mirror of
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1056 lines
43 KiB
Python
1056 lines
43 KiB
Python
# temporary minimum implementation of LoRA
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# Lumina 2 does not have Conv2d, so ignore
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# TODO commonize with the original implementation
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# LoRA network module
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# reference:
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# https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
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# https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py
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import math
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import os
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from typing import Dict, List, Optional, Tuple, Type, Union
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from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
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from transformers import CLIPTextModel
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import torch
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from torch import Tensor, nn
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from library.utils import setup_logging
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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class LoRAModule(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: str,
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org_module: nn.Module,
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multiplier: float =1.0,
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lora_dim: int = 4,
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alpha: Optional[float | int | Tensor] = 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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split_dims: Optional[List[int]] = None,
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):
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"""
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if alpha == 0 or None, alpha is rank (no scaling).
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split_dims is used to mimic the split qkv of lumina as same as Diffusers
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"""
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super().__init__()
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self.lora_name = lora_name
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if org_module.__class__.__name__ == "Conv2d":
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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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assert isinstance(in_dim, int)
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assert isinstance(out_dim, int)
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self.lora_dim = lora_dim
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self.split_dims = split_dims
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if split_dims is None:
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if org_module.__class__.__name__ == "Conv2d":
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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 = nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
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self.lora_up = 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 = nn.Linear(in_dim, self.lora_dim, bias=False)
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self.lora_up = nn.Linear(self.lora_dim, out_dim, bias=False)
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nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
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nn.init.zeros_(self.lora_up.weight)
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else:
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# conv2d not supported
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assert sum(split_dims) == out_dim, "sum of split_dims must be equal to out_dim"
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assert org_module.__class__.__name__ == "Linear", "split_dims is only supported for Linear"
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# print(f"split_dims: {split_dims}")
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self.lora_down = nn.ModuleList(
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[nn.Linear(in_dim, self.lora_dim, bias=False) for _ in range(len(split_dims))]
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)
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self.lora_up = nn.ModuleList([torch.nn.Linear(self.lora_dim, split_dim, bias=False) for split_dim in split_dims])
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for lora_down in self.lora_down:
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nn.init.kaiming_uniform_(lora_down.weight, a=math.sqrt(5))
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for lora_up in self.lora_up:
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nn.init.zeros_(lora_up.weight)
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if isinstance(alpha, Tensor):
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alpha = alpha.detach().cpu().float().item() # 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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self.multiplier = 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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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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def forward(self, x):
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org_forwarded = self.org_forward(x)
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# module dropout
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if self.module_dropout is not None and self.training:
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if torch.rand(1) < self.module_dropout:
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return org_forwarded
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if self.split_dims is None:
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lx = self.lora_down(x)
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# normal dropout
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if self.dropout is not None and self.training:
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lx = torch.nn.functional.dropout(lx, p=self.dropout)
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# rank dropout
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if self.rank_dropout is not None and self.training:
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mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout
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if len(lx.size()) == 3:
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mask = mask.unsqueeze(1) # for Text Encoder
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elif len(lx.size()) == 4:
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mask = mask.unsqueeze(-1).unsqueeze(-1) # for Conv2d
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lx = lx * mask
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# scaling for rank dropout: treat as if the rank is changed
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# maskから計算することも考えられるが、augmentation的な効果を期待してrank_dropoutを用いる
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scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability
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else:
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scale = self.scale
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lx = self.lora_up(lx)
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return org_forwarded + lx * self.multiplier * scale
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else:
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lxs = [lora_down(x) for lora_down in self.lora_down]
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# normal dropout
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if self.dropout is not None and self.training:
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lxs = [torch.nn.functional.dropout(lx, p=self.dropout) for lx in lxs]
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# rank dropout
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if self.rank_dropout is not None and self.training:
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masks = [torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout for lx in lxs]
