Add links to test models for loha, lokr, ia3.

invokeai/app/invocations/compel.py

@@ -19,7 +19,8 @@ from invokeai.app.invocations.model import CLIPField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
-from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -82,9 +83,10 @@ class CompelInvocation(BaseInvocation):
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora_text_encoder(
-                text_encoder,
-                loras=_lora_loader(),
+            LoraPatcher.apply_lora_patches(
+                model=text_encoder,
+                patches=_lora_loader(),
+                prefix="lora_te_",
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -177,9 +179,9 @@ class SDXLPromptInvocationBase:
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
             tokenizer_info as tokenizer,
-            ModelPatcher.apply_lora(
+            LoraPatcher.apply_lora_patches(
                 text_encoder,
-                loras=_lora_loader(),
+                patches=_lora_loader(),
                 prefix=lora_prefix,
                 cached_weights=cached_weights,
             ),

invokeai/app/invocations/denoise_latents.py

@@ -36,7 +36,8 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
@@ -979,9 +980,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
             SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
-            ModelPatcher.apply_lora_unet(
-                unet,
-                loras=_lora_loader(),
+            LoraPatcher.apply_lora_patches(
+                model=unet,
+                patches=_lora_loader(),
+                prefix="lora_unet_",
                 cached_weights=cached_weights,
             ),
         ):

invokeai/app/invocations/flux_denoise.py

@@ -1,4 +1,5 @@
-from typing import Callable, Optional
+from contextlib import ExitStack
+from typing import Callable, Iterator, Optional, Tuple
 
 import torch
 import torchvision.transforms as tv_transforms
@@ -29,6 +30,9 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
+from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
@@ -187,9 +191,40 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
-        with transformer_info as transformer:
+        with (
+            transformer_info.model_on_device() as (cached_weights, transformer),
+            ExitStack() as exit_stack,
+        ):
             assert isinstance(transformer, Flux)
 
+            config = transformer_info.config
+            assert config is not None
+
+            # Apply LoRA models to the transformer.
+            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            if config.format in [ModelFormat.Checkpoint]:
+                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
+                exit_stack.enter_context(
+                    LoraPatcher.apply_lora_patches(
+                        model=transformer,
+                        patches=self._lora_iterator(context),
+                        prefix="",
+                        cached_weights=cached_weights,
+                    )
+                )
+            elif config.format in [ModelFormat.BnbQuantizedLlmInt8b, ModelFormat.BnbQuantizednf4b]:
+                # The model is quantized, so apply the LoRA weights as sidecar layers. This results in slower inference,
+                # than directly patching the weights, but is agnostic to the quantization format.
+                exit_stack.enter_context(
+                    LoraPatcher.apply_lora_sidecar_patches(
+                        model=transformer,
+                        patches=self._lora_iterator(context),
+                        prefix="",
+                    )
+                )
+            else:
+                raise ValueError(f"Unsupported model format: {config.format}")
+
             x = denoise(
                 model=transformer,
                 img=x,
@@ -241,6 +276,13 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # `latents`.
         return mask.expand_as(latents)
 
+    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
+        for lora in self.transformer.loras:
+            lora_info = context.models.load(lora.lora)
+            assert isinstance(lora_info.model, LoRAModelRaw)
+            yield (lora_info.model, lora.weight)
+            del lora_info
+
     def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
         def step_callback(state: PipelineIntermediateState) -> None:
             state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()

invokeai/app/invocations/flux_lora_loader.py

@@ -0,0 +1,53 @@
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import LoRAField, ModelIdentifierField, TransformerField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("flux_lora_loader_output")
+class FluxLoRALoaderOutput(BaseInvocationOutput):
+    """FLUX LoRA Loader Output"""
+
+    transformer: TransformerField = OutputField(
+        default=None, description=FieldDescriptions.transformer, title="FLUX Transformer"
+    )
+
+
+@invocation(
+    "flux_lora_loader",
+    title="FLUX LoRA",
+    tags=["lora", "model", "flux"],
+    category="model",
+    version="1.0.0",
+)
+class FluxLoRALoaderInvocation(BaseInvocation):
+    """Apply a LoRA model to a FLUX transformer."""
+
+    lora: ModelIdentifierField = InputField(
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
+    )
+    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.transformer,
+        input=Input.Connection,
+        title="FLUX Transformer",
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
+        lora_key = self.lora.key
+
+        if not context.models.exists(lora_key):
+            raise ValueError(f"Unknown lora: {lora_key}!")
+
+        if any(lora.lora.key == lora_key for lora in self.transformer.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to transformer.')
+
+        transformer = self.transformer.model_copy(deep=True)
+        transformer.loras.append(
+            LoRAField(
+                lora=self.lora,
+                weight=self.weight,
+            )
+        )
+
+        return FluxLoRALoaderOutput(transformer=transformer)

invokeai/app/invocations/model.py

@@ -69,6 +69,7 @@ class CLIPField(BaseModel):
 
 class TransformerField(BaseModel):
     transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
 class T5EncoderField(BaseModel):
@@ -202,7 +203,7 @@ class FluxModelLoaderInvocation(BaseInvocation):
         assert isinstance(transformer_config, CheckpointConfigBase)
 
         return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer),
+            transformer=TransformerField(transformer=transformer, loras=[]),
             clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
             t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
             vae=VAEField(vae=vae),

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -22,8 +22,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
@@ -204,7 +204,11 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         # Load the UNet model.
         unet_info = context.models.load(self.unet.unet)
 
