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https://github.com/ostris/ai-toolkit.git
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314 lines
13 KiB
Python
314 lines
13 KiB
Python
import json
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import math
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import os
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import re
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import sys
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from typing import List, Optional, Dict, Type, Union
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import torch
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from transformers import CLIPTextModel
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from .network_mixins import ToolkitNetworkMixin, ToolkitModuleMixin
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from .paths import SD_SCRIPTS_ROOT
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sys.path.append(SD_SCRIPTS_ROOT)
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from networks.lora import LoRANetwork, get_block_index
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from torch.utils.checkpoint import checkpoint
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RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
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# diffusers specific stuff
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LINEAR_MODULES = [
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'Linear',
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'LoRACompatibleLinear'
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# 'GroupNorm',
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]
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CONV_MODULES = [
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'Conv2d',
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'LoRACompatibleConv'
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]
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class LoRAModule(ToolkitModuleMixin, torch.nn.Module):
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"""
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replaces forward method of the original Linear, instead of replacing the original Linear module.
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"""
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def __init__(
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self,
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lora_name,
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org_module: torch.nn.Module,
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multiplier=1.0,
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lora_dim=4,
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alpha=1,
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dropout=None,
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rank_dropout=None,
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module_dropout=None,
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):
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"""if alpha == 0 or None, alpha is rank (no scaling)."""
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super().__init__()
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self.lora_name = lora_name
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self.scalar = torch.tensor(1.0)
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if org_module.__class__.__name__ in CONV_MODULES:
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in_dim = org_module.in_channels
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out_dim = org_module.out_channels
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else:
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in_dim = org_module.in_features
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out_dim = org_module.out_features
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# if limit_rank:
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# self.lora_dim = min(lora_dim, in_dim, out_dim)
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# if self.lora_dim != lora_dim:
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# print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
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# else:
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self.lora_dim = lora_dim
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if org_module.__class__.__name__ in CONV_MODULES:
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kernel_size = org_module.kernel_size
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stride = org_module.stride
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padding = org_module.padding
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self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
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self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
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else:
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self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
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self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
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if type(alpha) == torch.Tensor:
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alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
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alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
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self.scale = alpha / self.lora_dim
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self.register_buffer("alpha", torch.tensor(alpha)) # 定数として扱える
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# same as microsoft's
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torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
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torch.nn.init.zeros_(self.lora_up.weight)
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self.multiplier: Union[float, List[float]] = multiplier
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self.org_module = org_module # remove in applying
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self.dropout = dropout
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self.rank_dropout = rank_dropout
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self.module_dropout = module_dropout
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self.is_checkpointing = False
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self.is_normalizing = False
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self.normalize_scaler = 1.0
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def apply_to(self):
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self.org_forward = self.org_module.forward
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self.org_module.forward = self.forward
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del self.org_module
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class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
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NUM_OF_BLOCKS = 12 # フルモデル相当でのup,downの層の数
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UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
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UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
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TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
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LORA_PREFIX_UNET = "lora_unet"
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LORA_PREFIX_TEXT_ENCODER = "lora_te"
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# SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
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LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
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LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"
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def __init__(
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self,
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text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
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unet,
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multiplier: float = 1.0,
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lora_dim: int = 4,
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alpha: float = 1,
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dropout: Optional[float] = None,
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rank_dropout: Optional[float] = None,
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module_dropout: Optional[float] = None,
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conv_lora_dim: Optional[int] = None,
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conv_alpha: Optional[float] = None,
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block_dims: Optional[List[int]] = None,
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block_alphas: Optional[List[float]] = None,
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conv_block_dims: Optional[List[int]] = None,
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conv_block_alphas: Optional[List[float]] = None,
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modules_dim: Optional[Dict[str, int]] = None,
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modules_alpha: Optional[Dict[str, int]] = None,
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module_class: Type[object] = LoRAModule,
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varbose: Optional[bool] = False,
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train_text_encoder: Optional[bool] = True,
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train_unet: Optional[bool] = True,
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is_sdxl=False,
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is_v2=False,
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) -> None:
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"""
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LoRA network: すごく引数が多いが、パターンは以下の通り
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1. lora_dimとalphaを指定
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2. lora_dim、alpha、conv_lora_dim、conv_alphaを指定
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3. block_dimsとblock_alphasを指定 : Conv2d3x3には適用しない
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4. block_dims、block_alphas、conv_block_dims、conv_block_alphasを指定 : Conv2d3x3にも適用する
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5. modules_dimとmodules_alphaを指定 (推論用)
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"""
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# call the parent of the parent we are replacing (LoRANetwork) init
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super(LoRANetwork, self).__init__()
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self.lora_dim = lora_dim
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self.alpha = alpha
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self.conv_lora_dim = conv_lora_dim
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self.conv_alpha = conv_alpha
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self.dropout = dropout
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self.rank_dropout = rank_dropout
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self.module_dropout = module_dropout
