mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
dfcf38be91
- Implement new model loader and modify invocations and embeddings - Finish implementation loaders for all models currently supported by InvokeAI. - Move lora, textual_inversion, and model patching support into backend/embeddings. - Restore support for model cache statistics collection (a little ugly, needs work). - Fixed up invocations that load and patch models. - Move seamless and silencewarnings utils into better location
626 lines
21 KiB
Python
626 lines
21 KiB
Python
# Copyright (c) 2024 The InvokeAI Development team
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"""LoRA model support."""
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import bisect
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Union
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import torch
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from safetensors.torch import load_file
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from typing_extensions import Self
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from invokeai.backend.model_manager import BaseModelType
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from .embedding_base import EmbeddingModelRaw
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class LoRALayerBase:
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# rank: Optional[int]
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# alpha: Optional[float]
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# bias: Optional[torch.Tensor]
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# layer_key: str
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# @property
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# def scale(self):
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# return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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if "alpha" in values:
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self.alpha = values["alpha"].item()
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else:
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self.alpha = None
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if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
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self.bias: Optional[torch.Tensor] = torch.sparse_coo_tensor(
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values["bias_indices"],
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values["bias_values"],
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tuple(values["bias_size"]),
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)
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else:
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self.bias = None
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self.rank = None # set in layer implementation
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self.layer_key = layer_key
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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raise NotImplementedError()
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def calc_size(self) -> int:
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model_size = 0
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for val in [self.bias]:
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if val is not None:
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model_size += val.nelement() * val.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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if self.bias is not None:
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self.bias = self.bias.to(device=device, dtype=dtype)
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# TODO: find and debug lora/locon with bias
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class LoRALayer(LoRALayerBase):
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# up: torch.Tensor
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# mid: Optional[torch.Tensor]
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# down: torch.Tensor
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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super().__init__(layer_key, values)
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self.up = values["lora_up.weight"]
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self.down = values["lora_down.weight"]
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if "lora_mid.weight" in values:
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self.mid: Optional[torch.Tensor] = values["lora_mid.weight"]
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else:
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self.mid = None
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self.rank = self.down.shape[0]
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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if self.mid is not None:
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up = self.up.reshape(self.up.shape[0], self.up.shape[1])
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down = self.down.reshape(self.down.shape[0], self.down.shape[1])
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weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
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else:
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weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
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return weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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for val in [self.up, self.mid, self.down]:
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if val is not None:
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model_size += val.nelement() * val.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().to(device=device, dtype=dtype)
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self.up = self.up.to(device=device, dtype=dtype)
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self.down = self.down.to(device=device, dtype=dtype)
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if self.mid is not None:
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self.mid = self.mid.to(device=device, dtype=dtype)
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class LoHALayer(LoRALayerBase):
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# w1_a: torch.Tensor
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# w1_b: torch.Tensor
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# w2_a: torch.Tensor
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# w2_b: torch.Tensor
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# t1: Optional[torch.Tensor] = None
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# t2: Optional[torch.Tensor] = None
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def __init__(self, layer_key: str, values: Dict[str, torch.Tensor]):
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super().__init__(layer_key, values)
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self.w1_a = values["hada_w1_a"]
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self.w1_b = values["hada_w1_b"]
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self.w2_a = values["hada_w2_a"]
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self.w2_b = values["hada_w2_b"]
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if "hada_t1" in values:
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self.t1: Optional[torch.Tensor] = values["hada_t1"]
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else:
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self.t1 = None
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if "hada_t2" in values:
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self.t2: Optional[torch.Tensor] = values["hada_t2"]
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else:
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self.t2 = None
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self.rank = self.w1_b.shape[0]
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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if self.t1 is None:
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weight: torch.Tensor = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
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else:
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rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
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rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
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weight = rebuild1 * rebuild2
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return weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
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if val is not None:
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model_size += val.nelement() * val.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().to(device=device, dtype=dtype)
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self.w1_a = self.w1_a.to(device=device, dtype=dtype)
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self.w1_b = self.w1_b.to(device=device, dtype=dtype)
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if self.t1 is not None:
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self.t1 = self.t1.to(device=device, dtype=dtype)
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self.w2_a = self.w2_a.to(device=device, dtype=dtype)
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self.w2_b = self.w2_b.to(device=device, dtype=dtype)
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if self.t2 is not None:
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self.t2 = self.t2.to(device=device, dtype=dtype)
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class LoKRLayer(LoRALayerBase):
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# w1: Optional[torch.Tensor] = None
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# w1_a: Optional[torch.Tensor] = None
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# w1_b: Optional[torch.Tensor] = None
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# w2: Optional[torch.Tensor] = None
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# w2_a: Optional[torch.Tensor] = None
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# w2_b: Optional[torch.Tensor] = None