-        with ExitStack() as exit_stack, unet_info as unet, ModelPatcher.apply_lora_unet(unet, _lora_loader()):
+        with (
+            ExitStack() as exit_stack,
+            unet_info as unet,
+            LoraPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
+        ):
             assert isinstance(unet, UNet2DConditionModel)
             latents = latents.to(device=unet.device, dtype=unet.dtype)
             if noise is not None:

invokeai/backend/lora.py

@@ -1,672 +0,0 @@
-# Copyright (c) 2024 The InvokeAI Development team
-"""LoRA model support."""
-
-import bisect
-from pathlib import Path
-from typing import Dict, List, Optional, Set, Tuple, Union
-
-import torch
-from safetensors.torch import load_file
-from typing_extensions import Self
-
-import invokeai.backend.util.logging as logger
-from invokeai.backend.model_manager import BaseModelType
-from invokeai.backend.raw_model import RawModel
-
-
-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: torch.Tensor) -> torch.Tensor:
-        raise NotImplementedError()
-
-    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
-        return self.bias
-
-    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
-        params = {"weight": self.get_weight(orig_module.weight)}
-        bias = self.get_bias(orig_module.bias)
-        if bias is not None:
-            params["bias"] = bias
-        return params
-
-    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)
-
-    def check_keys(self, values: Dict[str, torch.Tensor], known_keys: Set[str]):
-        """Log a warning if values contains unhandled keys."""
-        # {"alpha", "bias_indices", "bias_values", "bias_size"} are hard-coded, because they are handled by
-        # `LoRALayerBase`. Sub-classes should provide the known_keys that they handled.
-        all_known_keys = known_keys | {"alpha", "bias_indices", "bias_values", "bias_size"}
-        unknown_keys = set(values.keys()) - all_known_keys
-        if unknown_keys:
-            logger.warning(
-                f"Unexpected keys found in LoRA/LyCORIS layer, model might work incorrectly! Keys: {unknown_keys}"
-            )
-
-
-# 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"]
-        self.mid = values.get("lora_mid.weight", None)
-
-        self.rank = self.down.shape[0]
-        self.check_keys(
-            values,
-            {
-                "lora_up.weight",
-                "lora_down.weight",
-                "lora_mid.weight",
-            },
-        )
-
-    def get_weight(self, orig_weight: 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)
-
-
-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"]
-        self.t1 = values.get("hada_t1", None)
-        self.t2 = values.get("hada_t2", None)
-
-        self.rank = self.w1_b.shape[0]
-        self.check_keys(
-            values,
-            {
-                "hada_w1_a",
-                "hada_w1_b",
-                "hada_w2_a",
-                "hada_w2_b",
-                "hada_t1",
-                "hada_t2",
-            },
-        )
-
-    def get_weight(self, orig_weight: 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)
-
-
-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)
-
-        self.w1 = values.get("lokr_w1", None)
-        if self.w1 is None:
-            self.w1_a = values["lokr_w1_a"]
-            self.w1_b = values["lokr_w1_b"]
-        else:
-            self.w1_b = None
-            self.w1_a = None
-
-        self.w2 = values.get("lokr_w2", None)
-        if self.w2 is None:
-            self.w2_a = values["lokr_w2_a"]
-            self.w2_b = values["lokr_w2_b"]
-        else:
-            self.w2_a = None
-            self.w2_b = None
-
-        self.t2 = values.get("lokr_t2", None)
-
-        if self.w1_b is not None:
-            self.rank = self.w1_b.shape[0]
-        elif self.w2_b is not None:
-            self.rank = self.w2_b.shape[0]
-        else:
-            self.rank = None  # unscaled
-
-        self.check_keys(
-            values,
-            {
-                "lokr_w1",
-                "lokr_w1_a",
-                "lokr_w1_b",
-                "lokr_w2",
-                "lokr_w2_a",
-                "lokr_w2_b",
-                "lokr_t2",
-            },
-        )
-
-    def get_weight(self, orig_weight: 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)
-
-
-class FullLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["diff"]
-        self.bias = values.get("diff_b", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"diff", "diff_b"})
-
-    def get_weight(self, orig_weight: 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)
-
-
-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
-        self.check_keys(values, {"weight", "on_input"})
-
-    def get_weight(self, orig_weight: 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)
-
-
-class NormLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["w_norm"]
-        self.bias = values.get("b_norm", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"w_norm", "b_norm"})
-
-    def get_weight(self, orig_weight: 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)
-
-
-AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer]
-
-
-class LoRAModelRaw(RawModel):  # (torch.nn.Module):
-    _name: str
-    layers: Dict[str, AnyLoRALayer]
-
-    def __init__(
-        self,
-        name: str,
-        layers: Dict[str, AnyLoRALayer],
-    ):
-        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():
-            # Detect layers according to LyCORIS detection logic(`weight_list_det`)
-            # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
-
-            # lora and locon
-            if "lora_up.weight" in values:
-                layer: AnyLoRALayer = LoRALayer(layer_key, values)
-
-            # loha
-            elif "hada_w1_a" in values:
-                layer = LoHALayer(layer_key, values)
-
-            # lokr
-            elif "lokr_w1" in values or "lokr_w1_a" in values:
-                layer = LoKRLayer(layer_key, values)
-
-            # diff
-            elif "diff" in values:
-                layer = FullLayer(layer_key, values)
-
-            # ia3
-            elif "on_input" in values:
-                layer = IA3Layer(layer_key, values)
-
-            # norms
-            elif "w_norm" in values:
-                layer = NormLayer(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()
-}

