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"""
LoRA module for Diffusers
==========================
This file works independently and is designed to operate with Diffusers.
Credits
-------
- Modified from: https://github.com/vladmandic/automatic/blob/master/modules/lora_diffusers.py
- Originally from: https://github.com/kohya-ss/sd-scripts/blob/sdxl/networks/lora_diffusers.py
"""
import bisect
import math
import random
from typing import Any, Dict, List, Mapping, Optional, Union
from diffusers import UNet2DConditionModel
import numpy as np
from tqdm import tqdm
from transformers import CLIPTextModel
import torch
def make_unet_conversion_map() -> Dict[str, str]:
unet_conversion_map_layer = []
for i in range(3): # num_blocks is 3 in sdxl
# 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: commentout for sdxl
# 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}.{2}." # change for sdxl
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))
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 = []
for sd, hf in unet_conversion_map_layer:
if "resnets" in hf:
for sd_res, hf_res in unet_conversion_map_resnet:
unet_conversion_map.append((sd + sd_res, hf + hf_res))
else:
unet_conversion_map.append((sd, hf))
for j in range(2):
hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
sd_time_embed_prefix = f"time_embed.{j*2}."
unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
for j in range(2):
hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
sd_label_embed_prefix = f"label_emb.0.{j*2}."
unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
unet_conversion_map.append(("out.0.", "conv_norm_out."))
unet_conversion_map.append(("out.2.", "conv_out."))
sd_hf_conversion_map = {sd.replace(".", "_")[:-1]: hf.replace(".", "_")[:-1] for sd, hf in unet_conversion_map}
return sd_hf_conversion_map
UNET_CONVERSION_MAP = make_unet_conversion_map()
class LoRAModule(torch.nn.Module):
"""
replaces forward method of the original Linear, instead of replacing the original Linear module.
"""
def __init__(
self,
lora_name,
org_module: torch.nn.Module,
multiplier=1.0,
lora_dim=4,
alpha=1,
):
"""if alpha == 0 or None, alpha is rank (no scaling)."""
super().__init__()
self.lora_name = lora_name
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
in_dim = org_module.in_channels
out_dim = org_module.out_channels
else:
in_dim = org_module.in_features
out_dim = org_module.out_features
self.lora_dim = lora_dim
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
kernel_size = org_module.kernel_size
stride = org_module.stride
padding = org_module.padding
self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
else:
self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
if type(alpha) == torch.Tensor:
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
self.scale = alpha / self.lora_dim
self.register_buffer("alpha", torch.tensor(alpha)) # 勾配計算に含めない / not included in gradient calculation
# same as microsoft's
torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
torch.nn.init.zeros_(self.lora_up.weight)
self.multiplier = multiplier
self.org_module = [org_module]
self.enabled = True
self.network: LoRANetwork = None
self.org_forward = None
# override org_module's forward method
def apply_to(self, multiplier=None):
if multiplier is not None:
self.multiplier = multiplier
if self.org_forward is None:
self.org_forward = self.org_module[0].forward
self.org_module[0].forward = self.forward
# restore org_module's forward method
def unapply_to(self):
if self.org_forward is not None:
self.org_module[0].forward = self.org_forward
# forward with lora
# scale is used LoRACompatibleConv, but we ignore it because we have multiplier
def forward(self, x, scale=1.0):
if not self.enabled:
return self.org_forward(x)
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
def set_network(self, network):
self.network = network
# merge lora weight to org weight
def merge_to(self, multiplier=1.0):
# get lora weight
lora_weight = self.get_weight(multiplier)
# get org weight
org_sd = self.org_module[0].state_dict()
org_weight = org_sd["weight"]
weight = org_weight + lora_weight.to(org_weight.device, dtype=org_weight.dtype)
# set weight to org_module
org_sd["weight"] = weight
self.org_module[0].load_state_dict(org_sd)
# restore org weight from lora weight
def restore_from(self, multiplier=1.0):
# get lora weight
lora_weight = self.get_weight(multiplier)
# get org weight
org_sd = self.org_module[0].state_dict()
org_weight = org_sd["weight"]
