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import re

import torch

import logging

# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py

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

# 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."),

("q.", "to_q."),

("k.", "to_k."),

("v.", "to_v."),

("proj_out.", "to_out.0."),

("proj_out.", "proj_attn."),

]

def reshape_weight_for_sd(w, conv3d=False):

# convert HF linear weights to SD conv2d weights

if conv3d:

return w.reshape(*w.shape, 1, 1, 1)

else:

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()}

conv3d = False

for k, v in mapping.items():

for sd_part, hf_part in vae_conversion_map:

v = v.replace(hf_part, sd_part)

if v.endswith(".conv.weight"):

if not conv3d and vae_state_dict[k].ndim == 5:

conv3d = True

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:

logging.debug(f"Reshaping {k} for SD format")

new_state_dict[k] = reshape_weight_for_sd(v, conv3d=conv3d)

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}

# This function exists because at the time of writing torch.cat can't do fp8 with cuda

def cat_tensors(tensors):

x = 0

for t in tensors:

x += t.shape[0]

shape = [x] + list(tensors[0].shape)[1:]

out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)

x = 0

for t in tensors:

out[x:x + t.shape[0]] = t

x += t.shape[0]

return out

def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):

new_state_dict = {}

capture_qkv_weight = {}

capture_qkv_bias = {}

for k, v in text_enc_dict.items():

if not k.startswith(prefix):

continue

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

text_proj = "transformer.text_projection.weight"

if k.endswith(text_proj):

new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()

else:

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"] = cat_tensors(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"] = cat_tensors(tensors)

return new_state_dict

def convert_text_enc_state_dict(text_enc_dict):

return text_enc_dict