124 lines
4.8 KiB
Python
124 lines
4.8 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from typing import Optional
|
|
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
|
|
class LoRALinearLayer(nn.Module):
|
|
def __init__(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None):
|
|
super().__init__()
|
|
|
|
if rank > min(in_features, out_features):
|
|
raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
|
|
|
|
self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
|
|
self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
|
|
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
|
|
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
|
|
self.network_alpha = network_alpha
|
|
self.rank = rank
|
|
|
|
nn.init.normal_(self.down.weight, std=1 / rank)
|
|
nn.init.zeros_(self.up.weight)
|
|
|
|
def forward(self, hidden_states):
|
|
orig_dtype = hidden_states.dtype
|
|
dtype = self.down.weight.dtype
|
|
|
|
down_hidden_states = self.down(hidden_states.to(dtype))
|
|
up_hidden_states = self.up(down_hidden_states)
|
|
|
|
if self.network_alpha is not None:
|
|
up_hidden_states *= self.network_alpha / self.rank
|
|
|
|
return up_hidden_states.to(orig_dtype)
|
|
|
|
|
|
class LoRAConv2dLayer(nn.Module):
|
|
def __init__(
|
|
self, in_features, out_features, rank=4, kernel_size=(1, 1), stride=(1, 1), padding=0, network_alpha=None
|
|
):
|
|
super().__init__()
|
|
|
|
if rank > min(in_features, out_features):
|
|
raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
|
|
|
|
self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
|
|
# according to the official kohya_ss trainer kernel_size are always fixed for the up layer
|
|
# # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129
|
|
self.up = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
|
|
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
|
|
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
|
|
self.network_alpha = network_alpha
|
|
self.rank = rank
|
|
|
|
nn.init.normal_(self.down.weight, std=1 / rank)
|
|
nn.init.zeros_(self.up.weight)
|
|
|
|
def forward(self, hidden_states):
|
|
orig_dtype = hidden_states.dtype
|
|
dtype = self.down.weight.dtype
|
|
|
|
down_hidden_states = self.down(hidden_states.to(dtype))
|
|
up_hidden_states = self.up(down_hidden_states)
|
|
|
|
if self.network_alpha is not None:
|
|
up_hidden_states *= self.network_alpha / self.rank
|
|
|
|
return up_hidden_states.to(orig_dtype)
|
|
|
|
|
|
class LoRACompatibleConv(nn.Conv2d):
|
|
"""
|
|
A convolutional layer that can be used with LoRA.
|
|
"""
|
|
|
|
def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.lora_layer = lora_layer
|
|
|
|
def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
|
|
self.lora_layer = lora_layer
|
|
|
|
def forward(self, x):
|
|
if self.lora_layer is None:
|
|
# make sure to the functional Conv2D function as otherwise torch.compile's graph will break
|
|
# see: https://github.com/huggingface/diffusers/pull/4315
|
|
return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
|
else:
|
|
return super().forward(x) + self.lora_layer(x)
|
|
|
|
|
|
class LoRACompatibleLinear(nn.Linear):
|
|
"""
|
|
A Linear layer that can be used with LoRA.
|
|
"""
|
|
|
|
def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.lora_layer = lora_layer
|
|
|
|
def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
|
|
self.lora_layer = lora_layer
|
|
|
|
def forward(self, x):
|
|
if self.lora_layer is None:
|
|
return super().forward(x)
|
|
else:
|
|
return super().forward(x) + self.lora_layer(x)
|