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for i in range(len(lxs)):
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if len(lxs[i].size()) == 3:
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masks[i] = masks[i].unsqueeze(1)
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elif len(lxs[i].size()) == 4:
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masks[i] = masks[i].unsqueeze(-1).unsqueeze(-1)
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lxs[i] = lxs[i] * masks[i]
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# scaling for rank dropout: treat as if the rank is changed
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scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability
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else:
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scale = self.scale
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lxs = [lora_up(lx) for lora_up, lx in zip(self.lora_up, lxs)]
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return org_forwarded + torch.cat(lxs, dim=-1) * self.multiplier * scale
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class LoRAInfModule(LoRAModule):
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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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**kwargs,
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):
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# no dropout for inference
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super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)
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self.org_module_ref = [org_module] # 後から参照できるように
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self.enabled = True
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self.network: LoRANetwork = None
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def set_network(self, network):
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self.network = network
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# freezeしてマージする
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def merge_to(self, sd, dtype, device):
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# extract weight from org_module
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org_sd = self.org_module.state_dict()
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weight = org_sd["weight"]
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org_dtype = weight.dtype
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org_device = weight.device
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weight = weight.to(torch.float) # calc in float
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if dtype is None:
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dtype = org_dtype
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if device is None:
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device = org_device
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if self.split_dims is None:
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# get up/down weight
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down_weight = sd["lora_down.weight"].to(torch.float).to(device)
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up_weight = sd["lora_up.weight"].to(torch.float).to(device)
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# merge weight
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if len(weight.size()) == 2:
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# linear
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weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
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elif down_weight.size()[2:4] == (1, 1):
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# conv2d 1x1
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weight = (
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weight
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+ self.multiplier
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* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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* self.scale
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)
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else:
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# conv2d 3x3
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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# logger.info(conved.size(), weight.size(), module.stride, module.padding)
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weight = weight + self.multiplier * conved * self.scale
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# set weight to org_module
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org_sd["weight"] = weight.to(dtype)
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self.org_module.load_state_dict(org_sd)
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else:
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# split_dims: merge each split's LoRA into the correct slice of the fused QKV weight
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for i in range(len(self.split_dims)):
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# get up/down weight
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down_weight = sd[f"lora_down.{i}.weight"].to(torch.float).to(device) # (rank, in_dim)
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up_weight = sd[f"lora_up.{i}.weight"].to(torch.float).to(device) # (split_dim, rank)
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# merge into the correct slice of the fused weight
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start = sum(self.split_dims[:i])
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end = sum(self.split_dims[:i + 1])
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weight[start:end] += self.multiplier * (up_weight @ down_weight) * self.scale
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# set weight to org_module
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org_sd["weight"] = weight.to(dtype)
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self.org_module.load_state_dict(org_sd)
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# 復元できるマージのため、このモジュールのweightを返す
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def get_weight(self, multiplier=None):
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if multiplier is None:
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multiplier = self.multiplier
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# Handle split_dims case where lora_down/lora_up are ModuleList
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if self.split_dims is not None:
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# Each sub-module produces a partial weight; concatenate along output dim
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weights = []
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for lora_up, lora_down in zip(self.lora_up, self.lora_down):
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up_w = lora_up.weight.to(torch.float)
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down_w = lora_down.weight.to(torch.float)
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weights.append(up_w @ down_w)
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weight = self.multiplier * torch.cat(weights, dim=0) * self.scale
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return weight
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# get up/down weight from module
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up_weight = self.lora_up.weight.to(torch.float)
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down_weight = self.lora_down.weight.to(torch.float)
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# pre-calculated weight
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if len(down_weight.size()) == 2:
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# linear
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weight = self.multiplier * (up_weight @ down_weight) * self.scale
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elif down_weight.size()[2:4] == (1, 1):
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# conv2d 1x1
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weight = (
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self.multiplier
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* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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* self.scale
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)
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else:
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# conv2d 3x3
