256 lines
9.5 KiB
Python
256 lines
9.5 KiB
Python
import re
|
|
import torch
|
|
|
|
# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
|
|
|
|
# =================#
|
|
# 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
|
|
|
|
|