diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py at main · kn/diffusers

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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

# =================#

# 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 #

# =========================#

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.")

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()}

state_dict = {"state_dict": state_dict}

torch.save(state_dict, args.checkpoint_path)

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