File size: 6,905 Bytes
496d0db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
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")
|