235 lines
8.7 KiB
Python
235 lines
8.7 KiB
Python
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
|
|
# *Only* converts the UNet, VAE, and Text Encoder.
|
|
# Does not convert optimizer state or any other thing.
|
|
|
|
import argparse
|
|
import os.path as osp
|
|
|
|
import torch
|
|
|
|
|
|
# =================#
|
|
# UNet Conversion #
|
|
# =================#
|
|
|
|
unet_conversion_map = [
|
|
# (stable-diffusion, HF Diffusers)
|
|
("time_embed.0.weight", "time_embedding.linear_1.weight"),
|
|
("time_embed.0.bias", "time_embedding.linear_1.bias"),
|
|
("time_embed.2.weight", "time_embedding.linear_2.weight"),
|
|
("time_embed.2.bias", "time_embedding.linear_2.bias"),
|
|
("input_blocks.0.0.weight", "conv_in.weight"),
|
|
("input_blocks.0.0.bias", "conv_in.bias"),
|
|
("out.0.weight", "conv_norm_out.weight"),
|
|
("out.0.bias", "conv_norm_out.bias"),
|
|
("out.2.weight", "conv_out.weight"),
|
|
("out.2.bias", "conv_out.bias"),
|
|
]
|
|
|
|
unet_conversion_map_resnet = [
|
|
# (stable-diffusion, HF Diffusers)
|
|
("in_layers.0", "norm1"),
|
|
("in_layers.2", "conv1"),
|
|
("out_layers.0", "norm2"),
|
|
("out_layers.3", "conv2"),
|
|
("emb_layers.1", "time_emb_proj"),
|
|
("skip_connection", "conv_shortcut"),
|
|
]
|
|
|
|
unet_conversion_map_layer = []
|
|
# hardcoded number of downblocks and resnets/attentions...
|
|
# would need smarter logic for other networks.
|
|
for i in range(4):
|
|
# loop over downblocks/upblocks
|
|
|
|
for j in range(2):
|
|
# loop over resnets/attentions for downblocks
|
|
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
|
|
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
|
|
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
|
|
|
|
if i < 3:
|
|
# no attention layers in down_blocks.3
|
|
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
|
|
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
|
|
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
|
|
|
|
for j in range(3):
|
|
# loop over resnets/attentions for upblocks
|
|
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
|
|
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
|
|
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
|
|
|
|
if i > 0:
|
|
# no attention layers in up_blocks.0
|
|
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
|
|
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
|
|
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
|
|
|
|
if i < 3:
|
|
# no downsample in down_blocks.3
|
|
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
|
|
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
|
|
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
|
|
|
|
# no upsample in up_blocks.3
|
|
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
|
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
|
|
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
|
|
|
|
hf_mid_atn_prefix = "mid_block.attentions.0."
|
|
sd_mid_atn_prefix = "middle_block.1."
|
|
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
|
|
|
|
for j in range(2):
|
|
hf_mid_res_prefix = f"mid_block.resnets.{j}."
|
|
sd_mid_res_prefix = f"middle_block.{2*j}."
|
|
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
|
|
|
|
|
def convert_unet_state_dict(unet_state_dict):
|
|
# buyer beware: this is a *brittle* function,
|
|
# and correct output requires that all of these pieces interact in
|
|
# the exact order in which I have arranged them.
|
|
mapping = {k: k for k in unet_state_dict.keys()}
|
|
for sd_name, hf_name in unet_conversion_map:
|
|
mapping[hf_name] = sd_name
|
|
for k, v in mapping.items():
|
|
if "resnets" in k:
|
|
for sd_part, hf_part in unet_conversion_map_resnet:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
for k, v in mapping.items():
|
|
for sd_part, hf_part in unet_conversion_map_layer:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
|
|
return new_state_dict
|
|
|
|
|
|
# ================#
|
|
# VAE Conversion #
|
|
# ================#
|
|
|
|
vae_conversion_map = [
|
|
# (stable-diffusion, HF Diffusers)
|
|
("nin_shortcut", "conv_shortcut"),
|
|
("norm_out", "conv_norm_out"),
|
|
("mid.attn_1.", "mid_block.attentions.0."),
|
|
]
|
|
|
|
for i in range(4):
|
|
# down_blocks have two resnets
|
|
for j in range(2):
|
|
hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
|
|
sd_down_prefix = f"encoder.down.{i}.block.{j}."
