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https://github.com/invoke-ai/InvokeAI.git
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405 lines
14 KiB
Python
405 lines
14 KiB
Python
import re
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from pathlib import Path
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from ldm.invoke.globals import global_models_dir
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from ldm.invoke.devices import choose_torch_device
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from safetensors.torch import load_file
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import torch
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from torch.utils.hooks import RemovableHandle
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from diffusers.models import UNet2DConditionModel
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from transformers import CLIPTextModel
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class LoRALayer:
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lora_name: str
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name: str
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scale: float
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up: torch.nn.Module
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down: torch.nn.Module
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def __init__(self, lora_name: str, name: str, rank=4, alpha=1.0):
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self.lora_name = lora_name
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self.name = name
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self.scale = alpha / rank
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class LoRA:
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name: str
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layers: dict[str, LoRALayer]
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device: torch.device
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dtype: torch.dtype
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multiplier: float
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def __init__(self, name: str, device, dtype, multiplier=1.0):
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self.name = name
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self.layers = {}
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self.multiplier = multiplier
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self.device = device
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self.dtype = dtype
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self.rank = None
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self.alpha = None
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def load_from_dict(self,
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state_dict,
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text_modules: dict[str, torch.nn.Module],
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unet_modules: dict[str, torch.nn.Module]):
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for key, value in state_dict.items():
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stem, leaf = key.split(".", 1)
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if leaf.endswith("alpha"):
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if self.alpha is None:
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self.alpha = value.item()
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continue
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if stem.startswith(LORA_PREFIX_TEXT_ENCODER):
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wrapped = text_modules.get(stem, None)
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if wrapped is None:
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print(f">> Missing layer: {stem}")
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continue
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if self.rank is None and leaf == 'lora_down.weight' and len(value.size()) == 2:
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self.rank = value.shape[0]
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self.load_lora_layer(stem, leaf, value, wrapped)
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continue
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elif stem.startswith(LORA_PREFIX_UNET):
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wrapped = unet_modules.get(stem, None)
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if wrapped is None:
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print(f">> Missing layer: {stem}")
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continue
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if self.rank is None and leaf == 'lora_down.weight' and len(value.size()) == 2:
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self.rank = value.shape[0]
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self.load_lora_layer(stem, leaf, value, wrapped)
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continue
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else:
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continue
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def load_lora_layer(self, stem: str, leaf: str, value, wrapped: torch.nn.Module):
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layer = self.layers.get(stem, None)
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if layer is None:
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layer = LoRALayer(self.name, stem, self.rank, self.alpha)
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self.layers[stem] = layer
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if type(wrapped) == torch.nn.Linear:
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module = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
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elif type(wrapped) == torch.nn.Conv2d:
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module = torch.nn.Conv2d(value.shape[1], value.shape[0], (1, 1), bias=False)
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else:
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print(f">> Encountered unknown lora layer module in {self.name}: {type(value).__name__}")
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return
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with torch.no_grad():
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module.weight.copy_(value)
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module.to(device=self.device, dtype=self.dtype)
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if leaf == "lora_up.weight":
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layer.up = module
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elif leaf == "lora_down.weight":
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layer.down = module
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else:
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print(f">> Encountered unknown layer in lora {self.name}: {leaf}")
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return
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UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
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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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re_digits = re.compile(r"\d+")
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re_unet_transformer_attn_blocks = re.compile(
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r"lora_unet_(.+)_blocks_(\d+)_attentions_(\d+)_transformer_blocks_(\d+)_attn(\d+)_(.+).(weight|alpha)"
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)
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re_unet_mid_blocks = re.compile(
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r"lora_unet_mid_block_attentions_(\d+)_(.+).(weight|alpha)"
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)
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re_unet_transformer_blocks = re.compile(
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r"lora_unet_(.+)_blocks_(\d+)_attentions_(\d+)_transformer_blocks_(\d+)_(.+).(weight|alpha)"
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)
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re_unet_mid_transformer_blocks = re.compile(
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r"lora_unet_mid_block_attentions_(\d+)_transformer_blocks_(\d+)_(.+).(weight|alpha)"
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)
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re_unet_norm_blocks = re.compile(
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r"lora_unet_(.+)_blocks_(\d+)_attentions_(\d+)_(.+).(weight|alpha)"
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)
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re_out = re.compile(r"to_out_(\d+)")
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re_processor_weight = re.compile(r"(.+)_(\d+)_(.+)")
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re_processor_alpha = re.compile(r"(.+)_(\d+)")
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def convert_key_to_diffusers(key):
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def match(match_list, regex, subject):
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r = re.match(regex, subject)
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if not r:
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return False
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match_list.clear()
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match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
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return True
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m = []
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def get_front_block(first, second, third, fourth=None):
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if first == "mid":
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b_type = f"mid_block"
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else:
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b_type = f"{first}_blocks.{second}"
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if fourth is None:
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return f"{b_type}.attentions.{third}"
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return f"{b_type}.attentions.{third}.transformer_blocks.{fourth}"
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def get_back_block(first, second, third):
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second = second.replace(".lora_", "_lora.")