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -0,0 +1,209 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+
+
+def is_state_dict_likely_in_flux_diffusers_format(state_dict: Dict[str, torch.Tensor]) -> bool:
+    """Checks if the provided state dict is likely in the Diffusers FLUX LoRA format.
+
+    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision. (A
+    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
+    """
+    # First, check that all keys end in "lora_A.weight" or "lora_B.weight" (i.e. are in PEFT format).
+    all_keys_in_peft_format = all(k.endswith(("lora_A.weight", "lora_B.weight")) for k in state_dict.keys())
+
+    # Next, check that this is likely a FLUX model by spot-checking a few keys.
+    expected_keys = [
+        "transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight",
+        "transformer.single_transformer_blocks.0.attn.to_q.lora_B.weight",
+        "transformer.transformer_blocks.0.attn.add_q_proj.lora_A.weight",
+        "transformer.transformer_blocks.0.attn.add_q_proj.lora_B.weight",
+    ]
+    all_expected_keys_present = all(k in state_dict for k in expected_keys)
+
+    return all_keys_in_peft_format and all_expected_keys_present
+
+
+def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor], alpha: float) -> LoRAModelRaw:  # pyright: ignore[reportRedeclaration] (state_dict is intentionally re-declared)
+    """Loads a state dict in the Diffusers FLUX LoRA format into a LoRAModelRaw object.
+
+    This function is based on:
+    https://github.com/huggingface/diffusers/blob/55ac421f7bb12fd00ccbef727be4dc2f3f920abb/scripts/convert_flux_to_diffusers.py
+    """
+    # Group keys by layer.
+    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_by_layer(state_dict)
+
+    # Remove the "transformer." prefix from all keys.
+    grouped_state_dict = {k.replace("transformer.", ""): v for k, v in grouped_state_dict.items()}
+
+    # Constants for FLUX.1
+    num_double_layers = 19
+    num_single_layers = 38
+    # inner_dim = 3072
+    # mlp_ratio = 4.0
+
+    layers: dict[str, AnyLoRALayer] = {}
+
+    def add_lora_layer_if_present(src_key: str, dst_key: str) -> None:
+        if src_key in grouped_state_dict:
+            src_layer_dict = grouped_state_dict.pop(src_key)
+            layers[dst_key] = LoRALayer.from_state_dict_values(
+                values={
+                    "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
+                    "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
+                    "alpha": torch.tensor(alpha),
+                },
+            )
+            assert len(src_layer_dict) == 0
+
+    def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None:
+        """Handle the Q, K, V matrices for a transformer block. We need special handling because the diffusers format
+        stores them in separate matrices, whereas the BFL format used internally by InvokeAI concatenates them.
+        """
+        # We expect that either all src keys are present or none of them are. Verify this.
+        keys_present = [key in grouped_state_dict for key in src_keys]
+        assert all(keys_present) or not any(keys_present)
+
+        # If none of the keys are present, return early.
+        if not any(keys_present):
+            return
+
+        src_layer_dicts = [grouped_state_dict.pop(key) for key in src_keys]
+        sub_layers: list[LoRALayerBase] = []
+        for src_layer_dict in src_layer_dicts:
+            sub_layers.append(
+                LoRALayer.from_state_dict_values(
+                    values={
+                        "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
+                        "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
+                        "alpha": torch.tensor(alpha),
+                    },
+                )
+            )
+            assert len(src_layer_dict) == 0
+        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers, concat_axis=0)
+
+    # time_text_embed.timestep_embedder -> time_in.
+    add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")
+    add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_2", "time_in.out_layer")
+
+    # time_text_embed.text_embedder -> vector_in.
+    add_lora_layer_if_present("time_text_embed.text_embedder.linear_1", "vector_in.in_layer")
+    add_lora_layer_if_present("time_text_embed.text_embedder.linear_2", "vector_in.out_layer")
+
+    # time_text_embed.guidance_embedder -> guidance_in.
+    add_lora_layer_if_present("time_text_embed.guidance_embedder.linear_1", "guidance_in")
+    add_lora_layer_if_present("time_text_embed.guidance_embedder.linear_2", "guidance_in")
+
+    # context_embedder -> txt_in.
+    add_lora_layer_if_present("context_embedder", "txt_in")
+
+    # x_embedder -> img_in.
+    add_lora_layer_if_present("x_embedder", "img_in")
+
+    # Double transformer blocks.
+    for i in range(num_double_layers):
+        # norms.
+        add_lora_layer_if_present(f"transformer_blocks.{i}.norm1.linear", f"double_blocks.{i}.img_mod.lin")
+        add_lora_layer_if_present(f"transformer_blocks.{i}.norm1_context.linear", f"double_blocks.{i}.txt_mod.lin")
+
+        # Q, K, V
+        add_qkv_lora_layer_if_present(
+            [
+                f"transformer_blocks.{i}.attn.to_q",
+                f"transformer_blocks.{i}.attn.to_k",
+                f"transformer_blocks.{i}.attn.to_v",
+            ],
+            f"double_blocks.{i}.img_attn.qkv",
+        )
+        add_qkv_lora_layer_if_present(
+            [
+                f"transformer_blocks.{i}.attn.add_q_proj",
+                f"transformer_blocks.{i}.attn.add_k_proj",
+                f"transformer_blocks.{i}.attn.add_v_proj",
+            ],
+            f"double_blocks.{i}.txt_attn.qkv",
+        )
+
+        # ff img_mlp
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.ff.net.0.proj",
+            f"double_blocks.{i}.img_mlp.0",
+        )
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.ff.net.2",
+            f"double_blocks.{i}.img_mlp.2",
+        )
+
+        # ff txt_mlp
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.ff_context.net.0.proj",
+            f"double_blocks.{i}.txt_mlp.0",
+        )
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.ff_context.net.2",
+            f"double_blocks.{i}.txt_mlp.2",
+        )
+
+        # output projections.
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.attn.to_out.0",
+            f"double_blocks.{i}.img_attn.proj",
+        )
+        add_lora_layer_if_present(
+            f"transformer_blocks.{i}.attn.to_add_out",
+            f"double_blocks.{i}.txt_attn.proj",
+        )
+
+    # Single transformer blocks.
+    for i in range(num_single_layers):
+        # norms
+        add_lora_layer_if_present(
+            f"single_transformer_blocks.{i}.norm.linear",
+            f"single_blocks.{i}.modulation.lin",
+        )
+
+        # Q, K, V, mlp
+        add_qkv_lora_layer_if_present(
+            [
+                f"single_transformer_blocks.{i}.attn.to_q",
+                f"single_transformer_blocks.{i}.attn.to_k",
+                f"single_transformer_blocks.{i}.attn.to_v",
+                f"single_transformer_blocks.{i}.proj_mlp",
+            ],
+            f"single_blocks.{i}.linear1",
+        )
+
+        # Output projections.
+        add_lora_layer_if_present(
+            f"single_transformer_blocks.{i}.proj_out",
+            f"single_blocks.{i}.linear2",
+        )
+
+    # Final layer.
+    add_lora_layer_if_present("proj_out", "final_layer.linear")
+
+    # Assert that all keys were processed.
+    assert len(grouped_state_dict) == 0
+
+    return LoRAModelRaw(layers=layers)
+
+
+def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:
+    """Groups the keys in the state dict by layer."""
+    layer_dict: dict[str, dict[str, torch.Tensor]] = {}
+    for key in state_dict:
+        # Split the 'lora_A.weight' or 'lora_B.weight' suffix from the layer name.
+        parts = key.rsplit(".", maxsplit=2)
+        layer_name = parts[0]
+        key_name = ".".join(parts[1:])
+        if layer_name not in layer_dict:
+            layer_dict[layer_name] = {}
+        layer_dict[layer_name][key_name] = state_dict[key]
+    return layer_dict

