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import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def custom_convert_ldm_vae_checkpoint(checkpoint, config):
vae_state_dict = checkpoint
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
"encoder.conv_out.weight"
]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
"encoder.norm_out.weight"
]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
"encoder.norm_out.bias"
]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
"decoder.conv_out.weight"
]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
"decoder.norm_out.weight"
]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
"decoder.norm_out.bias"
]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len(
{
".".join(layer.split(".")[:3])
for layer in vae_state_dict
if "encoder.down" in layer
}
)
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len(
{
".".join(layer.split(".")[:3])
for layer in vae_state_dict
if "decoder.up" in layer
}
)
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [
key
for key in down_blocks[i]
if f"down.{i}" in key and f"down.{i}.downsample" not in key
]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[
f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
new_checkpoint[
f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key
for key in up_blocks[block_id]
if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[
f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
new_checkpoint[
f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def vae_pt_to_vae_diffuser(
checkpoint_path: str,
output_path: str,
):
# Only support V1
r = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
io_obj = io.BytesIO(r.content)
original_config = OmegaConf.load(io_obj)
image_size = 512
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = torch.load(checkpoint_path, map_location=device)
# Convert the VAE model.
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(
checkpoint["state_dict"], vae_config
)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
vae.save_pretrained(output_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--vae_pt_path",
default="/Users/cwq/code/github/lama-cleaner/scripts/anything-v4.0.vae.pt",
type=str,
help="Path to the VAE.pt to convert.",
)
parser.add_argument(
"--dump_path",
default="diffusion_pytorch_model.bin",
type=str,
help="Path to the VAE.pt to convert.",
)
args = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
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