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# t2: Optional[torch.Tensor] = None
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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super().__init__(layer_key, values)
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if "lokr_w1" in values:
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self.w1: Optional[torch.Tensor] = values["lokr_w1"]
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self.w1_a = None
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self.w1_b = None
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else:
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self.w1 = None
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self.w1_a = values["lokr_w1_a"]
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self.w1_b = values["lokr_w1_b"]
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if "lokr_w2" in values:
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self.w2: Optional[torch.Tensor] = values["lokr_w2"]
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self.w2_a = None
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self.w2_b = None
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else:
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self.w2 = None
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self.w2_a = values["lokr_w2_a"]
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self.w2_b = values["lokr_w2_b"]
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if "lokr_t2" in values:
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self.t2: Optional[torch.Tensor] = values["lokr_t2"]
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else:
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self.t2 = None
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if "lokr_w1_b" in values:
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self.rank = values["lokr_w1_b"].shape[0]
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elif "lokr_w2_b" in values:
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self.rank = values["lokr_w2_b"].shape[0]
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else:
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self.rank = None # unscaled
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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w1: Optional[torch.Tensor] = self.w1
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if w1 is None:
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assert self.w1_a is not None
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assert self.w1_b is not None
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w1 = self.w1_a @ self.w1_b
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w2 = self.w2
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if w2 is None:
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if self.t2 is None:
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assert self.w2_a is not None
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assert self.w2_b is not None
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w2 = self.w2_a @ self.w2_b
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else:
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w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
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if len(w2.shape) == 4:
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w1 = w1.unsqueeze(2).unsqueeze(2)
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w2 = w2.contiguous()
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assert w1 is not None
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assert w2 is not None
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weight = torch.kron(w1, w2)
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return weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
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if val is not None:
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model_size += val.nelement() * val.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().to(device=device, dtype=dtype)
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if self.w1 is not None:
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self.w1 = self.w1.to(device=device, dtype=dtype)
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else:
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assert self.w1_a is not None
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assert self.w1_b is not None
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self.w1_a = self.w1_a.to(device=device, dtype=dtype)
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self.w1_b = self.w1_b.to(device=device, dtype=dtype)
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if self.w2 is not None:
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self.w2 = self.w2.to(device=device, dtype=dtype)
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else:
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assert self.w2_a is not None
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assert self.w2_b is not None
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self.w2_a = self.w2_a.to(device=device, dtype=dtype)
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self.w2_b = self.w2_b.to(device=device, dtype=dtype)
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if self.t2 is not None:
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self.t2 = self.t2.to(device=device, dtype=dtype)
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class FullLayer(LoRALayerBase):
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# weight: torch.Tensor
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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super().__init__(layer_key, values)
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self.weight = values["diff"]
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if len(values.keys()) > 1:
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_keys = list(values.keys())
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_keys.remove("diff")
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raise NotImplementedError(f"Unexpected keys in lora diff layer: {_keys}")
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self.rank = None # unscaled
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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return self.weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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model_size += self.weight.nelement() * self.weight.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().to(device=device, dtype=dtype)
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self.weight = self.weight.to(device=device, dtype=dtype)
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class IA3Layer(LoRALayerBase):
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# weight: torch.Tensor
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# on_input: torch.Tensor
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def __init__(
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self,
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layer_key: str,
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values: Dict[str, torch.Tensor],
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):
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super().__init__(layer_key, values)
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self.weight = values["weight"]
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self.on_input = values["on_input"]
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self.rank = None # unscaled
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def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
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weight = self.weight
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if not self.on_input:
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weight = weight.reshape(-1, 1)
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assert orig_weight is not None
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return orig_weight * weight
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def calc_size(self) -> int:
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model_size = super().calc_size()
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model_size += self.weight.nelement() * self.weight.element_size()
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model_size += self.on_input.nelement() * self.on_input.element_size()
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return model_size
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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super().to(device=device, dtype=dtype)
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self.weight = self.weight.to(device=device, dtype=dtype)
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self.on_input = self.on_input.to(device=device, dtype=dtype)
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AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer]
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# TODO: rename all methods used in model logic with Info postfix and remove here Raw postfix
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class LoRAModelRaw(EmbeddingModelRaw): # (torch.nn.Module):
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_name: str
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layers: Dict[str, AnyLoRALayer]
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def __init__(
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self,
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name: str,
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layers: Dict[str, AnyLoRALayer],
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):
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self._name = name
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self.layers = layers
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@property
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def name(self) -> str:
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return self._name
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def to(
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self,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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# TODO: try revert if exception?