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -0,0 +1,80 @@
+import re
+from typing import Any, Dict, TypeVar
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+
+# A regex pattern that matches all of the keys in the Kohya FLUX LoRA format.
+# Example keys:
+#   lora_unet_double_blocks_0_img_attn_proj.alpha
+#   lora_unet_double_blocks_0_img_attn_proj.lora_down.weight
+#   lora_unet_double_blocks_0_img_attn_proj.lora_up.weight
+FLUX_KOHYA_KEY_REGEX = (
+    r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
+)
+
+
+def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> bool:
+    """Checks if the provided state dict is likely in the Kohya FLUX LoRA format.
+
+    This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
+    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
+    """
+    return all(re.match(FLUX_KOHYA_KEY_REGEX, k) for k in state_dict.keys())
+
+
+def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
+    # Group keys by layer.
+    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = {}
+    for key, value in state_dict.items():
+        layer_name, param_name = key.split(".", 1)
+        if layer_name not in grouped_state_dict:
+            grouped_state_dict[layer_name] = {}
+        grouped_state_dict[layer_name][param_name] = value
+
+    # Convert the state dict to the InvokeAI format.
+    grouped_state_dict = convert_flux_kohya_state_dict_to_invoke_format(grouped_state_dict)
+
+    # Create LoRA layers.
+    layers: dict[str, AnyLoRALayer] = {}
+    for layer_key, layer_state_dict in grouped_state_dict.items():
+        layers[layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+
+    # Create and return the LoRAModelRaw.
+    return LoRAModelRaw(layers=layers)
+
+
+T = TypeVar("T")
+
+
+def convert_flux_kohya_state_dict_to_invoke_format(state_dict: Dict[str, T]) -> Dict[str, T]:
+    """Converts a state dict from the Kohya FLUX LoRA format to LoRA weight format used internally by InvokeAI.
+
+    Example key conversions:
+    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
+    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
+    "lora_unet_double_blocks_0_img_attn_proj" -> "double_blocks.0.img_attn.proj"
+    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img_attn.qkv"
+    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
+    "lora_unet_double_blocks_0_img_attn_qkv" -> "double_blocks.0.img.attn.qkv"
+    """
+
+    def replace_func(match: re.Match[str]) -> str:
+        s = f"{match.group(1)}.{match.group(2)}.{match.group(3)}"
+        if match.group(4):
+            s += f".{match.group(4)}"
+        return s
+
+    converted_dict: dict[str, T] = {}
+    for k, v in state_dict.items():
+        match = re.match(FLUX_KOHYA_KEY_REGEX, k)
+        if match:
+            new_key = re.sub(FLUX_KOHYA_KEY_REGEX, replace_func, k)
+            converted_dict[new_key] = v
+        else:
+            raise ValueError(f"Key '{k}' does not match the expected pattern for FLUX LoRA weights.")
+
+    return converted_dict

invokeai/backend/lora/conversions/sd_lora_conversion_utils.py

@@ -0,0 +1,29 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+
+
+def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
+    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_state(state_dict)
+
+    layers: dict[str, AnyLoRALayer] = {}
+    for layer_key, values in grouped_state_dict.items():
+        layers[layer_key] = any_lora_layer_from_state_dict(values)
+
+    return LoRAModelRaw(layers=layers)
+
+
+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