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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weight = self.multiplier * conved * self.scale
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return weight
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def set_region(self, region):
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self.region = region
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self.region_mask = None
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def default_forward(self, x):
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# logger.info(f"default_forward {self.lora_name} {x.size()}")
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if self.split_dims is None:
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lx = self.lora_down(x)
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lx = self.lora_up(lx)
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return self.org_forward(x) + lx * self.multiplier * self.scale
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else:
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lxs = [lora_down(x) for lora_down in self.lora_down]
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lxs = [lora_up(lx) for lora_up, lx in zip(self.lora_up, lxs)]
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return self.org_forward(x) + torch.cat(lxs, dim=-1) * self.multiplier * self.scale
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def forward(self, x):
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if not self.enabled:
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return self.org_forward(x)
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return self.default_forward(x)
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def create_network(
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multiplier: float,
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network_dim: Optional[int],
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network_alpha: Optional[float],
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ae: AutoencoderKL,
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text_encoders: List[CLIPTextModel],
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lumina,
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neuron_dropout: Optional[float] = None,
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**kwargs,
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):
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if network_dim is None:
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network_dim = 4 # default
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if network_alpha is None:
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network_alpha = 1.0
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# extract dim/alpha for conv2d, and block dim
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conv_dim = kwargs.get("conv_dim", None)
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conv_alpha = kwargs.get("conv_alpha", None)
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if conv_dim is not None:
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conv_dim = int(conv_dim)
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if conv_alpha is None:
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conv_alpha = 1.0
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else:
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conv_alpha = float(conv_alpha)
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# attn dim, mlp dim for JointTransformerBlock
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attn_dim = kwargs.get("attn_dim", None) # attention dimension
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mlp_dim = kwargs.get("mlp_dim", None) # MLP dimension
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mod_dim = kwargs.get("mod_dim", None) # modulation dimension
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refiner_dim = kwargs.get("refiner_dim", None) # refiner blocks dimension
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if attn_dim is not None:
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attn_dim = int(attn_dim)
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if mlp_dim is not None:
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mlp_dim = int(mlp_dim)
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if mod_dim is not None:
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mod_dim = int(mod_dim)
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if refiner_dim is not None:
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refiner_dim = int(refiner_dim)
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type_dims = [attn_dim, mlp_dim, mod_dim, refiner_dim]
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if all([d is None for d in type_dims]):
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type_dims = None
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# embedder_dims for embedders
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embedder_dims = kwargs.get("embedder_dims", None)
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if embedder_dims is not None:
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embedder_dims = embedder_dims.strip()
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if embedder_dims.startswith("[") and embedder_dims.endswith("]"):
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embedder_dims = embedder_dims[1:-1]
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embedder_dims = [int(d) for d in embedder_dims.split(",")]
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assert len(embedder_dims) == 3, f"invalid embedder_dims: {embedder_dims}, must be 3 dimensions (x_embedder, t_embedder, cap_embedder)"
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# rank/module dropout
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rank_dropout = kwargs.get("rank_dropout", None)
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if rank_dropout is not None:
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rank_dropout = float(rank_dropout)
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module_dropout = kwargs.get("module_dropout", None)
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if module_dropout is not None:
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module_dropout = float(module_dropout)
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# single or double blocks
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train_blocks = kwargs.get("train_blocks", None) # None (default), "all" (same as None), "transformer", "refiners", "noise_refiner", "context_refiner"
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if train_blocks is not None:
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assert train_blocks in ["all", "transformer", "refiners", "noise_refiner", "context_refiner"], f"invalid train_blocks: {train_blocks}"
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# split qkv
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split_qkv = kwargs.get("split_qkv", False)
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if split_qkv is not None:
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split_qkv = True if split_qkv == "True" else False
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# verbose
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verbose = kwargs.get("verbose", False)
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if verbose is not None:
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verbose = True if verbose == "True" else False
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# すごく引数が多いな ( ^ω^)･･･
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network = LoRANetwork(
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text_encoders,
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lumina,
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multiplier=multiplier,
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lora_dim=network_dim,
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alpha=network_alpha,
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dropout=neuron_dropout,
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rank_dropout=rank_dropout,
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module_dropout=module_dropout,
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conv_lora_dim=conv_dim,
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conv_alpha=conv_alpha,
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train_blocks=train_blocks,
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split_qkv=split_qkv,