weight = org_weight - lora_weight.to(org_weight.device, dtype=org_weight.dtype)
# set weight to org_module
org_sd["weight"] = weight
self.org_module[0].load_state_dict(org_sd)
# return lora weight
def get_weight(self, multiplier=None):
if multiplier is None:
multiplier = self.multiplier
# get up/down weight from module
up_weight = self.lora_up.weight.to(torch.float)
down_weight = self.lora_down.weight.to(torch.float)
# pre-calculated weight
if len(down_weight.size()) == 2:
# linear
weight = self.multiplier * (up_weight @ down_weight) * self.scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = (
self.multiplier
* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
* self.scale
)
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
weight = self.multiplier * conved * self.scale
return weight
# Create network from weights for inference, weights are not loaded here
def create_network_from_weights(
text_encoder: Union[CLIPTextModel, List[CLIPTextModel]], unet: UNet2DConditionModel, weights_sd: Dict, multiplier: float = 1.0
):
# get dim/alpha mapping
modules_dim = {}
modules_alpha = {}
for key, value in weights_sd.items():
if "." not in key:
continue
lora_name = key.split(".")[0]
if "alpha" in key:
modules_alpha[lora_name] = value
elif "lora_down" in key:
dim = value.size()[0]
modules_dim[lora_name] = dim
# print(lora_name, value.size(), dim)
# support old LoRA without alpha
for key in modules_dim.keys():
if key not in modules_alpha:
modules_alpha[key] = modules_dim[key]
return LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
def merge_lora_weights(pipe, weights_sd: Dict, multiplier: float = 1.0):
text_encoders = [pipe.text_encoder, pipe.text_encoder_2] if hasattr(pipe, "text_encoder_2") else [pipe.text_encoder]
unet = pipe.unet
lora_network = create_network_from_weights(text_encoders, unet, weights_sd, multiplier=multiplier)
lora_network.load_state_dict(weights_sd)
lora_network.merge_to(multiplier=multiplier)
# block weightや学習に対応しない簡易版 / simple version without block weight and training
class LoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te"
# SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"
def __init__(
self,
text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
unet: UNet2DConditionModel,
multiplier: float = 1.0,
modules_dim: Optional[Dict[str, int]] = None,
modules_alpha: Optional[Dict[str, int]] = None,
varbose: Optional[bool] = False,
) -> None:
super().__init__()
self.multiplier = multiplier
print(f"create LoRA network from weights")
# convert SDXL Stability AI's U-Net modules to Diffusers
converted = self.convert_unet_modules(modules_dim, modules_alpha)
if converted:
print(f"converted {converted} Stability AI's U-Net LoRA modules to Diffusers (SDXL)")
# create module instances
def create_modules(
is_unet: bool,
text_encoder_idx: Optional[int], # None, 1, 2
root_module: torch.nn.Module,
target_replace_modules: List[torch.nn.Module],
) -> List[LoRAModule]:
prefix = (
self.LORA_PREFIX_UNET
if is_unet
else (
self.LORA_PREFIX_TEXT_ENCODER
if text_encoder_idx is None
else (self.LORA_PREFIX_TEXT_ENCODER1 if text_encoder_idx == 1 else self.LORA_PREFIX_TEXT_ENCODER2)
)
)
loras = []
skipped = []
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
is_linear = (
child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear"
)
is_conv2d = (
child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
)
if is_linear or is_conv2d:
lora_name = prefix + "." + name + "." + child_name
lora_name = lora_name.replace(".", "_")
if lora_name not in modules_dim:
# print(f"skipped {lora_name} (not found in modules_dim)")
skipped.append(lora_name)
continue
dim = modules_dim[lora_name]
alpha = modules_alpha[lora_name]
lora = LoRAModule(
lora_name,
child_module,
self.multiplier,
dim,
alpha,
)
loras.append(lora)
return loras, skipped
text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
# create LoRA for text encoder
# 毎回すべてのモジュールを作るのは無駄なので要検討 / it is wasteful to create all modules every time, need to consider
self.text_encoder_loras: List[LoRAModule] = []
skipped_te = []
for i, text_encoder in enumerate(text_encoders):
if len(text_encoders) > 1:
index = i + 1
else:
index = None
text_encoder_loras, skipped = create_modules(False, index, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
self.text_encoder_loras.extend(text_encoder_loras)
skipped_te += skipped
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
if len(skipped_te) > 0:
print(f"skipped {len(skipped_te)} modules because of missing weight for text encoder.")