invokeai/backend/lora/conversions/sdxl_lora_conversion_utils.py

@@ -0,0 +1,154 @@
+import bisect
+from typing import Dict, List, Tuple, TypeVar
+
+T = TypeVar("T")
+
+
+def convert_sdxl_keys_to_diffusers_format(state_dict: Dict[str, T]) -> dict[str, T]:
+    """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: dict[str, T] = {}
+    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
+
+
+# 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: list[tuple[str, str]] = []
+
+    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: list[tuple[str, str]] = []
+    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()
+}

invokeai/backend/lora/layers/any_lora_layer.py

@@ -0,0 +1,11 @@
+from typing import Union
+
+from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.lora.layers.full_layer import FullLayer
+from invokeai.backend.lora.layers.ia3_layer import IA3Layer
+from invokeai.backend.lora.layers.loha_layer import LoHALayer
+from invokeai.backend.lora.layers.lokr_layer import LoKRLayer
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+from invokeai.backend.lora.layers.norm_layer import NormLayer
+
+AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer, ConcatenatedLoRALayer]

invokeai/backend/lora/layers/concatenated_lora_layer.py

@@ -0,0 +1,46 @@
+from typing import List, Optional
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class ConcatenatedLoRALayer(LoRALayerBase):
+    """A LoRA layer that is composed of multiple LoRA layers concatenated along a specified axis.
+
+    This class was created to handle a special case with FLUX LoRA models. In the BFL FLUX model format, the attention
+    Q, K, V matrices are concatenated along the first dimension. In the diffusers LoRA format, the Q, K, V matrices are
+    stored as separate tensors. This class enables diffusers LoRA layers to be used in BFL FLUX models.
+    """
+
+    def __init__(self, lora_layers: List[LoRALayerBase], concat_axis: int = 0):
+        super().__init__(alpha=None, bias=None)
+
+        self.lora_layers = torch.nn.ModuleList(lora_layers)
+        self.concat_axis = concat_axis
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        # TODO(ryand): Currently, we pass orig_weight=None to the sub-layers. If we want to support sub-layers that
+        # require this value, we will need to implement chunking of the original weight tensor here.
+        # Note that we must apply the sub-layer scales here.
+        layer_weights = [lora_layer.get_weight(None) * lora_layer.scale() for lora_layer in self.lora_layers]  # pyright: ignore[reportArgumentType]
+        return torch.cat(layer_weights, dim=self.concat_axis)
+
+    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
+        # TODO(ryand): Currently, we pass orig_bias=None to the sub-layers. If we want to support sub-layers that
+        # require this value, we will need to implement chunking of the original bias tensor here.
+        # Note that we must apply the sub-layer scales here.
+        layer_biases: list[torch.Tensor] = []
+        for lora_layer in self.lora_layers:
+            layer_bias = lora_layer.get_bias(None)
+            if layer_bias is not None:
+                layer_biases.append(layer_bias * lora_layer.scale())
+
+        if len(layer_biases) == 0:
+            return None
+
+        assert len(layer_biases) == len(self.lora_layers)
+        return torch.cat(layer_biases, dim=self.concat_axis)
+
+    def calc_size(self) -> int:
+        return sum(lora_layer.calc_size() for lora_layer in self.lora_layers)

invokeai/backend/lora/layers/full_layer.py

@@ -0,0 +1,26 @@
+from typing import Dict, Optional
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class FullLayer(LoRALayerBase):
+    def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor]):
+        super().__init__(alpha=None, bias=bias)
+        self.weight = torch.nn.Parameter(weight)
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        layer = cls(weight=values["diff"], bias=values.get("diff_b", None))
+        cls.warn_on_unhandled_keys(values=values, handled_keys={"diff", "diff_b"})
+        return layer
+
+    def rank(self) -> int | None:
+        return None
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        return self.weight

invokeai/backend/lora/layers/ia3_layer.py

@@ -0,0 +1,53 @@
+from typing import Dict, Optional
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class IA3Layer(LoRALayerBase):
+    """IA3 Layer
+
+    Example model for testing this layer type: https://civitai.com/models/123930/gwendolyn-tennyson-ben-10-ia3
+    """
+
+    def __init__(self, weight: torch.Tensor, on_input: torch.Tensor, bias: Optional[torch.Tensor]):
+        super().__init__(alpha=None, bias=bias)
+        self.weight = torch.nn.Parameter(weight)
+        self.on_input = torch.nn.Parameter(on_input)
+
+    def rank(self) -> int | None:
+        return None
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        bias = cls._parse_bias(
+            values.get("bias_indices", None), values.get("bias_values", None), values.get("bias_size", None)
+        )
+        layer = cls(
+            weight=values["weight"],
+            on_input=values["on_input"],
+            bias=bias,
+        )
+        cls.warn_on_unhandled_keys(
+            values=values,
+            handled_keys={
+                # Default keys.
+                "bias_indices",
+                "bias_values",
+                "bias_size",
+                # Layer-specific keys.
+                "weight",
+                "on_input",
+            },
+        )
+        return layer
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        weight = self.weight
+        if not self.on_input:
+            weight = weight.reshape(-1, 1)
+        return orig_weight * weight