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type_dims=type_dims,
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embedder_dims=embedder_dims,
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verbose=verbose,
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)
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loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
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loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
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loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
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loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
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loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
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loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
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if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
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network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
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return network
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# Create network from weights for inference, weights are not loaded here (because can be merged)
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def create_network_from_weights(multiplier, file, ae, text_encoders, lumina, weights_sd=None, for_inference=False, **kwargs):
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# if unet is an instance of SdxlUNet2DConditionModel or subclass, set is_sdxl to True
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if weights_sd is None:
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if os.path.splitext(file)[1] == ".safetensors":
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from safetensors.torch import load_file, safe_open
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weights_sd = load_file(file)
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else:
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weights_sd = torch.load(file, map_location="cpu", weights_only=False)
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# get dim/alpha mapping, and train t5xxl
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modules_dim = {}
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modules_alpha = {}
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for key, value in weights_sd.items():
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if "." not in key:
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continue
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lora_name = key.split(".")[0]
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if "alpha" in key:
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modules_alpha[lora_name] = value
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elif "lora_down" in key:
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dim = value.size()[0]
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modules_dim[lora_name] = dim
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# logger.info(lora_name, value.size(), dim)
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# # split qkv
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# double_qkv_rank = None
|
|
# single_qkv_rank = None
|
|
# rank = None
|
|
# for lora_name, dim in modules_dim.items():
|
|
# if "double" in lora_name and "qkv" in lora_name:
|
|
# double_qkv_rank = dim
|
|
# elif "single" in lora_name and "linear1" in lora_name:
|
|
# single_qkv_rank = dim
|
|
# elif rank is None:
|
|
# rank = dim
|
|
# if double_qkv_rank is not None and single_qkv_rank is not None and rank is not None:
|
|
# break
|
|
# split_qkv = (double_qkv_rank is not None and double_qkv_rank != rank) or (
|
|
# single_qkv_rank is not None and single_qkv_rank != rank
|
|
# )
|
|
split_qkv = False # split_qkv is not needed to care, because state_dict is qkv combined
|
|
|
|
module_class = LoRAInfModule if for_inference else LoRAModule
|
|
|
|
network = LoRANetwork(
|
|
text_encoders,
|
|
lumina,
|
|
multiplier=multiplier,
|
|
modules_dim=modules_dim,
|
|
modules_alpha=modules_alpha,
|
|
module_class=module_class,
|
|
split_qkv=split_qkv,
|
|
)
|
|
return network, weights_sd
|
|
|
|
|
|
class LoRANetwork(torch.nn.Module):
|
|
LUMINA_TARGET_REPLACE_MODULE = ["JointTransformerBlock", "FinalLayer"]
|
|
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["Gemma2Attention", "Gemma2FlashAttention2", "Gemma2SdpaAttention", "Gemma2MLP"]
|
|
LORA_PREFIX_LUMINA = "lora_unet"
|
|
LORA_PREFIX_TEXT_ENCODER = "lora_te" # Simplified prefix since we only have one text encoder
|
|
|
|
def __init__(
|
|
self,
|
|
text_encoders, # Now this will be a single Gemma2 model
|
|
unet,
|
|
multiplier: float = 1.0,
|
|
lora_dim: int = 4,
|
|
alpha: float = 1,
|
|
dropout: Optional[float] = None,
|
|
rank_dropout: Optional[float] = None,
|
|
module_dropout: Optional[float] = None,
|
|
conv_lora_dim: Optional[int] = None,
|
|
conv_alpha: Optional[float] = None,
|
|
module_class: Type[LoRAModule] = LoRAModule,
|
|
modules_dim: Optional[Dict[str, int]] = None,
|
|
modules_alpha: Optional[Dict[str, int]] = None,
|
|
train_blocks: Optional[str] = None,
|
|
split_qkv: bool = False,
|
|
type_dims: Optional[List[int]] = None,
|
|
embedder_dims: Optional[List[int]] = None,
|
|
train_block_indices: Optional[List[bool]] = None,
|
|
verbose: Optional[bool] = False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.multiplier = multiplier
|
|
|
|
self.lora_dim = lora_dim
|
|
self.alpha = alpha
|
|
self.conv_lora_dim = conv_lora_dim
|
|
self.conv_alpha = conv_alpha
|
|
self.dropout = dropout
|
|
self.rank_dropout = rank_dropout
|
|
self.module_dropout = module_dropout
|
|
self.train_blocks = train_blocks if train_blocks is not None else "all"
|
|
self.split_qkv = split_qkv
|
|
|
|
self.type_dims = type_dims
|
|
self.embedder_dims = embedder_dims
|
|
|
|
self.train_block_indices = train_block_indices
|
|
|
|
self.loraplus_lr_ratio = None
|
|
self.loraplus_unet_lr_ratio = None
|
|
self.loraplus_text_encoder_lr_ratio = None
|
|
|
|
if modules_dim is not None:
|
|
logger.info(f"create LoRA network from weights")
|
|
self.embedder_dims = [0] * 5 # create embedder_dims
|
|
# verbose = True
|
|
else:
|
|
logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
|
|
logger.info(
|
|
f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}"
|
|
)
|
|
# if self.conv_lora_dim is not None:
|
|
# logger.info(
|
|
# f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}"
|
|
# )
|
|
if self.split_qkv:
|
|
logger.info(f"split qkv for LoRA")
|
|
if self.train_blocks is not None:
|
|
logger.info(f"train {self.train_blocks} blocks only")
|
|
|
|
# create module instances
|
|
def create_modules(
|
|
is_lumina: bool,
|
|
root_module: torch.nn.Module,
|
|
target_replace_modules: Optional[List[str]],
|
|
filter: Optional[str] = None,
|
|
default_dim: Optional[int] = None,
|
|
) -> List[LoRAModule]:
|
|
prefix = self.LORA_PREFIX_LUMINA if is_lumina else self.LORA_PREFIX_TEXT_ENCODER
|
|
|
|
loras = []
|
|
skipped = []
|
|
for name, module in root_module.named_modules():
|
|
if target_replace_modules is None or module.__class__.__name__ in target_replace_modules:
|
|
if target_replace_modules is None: # for handling embedders
|
|
module = root_module
|
|
|
|
for child_name, child_module in module.named_modules():
|
|
is_linear = child_module.__class__.__name__ == "Linear"
|
|
|
|
lora_name = prefix + "." + (name + "." if name else "") + child_name
|
|
lora_name = lora_name.replace(".", "_")
|
|
|
|
# Only Linear is supported
|
|
if not is_linear:
|
|
skipped.append(lora_name)
|
|
continue
|
|
|
|
if filter is not None and filter not in lora_name:
|
|
continue
|
|
|
|
dim = default_dim if default_dim is not None else self.lora_dim
|
|
alpha = self.alpha
|
|
|
|
# Set dim/alpha to modules dim/alpha
|
|
if modules_dim is not None and modules_alpha is not None:
|
|
# network from weights
|
|
if lora_name in modules_dim:
|
|
dim = modules_dim[lora_name]
|
|
alpha = modules_alpha[lora_name]
|
|
else:
|
|
dim = 0 # skip if not found
|
|
|
|
else:
|
|
# Set dims to type_dims
|
|
if is_lumina and type_dims is not None:
|
|
identifier = [
|
|
("attention",), # attention layers
|
|
("mlp",), # MLP layers
|
|
("modulation",), # modulation layers
|
|
("refiner",), # refiner blocks
|
|
]
|
|
for i, d in enumerate(type_dims):
|
|
if d is not None and all([id in lora_name for id in identifier[i]]):
|
|
dim = d # may be 0 for skip
|
|
break
|
|
|
|
# Drop blocks if we are only training some blocks
|
|
if (
|
|
is_lumina
|
|
and dim
|
|
and (
|
|
self.train_block_indices is not None
|
|
)
|
|
and ("layer" in lora_name)
|
|
):
