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# 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 re | |
import torch | |
from safetensors.torch import save_file | |
# =================# | |
# 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.", "to_q."), # | |
("k.", "to_k."), # | |
("v.", "to_v."), # | |
("proj_out.", "to_out.0."), # | |
] | |
# Original | |
# 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 # | |
# =========================# | |
textenc_conversion_lst = [ | |
# (stable-diffusion, HF Diffusers) | |
("resblocks.", "text_model.encoder.layers."), | |
("ln_1", "layer_norm1"), | |
("ln_2", "layer_norm2"), | |
(".c_fc.", ".fc1."), | |
(".c_proj.", ".fc2."), | |
(".attn", ".self_attn"), | |
("ln_final.", "transformer.text_model.final_layer_norm."), | |
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), | |
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), | |
] | |
protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} | |
textenc_pattern = re.compile("|".join(protected.keys())) | |
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp | |
code2idx = {"q": 0, "k": 1, "v": 2} | |
def convert_text_enc_state_dict_v20(text_enc_dict): | |
new_state_dict = {} | |
capture_qkv_weight = {} | |
capture_qkv_bias = {} | |
for k, v in text_enc_dict.items(): | |
if ( | |
k.endswith(".self_attn.q_proj.weight") | |
or k.endswith(".self_attn.k_proj.weight") | |
or k.endswith(".self_attn.v_proj.weight") | |
): | |
k_pre = k[: -len(".q_proj.weight")] | |
k_code = k[-len("q_proj.weight")] | |
if k_pre not in capture_qkv_weight: | |
capture_qkv_weight[k_pre] = [None, None, None] | |
capture_qkv_weight[k_pre][code2idx[k_code]] = v | |
continue | |
if ( | |
k.endswith(".self_attn.q_proj.bias") | |
or k.endswith(".self_attn.k_proj.bias") | |
or k.endswith(".self_attn.v_proj.bias") | |
): | |
k_pre = k[: -len(".q_proj.bias")] | |
k_code = k[-len("q_proj.bias")] | |
if k_pre not in capture_qkv_bias: | |
capture_qkv_bias[k_pre] = [None, None, None] | |
capture_qkv_bias[k_pre][code2idx[k_code]] = v | |
continue | |
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) | |
new_state_dict[relabelled_key] = v | |
for k_pre, tensors in capture_qkv_weight.items(): | |
if None in tensors: | |
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") | |
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) | |
new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) | |
for k_pre, tensors in capture_qkv_bias.items(): | |
if None in tensors: | |
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") | |
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) | |
new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) | |
return new_state_dict | |
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.") | |
parser.add_argument( | |
"--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." | |
) | |
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") | |
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper | |
is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict | |
if is_v20_model: | |
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm | |
text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} | |
text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) | |
text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} | |
else: | |
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()} | |
if args.use_safetensors: | |
save_file(state_dict, args.checkpoint_path) | |
else: | |
state_dict = {"state_dict": state_dict} | |
torch.save(state_dict, args.checkpoint_path) |