mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Split lora.py monolith into separate files.
This commit is contained in:
Ryan Dick
parent
5d41157404
commit
4e3f42e388
@ -9,7 +9,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
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from invokeai.app.invocations.primitives import ConditioningOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.ti_utils import generate_ti_list
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from invokeai.backend.lora import LoRAModelRaw
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from invokeai.backend.lora.lora_model import LoRAModelRaw
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from invokeai.backend.model_patcher import ModelPatcher
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
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BasicConditioningInfo,
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@ -48,7 +48,7 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.controlnet_utils import prepare_control_image
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from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
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from invokeai.backend.lora import LoRAModelRaw
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from invokeai.backend.lora.lora_model import LoRAModelRaw
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from invokeai.backend.model_manager import BaseModelType, LoadedModel
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from invokeai.backend.model_patcher import ModelPatcher
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from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
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@ -1,620 +0,0 @@
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# 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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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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class LoRAModelRaw(torch.nn.Module):
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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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super().__init__()
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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_", "")
|
||||
# Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
|
||||
position = bisect.bisect_right(stability_unet_keys, search_key)
|
||||
map_key = stability_unet_keys[position - 1]
|
||||
# Now, check if the map_key *actually* matches the search_key.
|
||||
if search_key.startswith(map_key):
|
||||
new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
|
||||
new_state_dict[new_key] = value
|
||||
converted_count += 1
|
||||
else:
|
||||
new_state_dict[full_key] = value
|
||||
not_converted_count += 1
|
||||
elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
|
||||
# The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
|
||||
new_state_dict[full_key] = value
|
||||
continue
|
||||
else:
|
||||
raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
|
||||
|
||||
if converted_count > 0 and not_converted_count > 0:
|
||||
raise ValueError(
|
||||
f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
|
||||
f" not_converted={not_converted_count}"
|
||||
)
|
||||
|
||||
return new_state_dict
|
||||
|
||||
@classmethod
|
||||
def from_checkpoint(
|
||||
cls,
|
||||
file_path: Union[str, Path],
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
base_model: Optional[BaseModelType] = None,
|
||||
) -> Self:
|
||||
device = device or torch.device("cpu")
|
||||
dtype = dtype or torch.float32
|
||||
|
||||
if isinstance(file_path, str):
|
||||
file_path = Path(file_path)
|
||||
|
||||
model = cls(
|
||||
name=file_path.stem,
|
||||
layers={},
|
||||
)
|
||||
|
||||
if file_path.suffix == ".safetensors":
|
||||
sd = load_file(file_path.absolute().as_posix(), device="cpu")
|
||||
else:
|
||||
sd = torch.load(file_path, map_location="cpu")
|
||||
|
||||
state_dict = cls._group_state(sd)
|
||||
|
||||
if base_model == BaseModelType.StableDiffusionXL:
|
||||
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:
|
||||
layer: AnyLoRALayer = LoRALayer(layer_key, values)
|
||||
|
||||
# loha
|
||||