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self.is_checkpointing = False
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self._multiplier: float = 1.0
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self.is_active: bool = False
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self._is_normalizing: bool = False
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# triggers the state updates
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self.multiplier = multiplier
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self.is_sdxl = is_sdxl
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self.is_v2 = is_v2
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if modules_dim is not None:
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print(f"create LoRA network from weights")
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elif block_dims is not None:
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print(f"create LoRA network from block_dims")
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print(
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f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
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print(f"block_dims: {block_dims}")
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print(f"block_alphas: {block_alphas}")
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if conv_block_dims is not None:
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print(f"conv_block_dims: {conv_block_dims}")
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print(f"conv_block_alphas: {conv_block_alphas}")
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else:
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print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
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print(
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f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
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if self.conv_lora_dim is not None:
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print(
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f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
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# create module instances
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def create_modules(
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is_unet: bool,
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text_encoder_idx: Optional[int], # None, 1, 2
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root_module: torch.nn.Module,
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target_replace_modules: List[torch.nn.Module],
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) -> List[LoRAModule]:
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prefix = (
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self.LORA_PREFIX_UNET
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if is_unet
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else (
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self.LORA_PREFIX_TEXT_ENCODER
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if text_encoder_idx is None
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else (self.LORA_PREFIX_TEXT_ENCODER1 if text_encoder_idx == 1 else self.LORA_PREFIX_TEXT_ENCODER2)
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)
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)
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loras = []
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skipped = []
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for name, module in root_module.named_modules():
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if module.__class__.__name__ in target_replace_modules:
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for child_name, child_module in module.named_modules():
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is_linear = child_module.__class__.__name__ in LINEAR_MODULES
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is_conv2d = child_module.__class__.__name__ in CONV_MODULES
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is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
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if is_linear or is_conv2d:
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lora_name = prefix + "." + name + "." + child_name
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lora_name = lora_name.replace(".", "_")
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dim = None
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alpha = None
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if modules_dim is not None:
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# モジュール指定あり
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if lora_name in modules_dim:
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dim = modules_dim[lora_name]
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alpha = modules_alpha[lora_name]
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elif is_unet and block_dims is not None:
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# U-Netでblock_dims指定あり
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block_idx = get_block_index(lora_name)
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if is_linear or is_conv2d_1x1:
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dim = block_dims[block_idx]
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alpha = block_alphas[block_idx]
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elif conv_block_dims is not None:
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dim = conv_block_dims[block_idx]
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alpha = conv_block_alphas[block_idx]
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else:
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# 通常、すべて対象とする
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if is_linear or is_conv2d_1x1:
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dim = self.lora_dim
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alpha = self.alpha
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elif self.conv_lora_dim is not None:
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dim = self.conv_lora_dim
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alpha = self.conv_alpha
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if dim is None or dim == 0:
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# skipした情報を出力
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if is_linear or is_conv2d_1x1 or (
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self.conv_lora_dim is not None or conv_block_dims is not None):
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skipped.append(lora_name)
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continue
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lora = module_class(
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lora_name,
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child_module,
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self.multiplier,
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dim,
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alpha,
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dropout=dropout,
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rank_dropout=rank_dropout,
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module_dropout=module_dropout,
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)
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loras.append(lora)
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return loras, skipped
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text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
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# create LoRA for text encoder
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# 毎回すべてのモジュールを作るのは無駄なので要検討
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self.text_encoder_loras = []
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skipped_te = []
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if train_text_encoder:
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for i, text_encoder in enumerate(text_encoders):
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if len(text_encoders) > 1:
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index = i + 1
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print(f"create LoRA for Text Encoder {index}:")
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else:
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index = None
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print(f"create LoRA for Text Encoder:")
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text_encoder_loras, skipped = create_modules(False, index, text_encoder,
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LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
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self.text_encoder_loras.extend(text_encoder_loras)
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skipped_te += skipped
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print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
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# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
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target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
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if modules_dim is not None or self.conv_lora_dim is not None or conv_block_dims is not None:
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target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
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if train_unet:
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self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
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else:
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self.unet_loras = []
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skipped_un = []
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print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
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skipped = skipped_te + skipped_un
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if varbose and len(skipped) > 0:
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print(
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f"because block_lr_weight is 0 or dim (rank) is 0, {len(skipped)} LoRA modules are skipped / block_lr_weightまたはdim (rank)が0の為、次の{len(skipped)}個のLoRAモジュールはスキップされます:"
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)
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for name in skipped:
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print(f"\t{name}")
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self.up_lr_weight: List[float] = None
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self.down_lr_weight: List[float] = None
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self.mid_lr_weight: float = None
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self.block_lr = False
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# assertion
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names = set()
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for lora in self.text_encoder_loras + self.unet_loras:
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assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
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names.add(lora.lora_name)
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