# extend U-Net target modules to include Conv2d 3x3
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
self.unet_loras: List[LoRAModule]
self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
if len(skipped_un) > 0:
print(f"skipped {len(skipped_un)} modules because of missing weight for U-Net.")
# assertion
names = set()
for lora in self.text_encoder_loras + self.unet_loras:
names.add(lora.lora_name)
for lora_name in modules_dim.keys():
assert lora_name in names, f"{lora_name} is not found in created LoRA modules."
# make to work load_state_dict
for lora in self.text_encoder_loras + self.unet_loras:
self.add_module(lora.lora_name, lora)
# SDXL: convert SDXL Stability AI's U-Net modules to Diffusers
def convert_unet_modules(self, modules_dim, modules_alpha):
converted_count = 0
not_converted_count = 0
map_keys = list(UNET_CONVERSION_MAP.keys())
map_keys.sort()
for key in list(modules_dim.keys()):
if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"):
search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "")
position = bisect.bisect_right(map_keys, search_key)
map_key = map_keys[position - 1]
if search_key.startswith(map_key):
new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key])
modules_dim[new_key] = modules_dim[key]
modules_alpha[new_key] = modules_alpha[key]
del modules_dim[key]
del modules_alpha[key]
converted_count += 1
else:
not_converted_count += 1
assert (
converted_count == 0 or not_converted_count == 0
), f"some modules are not converted: {converted_count} converted, {not_converted_count} not converted"
return converted_count
def set_multiplier(self, multiplier):
self.multiplier = multiplier
for lora in self.text_encoder_loras + self.unet_loras:
lora.multiplier = self.multiplier
def apply_to(self, multiplier=1.0, apply_text_encoder=True, apply_unet=True):
if apply_text_encoder:
print("enable LoRA for text encoder")
for lora in self.text_encoder_loras:
lora.apply_to(multiplier)
if apply_unet:
print("enable LoRA for U-Net")
for lora in self.unet_loras:
lora.apply_to(multiplier)
def unapply_to(self):
for lora in self.text_encoder_loras + self.unet_loras:
lora.unapply_to()
def merge_to(self, multiplier=1.0):
print("merge LoRA weights to original weights")
for lora in tqdm(self.text_encoder_loras + self.unet_loras):
lora.merge_to(multiplier)
print(f"weights are merged")
def restore_from(self, multiplier=1.0):
print("restore LoRA weights from original weights")
for lora in tqdm(self.text_encoder_loras + self.unet_loras):
lora.restore_from(multiplier)
print(f"weights are restored")
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
# convert SDXL Stability AI's state dict to Diffusers' based state dict
map_keys = list(UNET_CONVERSION_MAP.keys()) # prefix of U-Net modules
map_keys.sort()
for key in list(state_dict.keys()):
if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"):
search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "")
position = bisect.bisect_right(map_keys, search_key)
map_key = map_keys[position - 1]
if search_key.startswith(map_key):
new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key])
state_dict[new_key] = state_dict[key]
del state_dict[key]
# in case of V2, some weights have different shape, so we need to convert them
# because V2 LoRA is based on U-Net created by use_linear_projection=False
my_state_dict = self.state_dict()
for key in state_dict.keys():
if state_dict[key].size() != my_state_dict[key].size():
# print(f"convert {key} from {state_dict[key].size()} to {my_state_dict[key].size()}")
state_dict[key] = state_dict[key].view(my_state_dict[key].size())
return super().load_state_dict(state_dict, strict) |