elif "hada_w1_b" in values:
|
||||
layer = LoHALayer(layer_key, values)
|
||||
|
||||
# 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()
|
||||
}
|
||||
0
invokeai/backend/lora/__init__.py
Normal file
0
invokeai/backend/lora/__init__.py
Normal file
42
invokeai/backend/lora/full_layer.py
Normal file
42
invokeai/backend/lora/full_layer.py
Normal file
@ -0,0 +1,42 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora.lora_layer_base import LoRALayerBase
|
||||
|
||||
|
||||
class FullLayer(LoRALayerBase):
|
||||
# weight: torch.Tensor
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: Dict[str, torch.Tensor],
|
||||
):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
self.weight = values["diff"]
|
||||
|
||||
if len(values.keys()) > 1:
|
||||
_keys = list(values.keys())
|
||||
_keys.remove("diff")
|
||||
raise NotImplementedError(f"Unexpected keys in lora diff layer: {_keys}")
|
||||
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
return self.weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
model_size += self.weight.nelement() * self.weight.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
self.weight = self.weight.to(device=device, dtype=dtype)
|
||||
45
invokeai/backend/lora/ia3_layer.py
Normal file
45
invokeai/backend/lora/ia3_layer.py
Normal file
@ -0,0 +1,45 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora.lora_layer_base import LoRALayerBase
|
||||
|
||||
|
||||
class IA3Layer(LoRALayerBase):
|
||||
# weight: torch.Tensor
|
||||
# on_input: torch.Tensor
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: Dict[str, torch.Tensor],
|
||||
):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
self.weight = values["weight"]
|
||||
self.on_input = values["on_input"]
|
||||
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
weight = self.weight
|
||||
if not self.on_input:
|
||||
weight = weight.reshape(-1, 1)
|
||||
assert orig_weight is not None
|
||||
return orig_weight * weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
model_size += self.weight.nelement() * self.weight.element_size()
|
||||
model_size += self.on_input.nelement() * self.on_input.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
):
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
self.weight = self.weight.to(device=device, dtype=dtype)
|
||||
self.on_input = self.on_input.to(device=device, dtype=dtype)
|
||||
69
invokeai/backend/lora/loha_layer.py
Normal file
69
invokeai/backend/lora/loha_layer.py
Normal file
@ -0,0 +1,69 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora.lora_layer_base import LoRALayerBase
|
||||
|
||||
|
||||
class LoHALayer(LoRALayerBase):
|
||||
# w1_a: torch.Tensor
|
||||
# w1_b: torch.Tensor
|
||||
# w2_a: torch.Tensor
|
||||
# w2_b: torch.Tensor
|
||||
# t1: Optional[torch.Tensor] = None
|
||||
# t2: Optional[torch.Tensor] = None
|
||||
|
||||
def __init__(self, layer_key: str, values: Dict[str, torch.Tensor]):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
self.w1_a = values["hada_w1_a"]
|
||||
self.w1_b = values["hada_w1_b"]
|
||||
self.w2_a = values["hada_w2_a"]
|
||||
self.w2_b = values["hada_w2_b"]
|
||||
|
||||
if "hada_t1" in values:
|
||||
self.t1: Optional[torch.Tensor] = values["hada_t1"]
|
||||
else:
|
||||
self.t1 = None
|
||||
|
||||
if "hada_t2" in values:
|
||||
self.t2: Optional[torch.Tensor] = values["hada_t2"]
|
||||
else:
|
||||
self.t2 = None
|
||||
|
||||
self.rank = self.w1_b.shape[0]
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
if self.t1 is None:
|
||||
weight: torch.Tensor = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
|
||||
|
||||
else:
|
||||
rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
|
||||
rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
|
||||
weight = rebuild1 * rebuild2
|
||||
|
||||
return weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
|
||||
if val is not None:
|
||||
model_size += val.nelement() * val.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
self.w1_a = self.w1_a.to(device=device, dtype=dtype)
|
||||
self.w1_b = self.w1_b.to(device=device, dtype=dtype)
|
||||
if self.t1 is not None:
|
||||
self.t1 = self.t1.to(device=device, dtype=dtype)
|
||||
|
||||
self.w2_a = self.w2_a.to(device=device, dtype=dtype)
|
||||
self.w2_b = self.w2_b.to(device=device, dtype=dtype)
|
||||
if self.t2 is not None:
|
||||
