details.
|
|
#
|
|
# [1]: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/394ffa7/extensions-builtin/Lora/lora.py#L158-L225
|
|
|
|
refiners_to_diffusers = create_mapping()
|
|
assert refiners_to_diffusers is not None
|
|
|
|
# Compute the corresponding diffusers' keys where LoRA layers must be applied
|
|
lora_injection_points: list[str] = [
|
|
refiners_to_diffusers[submodule_to_key[linear]]
|
|
for target in [LoraTarget(t) for t in meta_value.split(sep=",")]
|
|
for layer in model.layers(layer_type=target.get_class())
|
|
for linear in layer.layers(layer_type=fl.Linear)
|
|
]
|
|
|
|
lora_weights = [w for w in [tensors[k] for k in sorted(tensors) if k.startswith(key_prefix)]]
|
|
assert len(lora_injection_points) == len(lora_weights) // 2
|
|
|
|
# Map LoRA weights to each key using SD-WebUI conventions (proper prefix and suffix, underscores)
|
|
for i, diffusers_key in enumerate(iterable=lora_injection_points):
|
|
lora_key = lora_prefix + diffusers_key.replace(".", "_")
|
|
# Note: no ".alpha" weights (those are used to scale the LoRA by alpha/rank). Refiners uses a scale = 1.0
|
|
# by default (see `lora_calc_updown` in SD-WebUI for more details)
|
|
state_dict[lora_key + ".lora_up.weight"] = lora_weights[2 * i]
|
|
state_dict[lora_key + ".lora_down.weight"] = lora_weights[2 * i + 1]
|
|
|
|
assert state_dict
|
|
save_to_safetensors(path=args.output_file, tensors=state_dict)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|