invokeai/backend/lora/layers/loha_layer.py

@@ -0,0 +1,85 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class LoHALayer(LoRALayerBase):
+    """LoHA LyCoris layer.
+
+    Example model for testing this layer type: https://civitai.com/models/27397/loha-renoir-the-dappled-light-style
+    """
+
+    def __init__(
+        self,
+        w1_a: torch.Tensor,
+        w1_b: torch.Tensor,
+        w2_a: torch.Tensor,
+        w2_b: torch.Tensor,
+        t1: torch.Tensor | None,
+        t2: torch.Tensor | None,
+        alpha: float | None,
+        bias: torch.Tensor | None,
+    ):
+        super().__init__(alpha=alpha, bias=bias)
+        self.w1_a = torch.nn.Parameter(w1_a)
+        self.w1_b = torch.nn.Parameter(w1_b)
+        self.w2_a = torch.nn.Parameter(w2_a)
+        self.w2_b = torch.nn.Parameter(w2_b)
+        self.t1 = torch.nn.Parameter(t1) if t1 is not None else None
+        self.t2 = torch.nn.Parameter(t2) if t2 is not None else None
+        assert (self.t1 is None) == (self.t2 is None)
+
+    def rank(self) -> int | None:
+        return self.w1_b.shape[0]
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        alpha = cls._parse_alpha(values.get("alpha", None))
+        bias = cls._parse_bias(
+            values.get("bias_indices", None), values.get("bias_values", None), values.get("bias_size", None)
+        )
+        layer = cls(
+            w1_a=values["hada_w1_a"],
+            w1_b=values["hada_w1_b"],
+            w2_a=values["hada_w2_a"],
+            w2_b=values["hada_w2_b"],
+            t1=values.get("hada_t1", None),
+            t2=values.get("hada_t2", None),
+            alpha=alpha,
+            bias=bias,
+        )
+
+        cls.warn_on_unhandled_keys(
+            values=values,
+            handled_keys={
+                # Default keys.
+                "alpha",
+                "bias_indices",
+                "bias_values",
+                "bias_size",
+                # Layer-specific keys.
+                "hada_w1_a",
+                "hada_w1_b",
+                "hada_w2_a",
+                "hada_w2_b",
+                "hada_t1",
+                "hada_t2",
+            },
+        )
+
+        return layer
+
+    def get_weight(self, orig_weight: 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

invokeai/backend/lora/layers/lokr_layer.py

@@ -0,0 +1,110 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class LoKRLayer(LoRALayerBase):
+    """LoKR LyCoris layer.
+
+    Example model for testing this layer type: https://civitai.com/models/346747/lokrnekopara-allgirl-for-jru2
+    """
+
+    def __init__(
+        self,
+        w1: torch.Tensor | None,
+        w1_a: torch.Tensor | None,
+        w1_b: torch.Tensor | None,
+        w2: torch.Tensor | None,
+        w2_a: torch.Tensor | None,
+        w2_b: torch.Tensor | None,
+        t2: torch.Tensor | None,
+        alpha: float | None,
+        bias: torch.Tensor | None,
+    ):
+        super().__init__(alpha=alpha, bias=bias)
+        self.w1 = torch.nn.Parameter(w1) if w1 is not None else None
+        self.w1_a = torch.nn.Parameter(w1_a) if w1_a is not None else None
+        self.w1_b = torch.nn.Parameter(w1_b) if w1_b is not None else None
+        self.w2 = torch.nn.Parameter(w2) if w2 is not None else None
+        self.w2_a = torch.nn.Parameter(w2_a) if w2_a is not None else None
+        self.w2_b = torch.nn.Parameter(w2_b) if w2_b is not None else None
+        self.t2 = torch.nn.Parameter(t2) if t2 is not None else None
+
+        # Validate parameters.
+        assert (self.w1 is None) != (self.w1_a is None)
+        assert (self.w1_a is None) == (self.w1_b is None)
+        assert (self.w2 is None) != (self.w2_a is None)
+
+    def rank(self) -> int | None:
+        if self.w1_b is not None:
+            return self.w1_b.shape[0]
+        elif self.w2_b is not None:
+            return self.w2_b.shape[0]
+        else:
+            return None
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        alpha = cls._parse_alpha(values.get("alpha", None))
+        bias = cls._parse_bias(
+            values.get("bias_indices", None), values.get("bias_values", None), values.get("bias_size", None)
+        )
+        layer = cls(
+            w1=values.get("lokr_w1", None),
+            w1_a=values.get("lokr_w1_a", None),
+            w1_b=values.get("lokr_w1_b", None),
+            w2=values.get("lokr_w2", None),
+            w2_a=values.get("lokr_w2_a", None),
+            w2_b=values.get("lokr_w2_b", None),
+            t2=values.get("lokr_t2", None),
+            alpha=alpha,
+            bias=bias,
+        )
+
+        cls.warn_on_unhandled_keys(
+            values,
+            {
+                # Default keys.
+                "alpha",
+                "bias_indices",
+                "bias_values",
+                "bias_size",
+                # Layer-specific keys.
+                "lokr_w1",
+                "lokr_w1_a",
+                "lokr_w1_b",
+                "lokr_w2",
+                "lokr_w2_a",
+                "lokr_w2_b",
+                "lokr_t2",
+            },
+        )
+
+        return layer
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        w1 = 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()
+        weight = torch.kron(w1, w2)
+        return weight

invokeai/backend/lora/layers/lora_layer.py

@@ -0,0 +1,69 @@
+from typing import Dict, Optional
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class LoRALayer(LoRALayerBase):
+    def __init__(
+        self,
+        up: torch.Tensor,
+        mid: Optional[torch.Tensor],
+        down: torch.Tensor,
+        alpha: float | None,
+        bias: Optional[torch.Tensor],
+    ):
+        super().__init__(alpha, bias)
+        self.up = torch.nn.Parameter(up)
+        self.mid = torch.nn.Parameter(mid) if mid is not None else None
+        self.down = torch.nn.Parameter(down)
+        self.bias = torch.nn.Parameter(bias) if bias is not None else None
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        alpha = cls._parse_alpha(values.get("alpha", None))
+        bias = cls._parse_bias(
+            values.get("bias_indices", None), values.get("bias_values", None), values.get("bias_size", None)
+        )
+
+        layer = cls(
+            up=values["lora_up.weight"],
+            down=values["lora_down.weight"],
+            mid=values.get("lora_mid.weight", None),
+            alpha=alpha,
+            bias=bias,
+        )
+
+        cls.warn_on_unhandled_keys(
+            values=values,
+            handled_keys={
+                # Default keys.
+                "alpha",
+                "bias_indices",
+                "bias_values",
+                "bias_size",
+                # Layer-specific keys.
+                "lora_up.weight",
+                "lora_down.weight",
+                "lora_mid.weight",
+            },
+        )
+
+        return layer
+
+    def rank(self) -> int:
+        return self.down.shape[0]
+
+    def get_weight(self, orig_weight: 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

