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import argparse
import time
from pathlib import Path
from typing import Any, Dict, Literal
import torch
from diffusers import AsymmetricAutoencoderKL
ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG = {
"in_channels": 3,
"out_channels": 3,
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
],
"down_block_out_channels": [128, 256, 512, 512],
"layers_per_down_block": 2,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
],
"up_block_out_channels": [192, 384, 768, 768],
"layers_per_up_block": 3,
"act_fn": "silu",
"latent_channels": 4,
"norm_num_groups": 32,
"sample_size": 256,
"scaling_factor": 0.18215,
}
ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG = {
"in_channels": 3,
"out_channels": 3,
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
],
"down_block_out_channels": [128, 256, 512, 512],
"layers_per_down_block": 2,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
],
"up_block_out_channels": [256, 512, 1024, 1024],
"layers_per_up_block": 5,
"act_fn": "silu",
"latent_channels": 4,
"norm_num_groups": 32,
"sample_size": 256,
"scaling_factor": 0.18215,
}
def convert_asymmetric_autoencoder_kl_state_dict(original_state_dict: Dict[str, Any]) -> Dict[str, Any]:
converted_state_dict = {}
for k, v in original_state_dict.items():
if k.startswith("encoder."):
converted_state_dict[
k.replace("encoder.down.", "encoder.down_blocks.")
.replace("encoder.mid.", "encoder.mid_block.")
.replace("encoder.norm_out.", "encoder.conv_norm_out.")
.replace(".downsample.", ".downsamplers.0.")
.replace(".nin_shortcut.", ".conv_shortcut.")
.replace(".block.", ".resnets.")
.replace(".block_1.", ".resnets.0.")
.replace(".block_2.", ".resnets.1.")
.replace(".attn_1.k.", ".attentions.0.to_k.")
.replace(".attn_1.q.", ".attentions.0.to_q.")
.replace(".attn_1.v.", ".attentions.0.to_v.")
.replace(".attn_1.proj_out.", ".attentions.0.to_out.0.")
.replace(".attn_1.norm.", ".attentions.0.group_norm.")
] = v
elif k.startswith("decoder.") and "up_layers" not in k:
converted_state_dict[
k.replace("decoder.encoder.", "decoder.condition_encoder.")
.replace(".norm_out.", ".conv_norm_out.")
.replace(".up.0.", ".up_blocks.3.")
.replace(".up.1.", ".up_blocks.2.")
.replace(".up.2.", ".up_blocks.1.")
.replace(".up.3.", ".up_blocks.0.")
.replace(".block.", ".resnets.")
.replace("mid", "mid_block")
.replace(".0.upsample.", ".0.upsamplers.0.")
.replace(".1.upsample.", ".1.upsamplers.0.")
.replace(".2.upsample.", ".2.upsamplers.0.")
.replace(".nin_shortcut.", ".conv_shortcut.")
.replace(".block_1.", ".resnets.0.")
.replace(".block_2.", ".resnets.1.")
.replace(".attn_1.k.", ".attentions.0.to_k.")
.replace(".attn_1.q.", ".attentions.0.to_q.")
.replace(".attn_1.v.", ".attentions.0.to_v.")
.replace(".attn_1.proj_out.", ".attentions.0.to_out.0.")
.replace(".attn_1.norm.", ".attentions.0.group_norm.")
] = v
elif k.startswith("quant_conv."):
converted_state_dict[k] = v
elif k.startswith("post_quant_conv."):
converted_state_dict[k] = v
else:
print(f" skipping key `{k}`")
# fix weights shape
for k, v in converted_state_dict.items():
if (
(k.startswith("encoder.mid_block.attentions.0") or k.startswith("decoder.mid_block.attentions.0"))
and k.endswith("weight")
and ("to_q" in k or "to_k" in k or "to_v" in k or "to_out" in k)
):
converted_state_dict[k] = converted_state_dict[k][:, :, 0, 0]
return converted_state_dict
def get_asymmetric_autoencoder_kl_from_original_checkpoint(
scale: Literal["1.5", "2"], original_checkpoint_path: str, map_location: torch.device
) -> AsymmetricAutoencoderKL:
print("Loading original state_dict")
original_state_dict = torch.load(original_checkpoint_path, map_location=map_location)
original_state_dict = original_state_dict["state_dict"]
print("Converting state_dict")
converted_state_dict = convert_asymmetric_autoencoder_kl_state_dict(original_state_dict)
kwargs = ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG if scale == "1.5" else ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG
print("Initializing AsymmetricAutoencoderKL model")
asymmetric_autoencoder_kl = AsymmetricAutoencoderKL(**kwargs)
print("Loading weight from converted state_dict")
asymmetric_autoencoder_kl.load_state_dict(converted_state_dict)
asymmetric_autoencoder_kl.eval()
print("AsymmetricAutoencoderKL successfully initialized")
return asymmetric_autoencoder_kl
if __name__ == "__main__":
start = time.time()
parser = argparse.ArgumentParser()
parser.add_argument(
"--scale",
default=None,
type=str,
required=True,
help="Asymmetric VQGAN scale: `1.5` or `2`",
)
parser.add_argument(
"--original_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the original Asymmetric VQGAN checkpoint",
)
parser.add_argument(
"--output_path",
default=None,
type=str,
required=True,
help="Path to save pretrained AsymmetricAutoencoderKL model",
)
parser.add_argument(
"--map_location",
default="cpu",
type=str,
required=False,
help="The device passed to `map_location` when loading the checkpoint",
)
args = parser.parse_args()
assert args.scale in ["1.5", "2"], f"{args.scale} should be `1.5` of `2`"
assert Path(args.original_checkpoint_path).is_file()
asymmetric_autoencoder_kl = get_asymmetric_autoencoder_kl_from_original_checkpoint(
scale=args.scale,
original_checkpoint_path=args.original_checkpoint_path,
map_location=torch.device(args.map_location),
)
print("Saving pretrained AsymmetricAutoencoderKL")
asymmetric_autoencoder_kl.save_pretrained(args.output_path)
print(f"Done in {time.time() - start:.2f} seconds")