self.t2 = self.t2.to(device=device, dtype=dtype)
|
||||
110
invokeai/backend/lora/lokr_layer.py
Normal file
110
invokeai/backend/lora/lokr_layer.py
Normal file
@ -0,0 +1,110 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora.lora_layer_base import LoRALayerBase
|
||||
|
||||
|
||||
class LoKRLayer(LoRALayerBase):
|
||||
# w1: Optional[torch.Tensor] = None
|
||||
# w1_a: Optional[torch.Tensor] = None
|
||||
# w1_b: Optional[torch.Tensor] = None
|
||||
# w2: Optional[torch.Tensor] = None
|
||||
# w2_a: Optional[torch.Tensor] = None
|
||||
# w2_b: Optional[torch.Tensor] = None
|
||||
# t2: Optional[torch.Tensor] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: Dict[str, torch.Tensor],
|
||||
):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
if "lokr_w1" in values:
|
||||
self.w1: Optional[torch.Tensor] = values["lokr_w1"]
|
||||
self.w1_a = None
|
||||
self.w1_b = None
|
||||
else:
|
||||
self.w1 = None
|
||||
self.w1_a = values["lokr_w1_a"]
|
||||
self.w1_b = values["lokr_w1_b"]
|
||||
|
||||
if "lokr_w2" in values:
|
||||
self.w2: Optional[torch.Tensor] = values["lokr_w2"]
|
||||
self.w2_a = None
|
||||
self.w2_b = None
|
||||
else:
|
||||
self.w2 = None
|
||||
self.w2_a = values["lokr_w2_a"]
|
||||
self.w2_b = values["lokr_w2_b"]
|
||||
|
||||
if "lokr_t2" in values:
|
||||
self.t2: Optional[torch.Tensor] = values["lokr_t2"]
|
||||
else:
|
||||
self.t2 = None
|
||||
|
||||
if "lokr_w1_b" in values:
|
||||
self.rank = values["lokr_w1_b"].shape[0]
|
||||
elif "lokr_w2_b" in values:
|
||||
self.rank = values["lokr_w2_b"].shape[0]
|
||||
else:
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
w1: Optional[torch.Tensor] = self.w1
|
||||
if w1 is None:
|
||||
assert self.w1_a is not None
|
||||
assert self.w1_b is not None
|
||||
w1 = self.w1_a @ self.w1_b
|
||||
|
||||
w2 = self.w2
|
||||
if w2 is None:
|
||||
if self.t2 is None:
|
||||
assert self.w2_a is not None
|
||||
assert self.w2_b is not None
|
||||
w2 = self.w2_a @ self.w2_b
|
||||
else:
|
||||
w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
|
||||
|
||||
if len(w2.shape) == 4:
|
||||
w1 = w1.unsqueeze(2).unsqueeze(2)
|
||||
w2 = w2.contiguous()
|
||||
assert w1 is not None
|
||||
assert w2 is not None
|
||||
weight = torch.kron(w1, w2)
|
||||
|
||||
return weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
|
||||
if val is not None:
|
||||
model_size += val.nelement() * val.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
if self.w1 is not None:
|
||||
self.w1 = self.w1.to(device=device, dtype=dtype)
|
||||
else:
|
||||
assert self.w1_a is not None
|
||||
assert self.w1_b is not None
|
||||
self.w1_a = self.w1_a.to(device=device, dtype=dtype)
|
||||
self.w1_b = self.w1_b.to(device=device, dtype=dtype)
|
||||
|
||||
if self.w2 is not None:
|
||||
self.w2 = self.w2.to(device=device, dtype=dtype)
|
||||
else:
|
||||
assert self.w2_a is not None
|
||||
assert self.w2_b is not None
|
||||
self.w2_a = self.w2_a.to(device=device, dtype=dtype)
|
||||
self.w2_b = self.w2_b.to(device=device, dtype=dtype)
|
||||
|
||||
if self.t2 is not None:
|
||||
self.t2 = self.t2.to(device=device, dtype=dtype)
|
||||
58
invokeai/backend/lora/lora_layer.py
Normal file
58
invokeai/backend/lora/lora_layer.py
Normal file
@ -0,0 +1,58 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora.lora_layer_base import LoRALayerBase
|
||||
|
||||
|
||||
# TODO: find and debug lora/locon with bias
|
||||
class LoRALayer(LoRALayerBase):
|
||||
# up: torch.Tensor
|
||||
# mid: Optional[torch.Tensor]
|
||||
# down: torch.Tensor
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: Dict[str, torch.Tensor],
|
||||
):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
self.up = values["lora_up.weight"]
|
||||
self.down = values["lora_down.weight"]
|
||||
if "lora_mid.weight" in values:
|
||||
self.mid: Optional[torch.Tensor] = values["lora_mid.weight"]
|
||||
else:
|
||||
self.mid = None
|
||||
|
||||
self.rank = self.down.shape[0]
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
if self.mid is not None:
|
||||
up = self.up.reshape(self.up.shape[0], self.up.shape[1])
|
||||
down = self.down.reshape(self.down.shape[0], self.down.shape[1])
|
||||
weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
|
||||
else:
|
||||
weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
|
||||
|
||||
return weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
for val in [self.up, self.mid, self.down]:
|
||||
if val is not None:
|
||||
model_size += val.nelement() * val.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
self.up = self.up.to(device=device, dtype=dtype)
|
||||
self.down = self.down.to(device=device, dtype=dtype)
|
||||
|
||||
if self.mid is not None:
|
||||
self.mid = self.mid.to(device=device, dtype=dtype)
|
||||
55
invokeai/backend/lora/lora_layer_base.py
Normal file
55
invokeai/backend/lora/lora_layer_base.py
Normal file
@ -0,0 +1,55 @@
|
||||
from typing import Dict, Optional
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
class LoRALayerBase:
|
||||
# rank: Optional[int]
|
||||
# alpha: Optional[float]
|
||||
# bias: Optional[torch.Tensor]
|
||||
# layer_key: str
|
||||
|
||||
# @property
|
||||
# def scale(self):
|
||||
# return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: Dict[str, torch.Tensor],
|
||||
):
|
||||
if "alpha" in values:
|
||||
self.alpha = values["alpha"].item()
|
||||
else:
|
||||
self.alpha = None
|
||||
|
||||
if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
|
||||
self.bias: Optional[torch.Tensor] = torch.sparse_coo_tensor(
|
||||
values["bias_indices"],
|
||||
values["bias_values"],
|
||||
tuple(values["bias_size"]),
|
||||
)
|
||||
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
self.rank = None # set in layer implementation
|
||||
self.layer_key = layer_key
|
||||
|
||||
def get_weight(self, orig_weight: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
raise NotImplementedError()
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = 0
|
||||
for val in [self.bias]:
|
||||
if val is not None:
|
||||
model_size += val.nelement() * val.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
if self.bias is not None:
|
||||
self.bias = self.bias.to(device=device, dtype=dtype)
|
||||
179
invokeai/backend/lora/lora_model.py
Normal file
179
invokeai/backend/lora/lora_model.py
Normal file
@ -0,0 +1,179 @@
|
||||
import bisect
|
||||
from pathlib import Path
|
||||
from typing import Dict, Optional, Union
|
||||
|
||||
import torch
|
||||
from safetensors.torch import load_file
|
||||
from typing_extensions import Self
|
||||
|
||||
from invokeai.backend.lora.full_layer import FullLayer
|
||||
from invokeai.backend.lora.ia3_layer import IA3Layer
|
||||
from invokeai.backend.lora.loha_layer import LoHALayer
|
||||
from invokeai.backend.lora.lokr_layer import LoKRLayer
|
||||
from invokeai.backend.lora.lora_layer import LoRALayer
|
||||
from invokeai.backend.lora.sdxl_state_dict_utils import SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP
|
||||
from invokeai.backend.model_manager import BaseModelType
|
||||
|
||||
AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer]
|
||||
|
||||
|
||||
class LoRAModelRaw(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
layers: Dict[str, AnyLoRALayer],
|
||||
):
|
||||
super().__init__()
|
||||
self._name = name
|
||||
self.layers = layers
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return self._name
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> None:
|
||||
# TODO: try revert if exception?
|
||||
for _key, layer in self.layers.items():
|
||||
layer.to(device=device, dtype=dtype)
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = 0
|
||||
for _, layer in self.layers.items():
|
||||
model_size += layer.calc_size()
|
||||
return model_size
|
||||
|
||||
@classmethod
|
||||
def _convert_sdxl_keys_to_diffusers_format(cls, state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
|
||||
"""Convert the keys of an SDXL LoRA state_dict to diffusers format.
|
||||
|
||||
The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
|
||||
diffusers format, then this function will have no effect.
|
||||
|
||||
This function is adapted from:
|
||||
https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
|
||||
|
||||
Args:
|
||||
state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
|
||||
|
||||
Raises:
|
||||
ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
|
||||
|
||||
Returns:
|
||||
Dict[str, Tensor]: The diffusers-format state_dict.
|
||||
"""
|
||||
converted_count = 0 # The number of Stability AI keys converted to diffusers format.
|
||||
not_converted_count = 0 # The number of keys that were not converted.