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if third == "weight":
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bm = []
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if match(bm, re_processor_weight, second):
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s_bm = bm[2].split('.')
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s_front = f"{bm[0]}_{s_bm[0]}"
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s_back = f"{s_bm[1]}"
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if int(bm[1]) == 0:
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second = f"{s_front}.{s_back}"
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else:
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second = f"{s_front}.{bm[1]}.{s_back}"
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elif third == "alpha":
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bma = []
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if match(bma, re_processor_alpha, second):
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if int(bma[1]) == 0:
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second = f"{bma[0]}"
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else:
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second = f"{bma[0]}.{bma[1]}"
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if first is None:
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return f"processor.{second}.{third}"
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return f"attn{first}.processor.{second}.{third}"
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if match(m, re_unet_transformer_attn_blocks, key):
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return f"{get_front_block(m[0], m[1], m[2], m[3])}.{get_back_block(m[4], m[5], m[6])}"
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if match(m, re_unet_transformer_blocks, key):
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return f"{get_front_block(m[0], m[1], m[2], m[3])}.{get_back_block(None, m[4], m[5])}"
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if match(m, re_unet_mid_transformer_blocks, key):
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return f"{get_front_block('mid', None, m[0], m[1])}.{get_back_block(None, m[2], m[3])}"
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if match(m, re_unet_norm_blocks, key):
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return f"{get_front_block(m[0], m[1], m[2])}.{get_back_block(None, m[3], m[4])}"
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if match(m, re_unet_mid_blocks, key):
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return f"{get_front_block('mid', None, m[0])}.{get_back_block(None, m[1], m[2])}"
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return key
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def load_lora_attn(
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name: str,
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path_file: Path,
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unet: UNet2DConditionModel,
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text_encoder: CLIPTextModel,
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multiplier=1.0
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):
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print(f">> Loading lora {name} from {path_file}")
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if path_file.suffix == '.safetensors':
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checkpoint = load_file(path_file.absolute().as_posix(), device='cpu')
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else:
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checkpoint = torch.load(path_file, map_location='cpu')
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for key in list(checkpoint.keys()):
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if key.startswith(LORA_PREFIX_UNET):
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# convert unet keys
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checkpoint[convert_key_to_diffusers(key)] = checkpoint.pop(key)
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elif key.startswith(LORA_PREFIX_UNET):
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# convert text encoder keys (not yet supported)
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# state_dict[convert_key_to_diffusers(key)] = state_dict.pop(key)
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checkpoint.pop(key)
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else:
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# remove invalid key
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checkpoint.pop(key)
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unet.load_attn_procs(checkpoint)
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# text_encoder.load_attn_procs(checkpoint)
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def lora_forward_hook(name):
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def lora_forward(module, input_h, output):
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if len(loaded_loras) == 0:
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return output
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for lora in applied_loras.values():
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layer = lora.layers.get(name, None)
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if layer is None:
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continue
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output = output + layer.up(layer.down(*input_h)) * lora.multiplier * layer.scale
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return output
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return lora_forward
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def load_lora(
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name: str,
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path_file: Path,
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device: torch.device,
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dtype: torch.dtype,
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text_modules: dict[str, torch.nn.Module],
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unet_modules: dict[str, torch.nn.Module],
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multiplier=1.0
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):
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print(f">> Loading lora {name} from {path_file}")
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if path_file.suffix == '.safetensors':
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checkpoint = load_file(path_file.absolute().as_posix(), device='cpu')
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else:
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checkpoint = torch.load(path_file, map_location='cpu')
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lora = LoRA(name, device, dtype, multiplier)
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lora.load_from_dict(checkpoint, text_modules, unet_modules)
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return lora
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class LoraManager:
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loras_to_load: dict[str, float]
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hooks: list[RemovableHandle]
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def __init__(self, pipe):
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self.lora_path = Path(global_models_dir(), 'lora')
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self.unet = pipe.unet
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self.text_encoder = pipe.text_encoder
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self.device = torch.device(choose_torch_device())
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self.dtype = pipe.unet.dtype
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self.loras_to_load = {}
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self.hooks = []
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def find_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> dict[str, torch.nn.Module]:
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mapping = {}
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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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layer_type = child_module.__class__.__name__
