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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")