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for _key, layer in self.layers.items():
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layer.to(device=device, dtype=dtype)
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def calc_size(self) -> int:
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model_size = 0
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for _, layer in self.layers.items():
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model_size += layer.calc_size()
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return model_size
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@classmethod
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def _convert_sdxl_keys_to_diffusers_format(cls, state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
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"""Convert the keys of an SDXL LoRA state_dict to diffusers format.
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The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
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diffusers format, then this function will have no effect.
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This function is adapted from:
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https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
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Args:
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state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
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Raises:
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ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
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Returns:
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Dict[str, Tensor]: The diffusers-format state_dict.
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"""
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converted_count = 0 # The number of Stability AI keys converted to diffusers format.
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not_converted_count = 0 # The number of keys that were not converted.
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# Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
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# For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
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# `input_blocks_4_1_proj_in`.
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stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
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stability_unet_keys.sort()
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new_state_dict = {}
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for full_key, value in state_dict.items():
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if full_key.startswith("lora_unet_"):
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search_key = full_key.replace("lora_unet_", "")
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# Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
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position = bisect.bisect_right(stability_unet_keys, search_key)
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map_key = stability_unet_keys[position - 1]
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# Now, check if the map_key *actually* matches the search_key.
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if search_key.startswith(map_key):
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new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
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new_state_dict[new_key] = value
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converted_count += 1
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else:
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new_state_dict[full_key] = value
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not_converted_count += 1
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elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
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# The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
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new_state_dict[full_key] = value
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continue
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else:
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raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
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if converted_count > 0 and not_converted_count > 0:
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raise ValueError(
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f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
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f" not_converted={not_converted_count}"
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)
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return new_state_dict
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@classmethod
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def from_checkpoint(
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cls,
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file_path: Union[str, Path],
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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base_model: Optional[BaseModelType] = None,
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) -> Self:
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device = device or torch.device("cpu")
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dtype = dtype or torch.float32
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if isinstance(file_path, str):
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file_path = Path(file_path)
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model = cls(
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name=file_path.stem,
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layers={},
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)
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|
|
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if file_path.suffix == ".safetensors":
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sd = load_file(file_path.absolute().as_posix(), device="cpu")
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|
else:
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sd = torch.load(file_path, map_location="cpu")
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|
|
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state_dict = cls._group_state(sd)
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|
|
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if base_model == BaseModelType.StableDiffusionXL:
|
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state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict)
|
|
|
|
for layer_key, values in state_dict.items():
|
|
# lora and locon
|
|
if "lora_down.weight" in values:
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|
layer: AnyLoRALayer = LoRALayer(layer_key, values)
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|
|
|
# loha
|
|
elif "hada_w1_b" in values:
|
|
layer = LoHALayer(layer_key, values)
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|
|
|
# lokr
|
|
elif "lokr_w1_b" in values or "lokr_w1" in values:
|
|
layer = LoKRLayer(layer_key, values)
|
|
|
|
# diff
|
|
elif "diff" in values:
|
|
layer = FullLayer(layer_key, values)
|
|
|
|
# ia3
|
|
elif "weight" in values and "on_input" in values:
|
|
layer = IA3Layer(layer_key, values)
|
|
|
|
else:
|
|
print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
|
|
raise Exception("Unknown lora format!")