|
|
# "lora_unet_layers_0_..." or "lora_unet_cap_refiner_0_..." or or "lora_unet_noise_refiner_0_..."
|
|
block_index = int(lora_name.split("_")[3]) # bit dirty
|
|
if (
|
|
"layer" in lora_name
|
|
and self.train_block_indices is not None
|
|
and not self.train_block_indices[block_index]
|
|
):
|
|
dim = 0
|
|
|
|
|
|
if dim is None or dim == 0:
|
|
# skipした情報を出力
|
|
skipped.append(lora_name)
|
|
continue
|
|
|
|
lora = module_class(
|
|
lora_name,
|
|
child_module,
|
|
self.multiplier,
|
|
dim,
|
|
alpha,
|
|
dropout=dropout,
|
|
rank_dropout=rank_dropout,
|
|
module_dropout=module_dropout,
|
|
)
|
|
loras.append(lora)
|
|
|
|
if target_replace_modules is None:
|
|
break # all modules are searched
|
|
return loras, skipped
|
|
|
|
# create LoRA for text encoder (Gemma2)
|
|
self.text_encoder_loras: List[Union[LoRAModule, LoRAInfModule]] = []
|
|
skipped_te = []
|
|
|
|
logger.info(f"create LoRA for Gemma2 Text Encoder:")
|
|
text_encoder_loras, skipped = create_modules(False, text_encoders[0], LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
|
|
logger.info(f"create LoRA for Gemma2 Text Encoder: {len(text_encoder_loras)} modules.")
|
|
self.text_encoder_loras.extend(text_encoder_loras)
|
|
skipped_te += skipped
|
|
|
|
# create LoRA for U-Net
|
|
target_replace_modules = LoRANetwork.LUMINA_TARGET_REPLACE_MODULE
|
|
# Filter by block type using name-based filtering in create_modules
|
|
# All block types use JointTransformerBlock, so we filter by module path name
|
|
block_filter = None # None means no filtering (train all)
|
|
if self.train_blocks == "all":
|
|
block_filter = None
|
|
elif self.train_blocks == "transformer":
|
|
block_filter = "layers_" # main transformer blocks: "lora_unet_layers_N_..."
|
|
elif self.train_blocks == "noise_refiner":
|
|
block_filter = "noise_refiner"
|
|
elif self.train_blocks == "context_refiner":
|
|
block_filter = "context_refiner"
|
|
elif self.train_blocks == "refiners":
|
|
block_filter = None # handled below with two calls
|
|
|
|
self.unet_loras: List[Union[LoRAModule, LoRAInfModule]]
|
|
if self.train_blocks == "refiners":
|
|
# Refiners = noise_refiner + context_refiner, need two calls
|
|
noise_loras, skipped_noise = create_modules(True, unet, target_replace_modules, filter="noise_refiner")
|
|
context_loras, skipped_context = create_modules(True, unet, target_replace_modules, filter="context_refiner")
|
|
self.unet_loras = noise_loras + context_loras
|
|
skipped_un = skipped_noise + skipped_context
|
|
else:
|
|
self.unet_loras, skipped_un = create_modules(True, unet, target_replace_modules, filter=block_filter)
|
|
|
|
# Handle embedders
|
|
if self.embedder_dims:
|
|
for filter, embedder_dim in zip(["x_embedder", "t_embedder", "cap_embedder"], self.embedder_dims):
|
|
loras, _ = create_modules(True, unet, None, filter=filter, default_dim=embedder_dim)
|
|
self.unet_loras.extend(loras)
|
|
|
|
logger.info(f"create LoRA for Lumina blocks: {len(self.unet_loras)} modules.")