|
|
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
|
|
|
|
if i < 3:
|
|
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
|
|
sd_downsample_prefix = f"down.{i}.downsample."
|
|
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
|
|
|
|
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
|
sd_upsample_prefix = f"up.{3-i}.upsample."
|
|
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
|
|
|
|
# up_blocks have three resnets
|
|
# also, up blocks in hf are numbered in reverse from sd
|
|
for j in range(3):
|
|
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
|
|
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
|
|
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
|
|
|
|
# this part accounts for mid blocks in both the encoder and the decoder
|
|
for i in range(2):
|
|
hf_mid_res_prefix = f"mid_block.resnets.{i}."
|
|
sd_mid_res_prefix = f"mid.block_{i+1}."
|
|
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
|
|
|
|
|
vae_conversion_map_attn = [
|
|
# (stable-diffusion, HF Diffusers)
|
|
("norm.", "group_norm."),
|
|
("q.", "query."),
|
|
("k.", "key."),
|
|
("v.", "value."),
|
|
("proj_out.", "proj_attn."),
|
|
]
|
|
|
|
|
|
def reshape_weight_for_sd(w):
|
|
# convert HF linear weights to SD conv2d weights
|
|
return w.reshape(*w.shape, 1, 1)
|
|
|
|
|
|
def convert_vae_state_dict(vae_state_dict):
|
|
mapping = {k: k for k in vae_state_dict.keys()}
|
|
for k, v in mapping.items():
|
|
for sd_part, hf_part in vae_conversion_map:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
for k, v in mapping.items():
|
|
if "attentions" in k:
|
|
for sd_part, hf_part in vae_conversion_map_attn:
|
|
v = v.replace(hf_part, sd_part)
|
|
mapping[k] = v
|
|
new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
|
|
weights_to_convert = ["q", "k", "v", "proj_out"]
|
|
for k, v in new_state_dict.items():
|
|
for weight_name in weights_to_convert:
|
|
if f"mid.attn_1.{weight_name}.weight" in k:
|
|
print(f"Reshaping {k} for SD format")
|
|
new_state_dict[k] = reshape_weight_for_sd(v)
|
|
return new_state_dict
|
|
|
|
|
|
# =========================#
|
|
# Text Encoder Conversion #
|
|
# =========================#
|
|
# pretty much a no-op
|
|
|
|
|
|
def convert_text_enc_state_dict(text_enc_dict):
|
|
return text_enc_dict
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
|
|
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
|
|
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
assert args.model_path is not None, "Must provide a model path!"
|
|
|
|
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
|
|
|
|
unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
|
|
vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
|
|
text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
|
|
|
|
# Convert the UNet model
|
|
unet_state_dict = torch.load(unet_path, map_location="cpu")
|
|
unet_state_dict = convert_unet_state_dict(unet_state_dict)
|
|
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
|
|
|
|
# Convert the VAE model
|
|
vae_state_dict = torch.load(vae_path, map_location="cpu")
|
|
vae_state_dict = convert_vae_state_dict(vae_state_dict)
|
|
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
|
|
|
|
# Convert the text encoder model
|
|
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
|
|
text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
|
|
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
|
|
|
|
# Put together new checkpoint
|
|
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
|
|
if args.half:
|
|
state_dict = {k: v.half() for k, v in state_dict.items()}
|
|
state_dict = {"state_dict": state_dict}
|
|
torch.save(state_dict, args.checkpoint_path)
|