|
|
|
|
# lower memory consumption by removing already parsed layer values
|
|
state_dict[layer_key].clear()
|
|
|
|
layer.to(device=device, dtype=dtype)
|
|
model.layers[layer_key] = layer
|
|
|
|
return model
|
|
|
|
@staticmethod
|
|
def _group_state(state_dict: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, torch.Tensor]]:
|
|
state_dict_groupped: Dict[str, Dict[str, torch.Tensor]] = {}
|
|
|
|
for key, value in state_dict.items():
|
|
stem, leaf = key.split(".", 1)
|
|
if stem not in state_dict_groupped:
|
|
state_dict_groupped[stem] = {}
|
|
state_dict_groupped[stem][leaf] = value
|
|
|
|
return state_dict_groupped
|
|
|
|
|
|
# code from
|
|
# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
|
|
def make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
|
|
"""Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
|
|
unet_conversion_map_layer = []
|
|
|
|
for i in range(3): # num_blocks is 3 in sdxl
|
|
# loop over downblocks/upblocks
|
|
for j in range(2):
|
|
# loop over resnets/attentions for downblocks
|
|
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
|
|
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
|
|
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
|
|
|
|
if i < 3:
|
|
# no attention layers in down_blocks.3
|
|
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
|
|
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
|
|
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
|
|
|
|
for j in range(3):
|
|
# loop over resnets/attentions for upblocks
|
|
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
|
|
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
|
|
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
|
|
|
|
# if i > 0: commentout for sdxl
|
|
# no attention layers in up_blocks.0
|
|
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
|
|
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
|
|
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
|
|
|
|
if i < 3:
|
|
# no downsample in down_blocks.3
|
|
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
|
|
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
|
|
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
|
|
|
|
# no upsample in up_blocks.3
|
|
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
|
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}." # change for sdxl
|
|
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
|
|
|
|
hf_mid_atn_prefix = "mid_block.attentions.0."
|
|
sd_mid_atn_prefix = "middle_block.1."
|
|
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
|
|
|
|
for j in range(2):
|
|
hf_mid_res_prefix = f"mid_block.resnets.{j}."
|
|
sd_mid_res_prefix = f"middle_block.{2*j}."
|
|
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
|
|
|
unet_conversion_map_resnet = [
|
|
# (stable-diffusion, HF Diffusers)
|
|
("in_layers.0.", "norm1."),
|
|
("in_layers.2.", "conv1."),
|
|
("out_layers.0.", "norm2."),
|
|
("out_layers.3.", "conv2."),
|
|
("emb_layers.1.", "time_emb_proj."),
|
|
("skip_connection.", "conv_shortcut."),
|
|
]
|
|
|
|
unet_conversion_map = []
|
|
for sd, hf in unet_conversion_map_layer:
|
|
if "resnets" in hf:
|
|
for sd_res, hf_res in unet_conversion_map_resnet:
|
|
unet_conversion_map.append((sd + sd_res, hf + hf_res))
|
|
else:
|
|
unet_conversion_map.append((sd, hf))
|
|
|
|
for j in range(2):
|
|
hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
|
|
sd_time_embed_prefix = f"time_embed.{j*2}."
|
|
unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
|
|
|
|
for j in range(2):
|
|
hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
|
|
sd_label_embed_prefix = f"label_emb.0.{j*2}."
|
|
unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
|
|
|
|
unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
|
|
unet_conversion_map.append(("out.0.", "conv_norm_out."))
|
|
unet_conversion_map.append(("out.2.", "conv_out."))
|
|
|
|
return unet_conversion_map
|
|
|
|
|
|
SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
|
|
sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()
|
|
}
|