|
|
if verbose:
|
|
for lora in self.unet_loras:
|
|
logger.info(f"\t{lora.lora_name:50} {lora.lora_dim}, {lora.alpha}")
|
|
|
|
skipped = skipped_te + skipped_un
|
|
if verbose and len(skipped) > 0:
|
|
logger.warning(
|
|
f"because dim (rank) is 0, {len(skipped)} LoRA modules are skipped / 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:"
|
|
)
|
|
for name in skipped:
|
|
logger.info(f"\t{name}")
|
|
|
|
# assertion
|
|
names = set()
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
|
|
names.add(lora.lora_name)
|
|
|
|
def set_multiplier(self, multiplier):
|
|
self.multiplier = multiplier
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.multiplier = self.multiplier
|
|
|
|
def set_enabled(self, is_enabled):
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.enabled = is_enabled
|
|
|
|
def load_weights(self, file):
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import load_file
|
|
|
|
weights_sd = load_file(file)
|
|
else:
|
|
weights_sd = torch.load(file, map_location="cpu", weights_only=False)
|
|
|
|
info = self.load_state_dict(weights_sd, False)
|
|
return info
|
|
|
|
def load_state_dict(self, state_dict, strict=True):
|
|
# override to convert original weight to split qkv
|
|
if not self.split_qkv:
|
|
return super().load_state_dict(state_dict, strict)
|
|
|
|
# # split qkv
|
|
# for key in list(state_dict.keys()):
|
|
# if "double" in key and "qkv" in key:
|
|
# split_dims = [3072] * 3
|
|
# elif "single" in key and "linear1" in key:
|
|
# split_dims = [3072] * 3 + [12288]
|
|
# else:
|
|
# continue
|
|
|
|
# weight = state_dict[key]
|
|
# lora_name = key.split(".")[0]
|
|
|
|
# if key not in state_dict:
|
|
# continue # already merged
|
|
|
|
# # (rank, in_dim) * 3
|
|
# down_weights = [state_dict.pop(f"{lora_name}.lora_down.{i}.weight") for i in range(len(split_dims))]
|
|
# # (split dim, rank) * 3
|
|
# up_weights = [state_dict.pop(f"{lora_name}.lora_up.{i}.weight") for i in range(len(split_dims))]
|
|
|
|
# alpha = state_dict.pop(f"{lora_name}.alpha")
|
|
|
|
# # merge down weight
|
|
# down_weight = torch.cat(down_weights, dim=0) # (rank, split_dim) * 3 -> (rank*3, sum of split_dim)
|
|
|
|
# # merge up weight (sum of split_dim, rank*3)
|
|
# rank = up_weights[0].size(1)
|
|
# up_weight = torch.zeros((sum(split_dims), down_weight.size(0)), device=down_weight.device, dtype=down_weight.dtype)
|
|
# i = 0
|
|
# for j in range(len(split_dims)):
|
|
# up_weight[i : i + split_dims[j], j * rank : (j + 1) * rank] = up_weights[j]
|
|
# i += split_dims[j]
|
|
|
|
# state_dict[f"{lora_name}.lora_down.weight"] = down_weight
|
|
# state_dict[f"{lora_name}.lora_up.weight"] = up_weight
|
|
# state_dict[f"{lora_name}.alpha"] = alpha
|
|
|
|
# # print(
|
|
# # f"merged {lora_name}: {lora_name}, {[w.shape for w in down_weights]}, {[w.shape for w in up_weights]} to {down_weight.shape}, {up_weight.shape}"
|
|
# # )
|
|
# print(f"new key: {lora_name}.lora_down.weight, {lora_name}.lora_up.weight, {lora_name}.alpha")
|
|
|
|
return super().load_state_dict(state_dict, strict)
|
|
|
|
def state_dict(self, destination=None, prefix="", keep_vars=False):
|
|
if not self.split_qkv:
|
|
return super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
|
|
|
|
# merge qkv
|
|
state_dict = super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
|
|
new_state_dict = {}
|
|
for key in list(state_dict.keys()):
|
|
if "qkv" in key:
|
|
# Lumina 2B: dim=2304, n_heads=24, n_kv_heads=8, head_dim=96
|
|
# Q=24*96=2304, K=8*96=768, V=8*96=768
|
|
split_dims = [2304, 768, 768]
|
|
else:
|
|
new_state_dict[key] = state_dict[key]
|
|
continue
|
|
|
|
if key not in state_dict:
|
|
continue # already merged
|
|
|
|
lora_name = key.split(".")[0]
|
|
|
|
# (rank, in_dim) * 3
|
|
down_weights = [state_dict.pop(f"{lora_name}.lora_down.{i}.weight") for i in range(len(split_dims))]
|
|
# (split dim, rank) * 3
|
|
up_weights = [state_dict.pop(f"{lora_name}.lora_up.{i}.weight") for i in range(len(split_dims))]
|
|
|
|
alpha = state_dict.pop(f"{lora_name}.alpha")
|
|
|
|
# merge down weight
|
|
down_weight = torch.cat(down_weights, dim=0) # (rank, split_dim) * 3 -> (rank*3, sum of split_dim)
|
|
|
|
# merge up weight (sum of split_dim, rank*3)
|
|
rank = up_weights[0].size(1)
|
|
up_weight = torch.zeros((sum(split_dims), down_weight.size(0)), device=down_weight.device, dtype=down_weight.dtype)
|
|
i = 0
|
|
for j in range(len(split_dims)):
|
|
up_weight[i : i + split_dims[j], j * rank : (j + 1) * rank] = up_weights[j]
|
|
i += split_dims[j]
|
|
|
|
new_state_dict[f"{lora_name}.lora_down.weight"] = down_weight
|
|
new_state_dict[f"{lora_name}.lora_up.weight"] = up_weight
|
|
new_state_dict[f"{lora_name}.alpha"] = alpha
|
|
|
|
# print(
|
|
# f"merged {lora_name}: {lora_name}, {[w.shape for w in down_weights]}, {[w.shape for w in up_weights]} to {down_weight.shape}, {up_weight.shape}"
|
|
# )
|
|
print(f"new key: {lora_name}.lora_down.weight, {lora_name}.lora_up.weight, {lora_name}.alpha")