invokeai/backend/lora/layers/lora_layer_base.py

@@ -0,0 +1,68 @@
+from typing import Dict, Optional, Set
+
+import torch
+
+import invokeai.backend.util.logging as logger
+
+
+class LoRALayerBase(torch.nn.Module):
+    """Base class for all LoRA-like patching layers."""
+
+    def __init__(self, alpha: float | None, bias: torch.Tensor | None):
+        super().__init__()
+        self._alpha = alpha
+        self.bias = torch.nn.Parameter(bias) if bias is not None else None
+
+    @classmethod
+    def _parse_bias(
+        cls, bias_indices: torch.Tensor | None, bias_values: torch.Tensor | None, bias_size: torch.Tensor | None
+    ) -> torch.Tensor | None:
+        assert (bias_indices is None) == (bias_values is None) == (bias_size is None)
+
+        bias = None
+        if bias_indices is not None:
+            bias = torch.sparse_coo_tensor(bias_indices, bias_values, tuple(bias_size))
+        return bias
+
+    @classmethod
+    def _parse_alpha(
+        cls,
+        alpha: torch.Tensor | None,
+    ) -> float | None:
+        return alpha.item() if alpha is not None else None
+
+    def rank(self) -> int | None:
+        raise NotImplementedError()
+
+    def scale(self) -> float:
+        if self._alpha is None or self.rank() is None:
+            return 1.0
+        return self._alpha / self.rank()
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError()
+
+    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
+        return self.bias
+
+    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
+        params = {"weight": self.get_weight(orig_module.weight)}
+        bias = self.get_bias(orig_module.bias)
+        if bias is not None:
+            params["bias"] = bias
+        return params
+
+    @classmethod
+    def warn_on_unhandled_keys(cls, values: Dict[str, torch.Tensor], handled_keys: Set[str]):
+        """Log a warning if values contains unhandled keys."""
+        unknown_keys = set(values.keys()) - handled_keys
+        if unknown_keys:
+            logger.warning(
+                f"Unexpected keys found in LoRA/LyCORIS layer, model might work incorrectly! Unexpected keys: {unknown_keys}"
+            )
+
+    def calc_size(self) -> int:
+        # HACK(ryand): Fix this issue with circular imports.
+        from invokeai.backend.model_manager.load.model_util import calc_module_size
+
+        return calc_module_size(self)

invokeai/backend/lora/layers/norm_layer.py

@@ -0,0 +1,26 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+
+
+class NormLayer(LoRALayerBase):
+    def __init__(self, weight: torch.Tensor, bias: torch.Tensor | None):
+        super().__init__(alpha=None, bias=bias)
+        self.weight = torch.nn.Parameter(weight)
+
+    @classmethod
+    def from_state_dict_values(
+        cls,
+        values: Dict[str, torch.Tensor],
+    ):
+        layer = cls(weight=values["w_norm"], bias=values.get("b_norm", None))
+        cls.warn_on_unhandled_keys(values, {"w_norm", "b_norm"})
+        return layer
+
+    def rank(self) -> int | None:
+        return None
+
+    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
+        return self.weight

invokeai/backend/lora/layers/utils.py

@@ -0,0 +1,33 @@
+from typing import Dict
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.full_layer import FullLayer
+from invokeai.backend.lora.layers.ia3_layer import IA3Layer
+from invokeai.backend.lora.layers.loha_layer import LoHALayer
+from invokeai.backend.lora.layers.lokr_layer import LoKRLayer
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+from invokeai.backend.lora.layers.norm_layer import NormLayer
+
+
+def any_lora_layer_from_state_dict(state_dict: Dict[str, torch.Tensor]) -> AnyLoRALayer:
+    # Detect layers according to LyCORIS detection logic(`weight_list_det`)
+    # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
+
+    if "lora_up.weight" in state_dict:
+        # LoRA a.k.a LoCon
+        return LoRALayer.from_state_dict_values(state_dict)
+    elif "hada_w1_a" in state_dict:
+        return LoHALayer.from_state_dict_values(state_dict)
+    elif "lokr_w1" in state_dict or "lokr_w1_a" in state_dict:
+        return LoKRLayer.from_state_dict_values(state_dict)
+    elif "diff" in state_dict:
+        # Full a.k.a Diff
+        return FullLayer.from_state_dict_values(state_dict)
+    elif "on_input" in state_dict:
+        return IA3Layer.from_state_dict_values(state_dict)
+    elif "w_norm" in state_dict:
+        return NormLayer.from_state_dict_values(state_dict)
+    else:
+        raise ValueError(f"Unsupported lora format: {state_dict.keys()}")

invokeai/backend/lora/lora_model_raw.py

@@ -0,0 +1,22 @@
+# Copyright (c) 2024 The InvokeAI Development team
+from typing import Dict, Optional
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.raw_model import RawModel
+
+
+class LoRAModelRaw(RawModel):  # (torch.nn.Module):
+    def __init__(self, layers: Dict[str, AnyLoRALayer]):
+        self.layers = layers
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        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