|
||||
|
||||
# Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
|
||||
# For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
|
||||
# `input_blocks_4_1_proj_in`.
|
||||
stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
|
||||
stability_unet_keys.sort()
|
||||
|
||||
new_state_dict = {}
|
||||
for full_key, value in state_dict.items():
|
||||
if full_key.startswith("lora_unet_"):
|
||||
search_key = full_key.replace("lora_unet_", "")
|
||||
# Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
|
||||
position = bisect.bisect_right(stability_unet_keys, search_key)
|
||||
map_key = stability_unet_keys[position - 1]
|
||||
# Now, check if the map_key *actually* matches the search_key.
|
||||
if search_key.startswith(map_key):
|
||||
new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
|
||||
new_state_dict[new_key] = value
|
||||
converted_count += 1
|
||||
else:
|
||||
new_state_dict[full_key] = value
|
||||
not_converted_count += 1
|
||||
elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
|
||||
# The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
|
||||
new_state_dict[full_key] = value
|
||||
continue
|
||||
else:
|
||||
raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
|
||||
|
||||
if converted_count > 0 and not_converted_count > 0:
|
||||
raise ValueError(
|
||||
f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
|
||||
f" not_converted={not_converted_count}"
|
||||
)
|
||||
|
||||
return new_state_dict
|
||||
|
||||
@classmethod
|
||||
def from_checkpoint(
|
||||
cls,
|
||||
file_path: Union[str, Path],
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
base_model: Optional[BaseModelType] = None,
|
||||
) -> Self:
|
||||
device = device or torch.device("cpu")
|
||||
dtype = dtype or torch.float32
|
||||
|
||||
if isinstance(file_path, str):
|
||||
file_path = Path(file_path)
|
||||
|
||||
model = cls(
|
||||
name=file_path.stem,
|
||||
layers={},
|
||||
)
|
||||
|
||||
if file_path.suffix == ".safetensors":
|
||||
sd = load_file(file_path.absolute().as_posix(), device="cpu")
|
||||
else:
|
||||
sd = torch.load(file_path, map_location="cpu")
|
||||
|
||||
state_dict = cls._group_state(sd)
|
||||
|
||||
if base_model == BaseModelType.StableDiffusionXL:
|
||||
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:
|
||||
layer: AnyLoRALayer = LoRALayer(layer_key, values)
|
||||
|
||||
# loha
|
||||
elif "hada_w1_b" in values:
|
||||
layer = LoHALayer(layer_key, values)
|
||||
|
||||
# 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
|
||||
93
invokeai/backend/lora/sdxl_state_dict_utils.py
Normal file
93
invokeai/backend/lora/sdxl_state_dict_utils.py
Normal file
@ -0,0 +1,93 @@
|
||||
from typing import List, Tuple
|
||||
|
||||
|
||||
# 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."))
|
||||
|
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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()
|
||||
}
|
||||
@ -4,7 +4,7 @@ import torch
|
||||
from diffusers.models.modeling_utils import ModelMixin
|
||||
|
||||
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
|
||||
from invokeai.backend.lora import LoRAModelRaw
|
||||
from invokeai.backend.lora.lora_model import LoRAModelRaw
|
||||
from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
|
||||
from invokeai.backend.textual_inversion import TextualInversionModelRaw
|
||||
|
||||
|
||||
@ -6,7 +6,7 @@ from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.backend.lora import LoRAModelRaw
|
||||
from invokeai.backend.lora.lora_model import LoRAModelRaw
|
||||
from invokeai.backend.model_manager import (
|
||||
AnyModelConfig,
|
||||
BaseModelType,
|
||||
|
||||
@ -13,11 +13,11 @@ from diffusers import OnnxRuntimeModel, UNet2DConditionModel
|
||||
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
|
||||
|
||||
from invokeai.app.shared.models import FreeUConfig
|
||||
from invokeai.backend.lora.lora_model import LoRAModelRaw
|
||||
from invokeai.backend.model_manager.any_model_type import AnyModel
|
||||
from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
|
||||
from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
|
||||
|
||||
from .lora import LoRAModelRaw
|
||||
from .textual_inversion import TextualInversionManager, TextualInversionModelRaw
|
||||
|
||||
"""
|
||||
|
||||
@ -5,7 +5,8 @@
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from invokeai.backend.lora import LoRALayer, LoRAModelRaw
|
||||
from invokeai.backend.lora.lora_layer import LoRALayer
|
||||
from invokeai.backend.lora.lora_model import LoRAModelRaw
|
||||
from invokeai.backend.model_patcher import ModelPatcher
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user