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convert_vae_pt_to_diffusers.py
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importargparse
importio
importrequests
importtorch
importyaml
fromdiffusersimportAutoencoderKL
fromdiffusers.pipelines.stable_diffusion.convert_from_ckptimport (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
fromdiffusers.utils.constantsimportDIFFUSERS_REQUEST_TIMEOUT
defcustom_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]) forlayerinvae_state_dictif"encoder.down"inlayer})
down_blocks= {
layer_id: [keyforkeyinvae_state_dictiff"down.{layer_id}"inkey] forlayer_idinrange(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks=len({".".join(layer.split(".")[:3]) forlayerinvae_state_dictif"decoder.up"inlayer})
up_blocks= {
layer_id: [keyforkeyinvae_state_dictiff"up.{layer_id}"inkey] forlayer_idinrange(num_up_blocks)
}
foriinrange(num_down_blocks):
resnets= [
key
forkeyindown_blocks[i]
iff"down.{i}"inkeyandf"down.{i}.downsample"notinkeyand"attn"notinkey
]
attentions= [keyforkeyindown_blocks[i] iff"down.{i}.attn"inkey]
iff"encoder.down.{i}.downsample.conv.weight"invae_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)
paths=renew_vae_attention_paths(attentions)
meta_path= {"old": f"down.{i}.attn", "new": f"down_blocks.{i}.attentions"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets= [keyforkeyinvae_state_dictif"encoder.mid.block"inkey]
num_mid_res_blocks=2
foriinrange(1, num_mid_res_blocks+1):
resnets= [keyforkeyinmid_resnetsiff"encoder.mid.block_{i}"inkey]
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= [keyforkeyinvae_state_dictif"encoder.mid.attn"inkey]
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)
foriinrange(num_up_blocks):
block_id=num_up_blocks-1-i
resnets= [
key
forkeyinup_blocks[block_id]
iff"up.{block_id}"inkeyandf"up.{block_id}.upsample"notinkeyand"attn"notinkey
]
attentions= [keyforkeyinup_blocks[block_id] iff"up.{block_id}.attn"inkey]
iff"decoder.up.{block_id}.upsample.conv.weight"invae_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)
paths=renew_vae_attention_paths(attentions)
meta_path= {"old": f"up.{block_id}.attn", "new": f"up_blocks.{i}.attentions"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets= [keyforkeyinvae_state_dictif"decoder.mid.block"inkey]
num_mid_res_blocks=2
foriinrange(1, num_mid_res_blocks+1):
resnets= [keyforkeyinmid_resnetsiff"decoder.mid.block_{i}"inkey]
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= [keyforkeyinvae_state_dictif"decoder.mid.attn"inkey]
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)
returnnew_checkpoint
defvae_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",
timeout=DIFFUSERS_REQUEST_TIMEOUT,
)
io_obj=io.BytesIO(r.content)
original_config=yaml.safe_load(io_obj)
image_size=512
device="cuda"iftorch.cuda.is_available() else"cpu"
ifcheckpoint_path.endswith("safetensors"):
fromsafetensorsimportsafe_open
checkpoint= {}
withsafe_open(checkpoint_path, framework="pt", device="cpu") asf:
forkeyinf.keys():
checkpoint[key] =f.get_tensor(key)
else:
checkpoint=torch.load(checkpoint_path, map_location=device)["state_dict"]
# 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, 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=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
args=parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