|
|
|
|
return new_state_dict
|
|
|
|
def apply_to(self, text_encoders, flux, apply_text_encoder=True, apply_unet=True):
|
|
if apply_text_encoder:
|
|
logger.info(f"enable LoRA for text encoder: {len(self.text_encoder_loras)} modules")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info(f"enable LoRA for U-Net: {len(self.unet_loras)} modules")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.apply_to()
|
|
self.add_module(lora.lora_name, lora)
|
|
|
|
# マージできるかどうかを返す
|
|
def is_mergeable(self):
|
|
return True
|
|
|
|
# TODO refactor to common function with apply_to
|
|
def merge_to(self, text_encoders, flux, weights_sd, dtype=None, device=None):
|
|
apply_text_encoder = apply_unet = False
|
|
for key in weights_sd.keys():
|
|
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
|
|
apply_text_encoder = True
|
|
elif key.startswith(LoRANetwork.LORA_PREFIX_LUMINA):
|
|
apply_unet = True
|
|
|
|
if apply_text_encoder:
|
|
logger.info("enable LoRA for text encoder")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info("enable LoRA for U-Net")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
sd_for_lora = {}
|
|
for key in weights_sd.keys():
|
|
if key.startswith(lora.lora_name):
|
|
sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key]
|
|
lora.merge_to(sd_for_lora, dtype, device)
|
|
|
|
logger.info(f"weights are merged")
|
|
|
|
def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
|
|
self.loraplus_lr_ratio = loraplus_lr_ratio
|
|
self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
|
|
self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
|
|
|
|
logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}")
|
|
logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}")
|
|
|
|
def prepare_optimizer_params_with_multiple_te_lrs(self, text_encoder_lr, unet_lr, default_lr):
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# make sure text_encoder_lr as list of two elements
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# if float, use the same value for both text encoders
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if text_encoder_lr is None or (isinstance(text_encoder_lr, list) and len(text_encoder_lr) == 0):
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text_encoder_lr = [default_lr, default_lr]
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elif isinstance(text_encoder_lr, float) or isinstance(text_encoder_lr, int):
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text_encoder_lr = [float(text_encoder_lr), float(text_encoder_lr)]
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elif len(text_encoder_lr) == 1:
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text_encoder_lr = [text_encoder_lr[0], text_encoder_lr[0]]
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|
|
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self.requires_grad_(True)
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|
|
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all_params = []
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|
lr_descriptions = []
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|
|
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def assemble_params(loras, lr, loraplus_ratio):
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param_groups = {"lora": {}, "plus": {}}
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for lora in loras:
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for name, param in lora.named_parameters():
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if loraplus_ratio is not None and "lora_up" in name:
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param_groups["plus"][f"{lora.lora_name}.{name}"] = param
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else:
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param_groups["lora"][f"{lora.lora_name}.{name}"] = param
|
|
|
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params = []
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|
descriptions = []
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for key in param_groups.keys():
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param_data = {"params": param_groups[key].values()}
|
|
|
|
if len(param_data["params"]) == 0:
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continue
|
|
|
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if lr is not None:
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if key == "plus":
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param_data["lr"] = lr * loraplus_ratio
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else:
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param_data["lr"] = lr
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|
|
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if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
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logger.info("NO LR skipping!")