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if layer_type == "Linear" or (layer_type == "Conv2d" and child_module.kernel_size == (1, 1)):
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lora_name = prefix + '.' + name + '.' + child_name
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lora_name = lora_name.replace('.', '_')
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mapping[lora_name] = child_module
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self.apply_module_forward(child_module, lora_name)
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return mapping
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self.text_modules = find_modules(
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LORA_PREFIX_TEXT_ENCODER, self.text_encoder, TEXT_ENCODER_TARGET_REPLACE_MODULE)
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self.unet_modules = find_modules(
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LORA_PREFIX_UNET, self.unet, UNET_TARGET_REPLACE_MODULE)
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def _load_lora(self, name, path_file, multiplier: float = 1.0):
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# can be used instead to load through diffusers, once enough support is added
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# lora = load_lora_attn(name, path_file, self.unet, self.text_encoder, multiplier)
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lora = load_lora(name, path_file, self.device, self.dtype, self.text_modules, self.unet_modules, multiplier)
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loaded_loras[name] = lora
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return lora
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def apply_module_forward(self, module, lora_name):
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handle = module.register_forward_hook(lora_forward_hook(lora_name))
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self.hooks.append(handle)
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def apply_lora_model(self, name, mult: float = 1.0):
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path = Path(self.lora_path, name)
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file = Path(path, "pytorch_lora_weights.bin")
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if path.is_dir() and file.is_file():
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print(f"Diffusers lora is currently disabled: {path}")
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# print(f"loading lora: {path}")
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# self.unet.load_attn_procs(path.absolute().as_posix())
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else:
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path_file = Path(self.lora_path, f'{name}.ckpt')
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if Path(self.lora_path, f'{name}.safetensors').exists():
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path_file = Path(self.lora_path, f'{name}.safetensors')
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if not path_file.exists():
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print(f">> Unable to find lora: {name}")
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return
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lora = loaded_loras.get(name, None)
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if lora is None:
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lora = self._load_lora(name, path_file, mult)
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lora.multiplier = mult
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applied_loras[name] = lora
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def load_lora(self):
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for name, multiplier in self.loras_to_load.items():
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self.apply_lora_model(name, multiplier)
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# unload any lora's not defined by loras_to_load
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for name in list(applied_loras.keys()):
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if name not in self.loras_to_load:
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self.unload_applied_lora(name)
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@staticmethod
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def unload_applied_lora(lora_name: str):
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if lora_name in applied_loras:
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del applied_loras[lora_name]
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@staticmethod
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def unload_lora(lora_name: str):
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if lora_name in loaded_loras:
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del loaded_loras[lora_name]
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# Define a lora to be loaded
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# Can be used to define a lora to be loaded outside of prompts
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def set_lora(self, name, multiplier: float = 1.0):
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self.loras_to_load[name] = multiplier
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# update the multiplier if the lora was already loaded
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if name in loaded_loras:
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loaded_loras[name].multiplier = multiplier
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# Load the lora from a prompt, syntax is <lora:lora_name:multiplier>
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# Multiplier should be a value between 0.0 and 1.0
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def configure_prompt(self, prompt: str) -> str:
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self.clear_loras()
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lora_match = re.compile(r"<lora:([^>]+)>")
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for match in re.findall(lora_match, prompt):
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match = match.split(':')
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name = match[0]
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mult = 1.0
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if len(match) == 2:
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mult = float(match[1])
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self.set_lora(name, mult)
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# remove lora and return prompt to avoid the lora prompt causing issues in inference
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return re.sub(lora_match, "", prompt)
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def clear_loras(self):
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clear_applied_loras()
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self.loras_to_load = {}
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def clear_hooks(self):
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for hook in self.hooks:
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hook.remove()
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self.hooks.clear()
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def __del__(self):
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self.clear_hooks()
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clear_applied_loras()
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clear_loaded_loras()
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del self.text_modules
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del self.unet_modules
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del self.loras_to_load
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applied_loras = {}
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loaded_loras = {}
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def clear_applied_loras():
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applied_loras.clear()
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def clear_loaded_loras():
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loaded_loras.clear()
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