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|
continue
|
|
|
|
params.append(param_data)
|
|
descriptions.append("plus" if key == "plus" else "")
|
|
|
|
return params, descriptions
|
|
|
|
if self.text_encoder_loras:
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|
loraplus_lr_ratio = self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio
|
|
|
|
# split text encoder loras for te1 and te3
|
|
te_loras = [lora for lora in self.text_encoder_loras]
|
|
if len(te_loras) > 0:
|
|
logger.info(f"Text Encoder: {len(te_loras)} modules, LR {text_encoder_lr[0]}")
|
|
params, descriptions = assemble_params(te_loras, text_encoder_lr[0], loraplus_lr_ratio)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["textencoder " + (" " + d if d else "") for d in descriptions])
|
|
|
|
if self.unet_loras:
|
|
params, descriptions = assemble_params(
|
|
self.unet_loras,
|
|
unet_lr if unet_lr is not None else default_lr,
|
|
self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
|
|
)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions])
|
|
|
|
return all_params, lr_descriptions
|
|
|
|
def enable_gradient_checkpointing(self):
|
|
# not supported
|
|
pass
|
|
|
|
def prepare_grad_etc(self, text_encoder, unet):
|
|
self.requires_grad_(True)
|
|
|
|
def on_epoch_start(self, text_encoder, unet):
|
|
self.train()
|
|
|
|
def get_trainable_params(self):
|
|
return self.parameters()
|
|
|
|
def save_weights(self, file, dtype, metadata):
|
|
if metadata is not None and len(metadata) == 0:
|
|
metadata = None
|
|
|
|
state_dict = self.state_dict()
|
|
|
|
if dtype is not None:
|
|
for key in list(state_dict.keys()):
|
|
v = state_dict[key]
|
|
v = v.detach().clone().to("cpu").to(dtype)
|
|
state_dict[key] = v
|
|
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import save_file
|
|
from library import train_util
|
|
|
|
# Precalculate model hashes to save time on indexing
|
|
if metadata is None:
|
|
metadata = {}
|
|
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
|
|
metadata["sshs_model_hash"] = model_hash
|
|
metadata["sshs_legacy_hash"] = legacy_hash
|
|
|
|
save_file(state_dict, file, metadata)
|
|
else:
|
|
torch.save(state_dict, file)
|
|
|
|
def backup_weights(self):
|
|
# 重みのバックアップを行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not hasattr(org_module, "_lora_org_weight"):
|
|
sd = org_module.state_dict()
|
|
org_module._lora_org_weight = sd["weight"].detach().clone()
|
|
org_module._lora_restored = True
|
|
|
|
def restore_weights(self):
|
|
# 重みのリストアを行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not org_module._lora_restored:
|
|
sd = org_module.state_dict()
|
|
sd["weight"] = org_module._lora_org_weight
|
|
org_module.load_state_dict(sd)
|
|
org_module._lora_restored = True
|
|
|
|
def pre_calculation(self):
|
|
# 事前計算を行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
sd = org_module.state_dict()
|
|
|
|
org_weight = sd["weight"]
|
|
lora_weight = lora.get_weight().to(org_weight.device, dtype=org_weight.dtype)
|
|
sd["weight"] = org_weight + lora_weight
|
|
assert sd["weight"].shape == org_weight.shape
|
|
org_module.load_state_dict(sd)
|
|
|
|
org_module._lora_restored = False
|
|
lora.enabled = False
|
|
|
|
def apply_max_norm_regularization(self, max_norm_value, device):
|
|
downkeys = []
|
|
upkeys = []
|
|
alphakeys = []
|
|
norms = []
|
|
keys_scaled = 0
|
|
|
|
state_dict = self.state_dict()
|
|
for key in state_dict.keys():
|
|
if "lora_down" in key and "weight" in key:
|
|
downkeys.append(key)
|
|
upkeys.append(key.replace("lora_down", "lora_up"))
|
|
alphakeys.append(key.replace("lora_down.weight", "alpha"))
|
|
|
|
for i in range(len(downkeys)):
|
|
down = state_dict[downkeys[i]].to(device)
|
|
up = state_dict[upkeys[i]].to(device)
|
|
alpha = state_dict[alphakeys[i]].to(device)
|
|
dim = down.shape[0]
|
|
scale = alpha / dim
|
|
|
|
if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
|
|
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
|
|
elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
|
|
updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
|
|
else:
|
|
updown = up @ down
|
|
|
|
updown *= scale
|
|
|
|
norm = updown.norm().clamp(min=max_norm_value / 2)
|
|
desired = torch.clamp(norm, max=max_norm_value)
|
|
ratio = desired.cpu() / norm.cpu()
|
|
sqrt_ratio = ratio**0.5
|
|
if ratio != 1:
|
|
keys_scaled += 1
|
|
state_dict[upkeys[i]] *= sqrt_ratio
|
|
state_dict[downkeys[i]] *= sqrt_ratio
|
|
scalednorm = updown.norm() * ratio
|
|
norms.append(scalednorm.item())
|
|
|
|
return keys_scaled, sum(norms) / len(norms), max(norms) |