invokeai/backend/lora/lora_patcher.py

@@ -0,0 +1,274 @@
+from contextlib import contextmanager
+from typing import Dict, Iterable, Optional, Tuple
+
+import torch
+
+from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.sidecar_layers.concatenated_lora.concatenated_lora_linear_sidecar_layer import (
+    ConcatenatedLoRALinearSidecarLayer,
+)
+from invokeai.backend.lora.sidecar_layers.lora.lora_conv_sidecar_layer import (
+    LoRAConv1dSidecarLayer,
+    LoRAConv2dSidecarLayer,
+    LoRAConv3dSidecarLayer,
+)
+from invokeai.backend.lora.sidecar_layers.lora.lora_linear_sidecar_layer import LoRALinearSidecarLayer
+from invokeai.backend.lora.sidecar_layers.lora_sidecar_module import LoRASidecarModule
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
+
+
+class LoraPatcher:
+    @staticmethod
+    @torch.no_grad()
+    @contextmanager
+    def apply_lora_patches(
+        model: torch.nn.Module,
+        patches: Iterable[Tuple[LoRAModelRaw, float]],
+        prefix: str,
+        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
+    ):
+        """Apply one or more LoRA patches to a model within a context manager.
+
+        :param model: The model to patch.
+        :param loras: An iterator that returns tuples of LoRA patches and associated weights. An iterator is used so
+            that the LoRA patches do not need to be loaded into memory all at once.
+        :param prefix: The keys in the patches will be filtered to only include weights with this prefix.
+        :cached_weights: Read-only copy of the model's state dict in CPU, for efficient unpatching purposes.
+        """
+        original_weights = OriginalWeightsStorage(cached_weights)
+        try:
+            for patch, patch_weight in patches:
+                LoraPatcher.apply_lora_patch(
+                    model=model,
+                    prefix=prefix,
+                    patch=patch,
+                    patch_weight=patch_weight,
+                    original_weights=original_weights,
+                )
+                del patch
+
+            yield
+        finally:
+            for param_key, weight in original_weights.get_changed_weights():
+                model.get_parameter(param_key).copy_(weight)
+
+    @staticmethod
+    @torch.no_grad()
+    def apply_lora_patch(
+        model: torch.nn.Module,
+        prefix: str,
+        patch: LoRAModelRaw,
+        patch_weight: float,
+        original_weights: OriginalWeightsStorage,
+    ):
+        """
+        Apply a single LoRA patch to a model.
+        :param model: The model to patch.
+        :param patch: LoRA model to patch in.
+        :param patch_weight: LoRA patch weight.
+        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
+        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
+        """
+
+        if patch_weight == 0:
+            return
+
+        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
+        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
+        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
+        # without searching, but some legacy code still uses flattened keys.
+        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
+
+        prefix_len = len(prefix)
+
+        for layer_key, layer in patch.layers.items():
+            if not layer_key.startswith(prefix):
+                continue
+
+            module_key, module = LoraPatcher._get_submodule(
+                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
+            )
+
+            # All of the LoRA weight calculations will be done on the same device as the module weight.
+            # (Performance will be best if this is a CUDA device.)
+            device = module.weight.device
+            dtype = module.weight.dtype
+
+            layer_scale = layer.scale()
+
+            # We intentionally move to the target device first, then cast. Experimentally, this was found to
+            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
+            # same thing in a single call to '.to(...)'.
+            layer.to(device=device)
+            layer.to(dtype=torch.float32)
+
+            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
+            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
+            for param_name, lora_param_weight in layer.get_parameters(module).items():
+                param_key = module_key + "." + param_name
+                module_param = module.get_parameter(param_name)
+
+                # Save original weight
+                original_weights.save(param_key, module_param)
+
+                if module_param.shape != lora_param_weight.shape:
+                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
+
+                lora_param_weight *= patch_weight * layer_scale
+                module_param += lora_param_weight.to(dtype=dtype)
+
+            layer.to(device=TorchDevice.CPU_DEVICE)
+
+    @staticmethod
+    @torch.no_grad()
+    @contextmanager
+    def apply_lora_sidecar_patches(
+        model: torch.nn.Module,
+        patches: Iterable[Tuple[LoRAModelRaw, float]],
+        prefix: str,
+    ):
+        original_modules: dict[str, torch.nn.Module] = {}
+        try:
+            for patch, patch_weight in patches:
+                LoraPatcher._apply_lora_sidecar_patch(
+                    model=model,
+                    prefix=prefix,
+                    patch=patch,
+                    patch_weight=patch_weight,
+                    original_modules=original_modules,
+                )
+
+            yield
+        finally:
+            # Restore original modules.
+            # Note: This logic assumes no nested modules in original_modules.
+            for module_key, orig_module in original_modules.items():
+                module_parent_key, module_name = module_key.rsplit(".", 1)
+                parent_module = model.get_submodule(module_parent_key)
+                LoraPatcher._set_submodule(parent_module, module_name, orig_module)
+
+    @staticmethod
+    def _apply_lora_sidecar_patch(
+        model: torch.nn.Module,
+        patch: LoRAModelRaw,
+        patch_weight: float,
+        prefix: str,
+        original_modules: dict[str, torch.nn.Module],
+    ):
+        if patch_weight == 0:
+            return
+
+        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
+        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
+        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
+        # without searching, but some legacy code still uses flattened keys.
+        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
+
+        prefix_len = len(prefix)
+
+        for layer_key, layer in patch.layers.items():
+            if not layer_key.startswith(prefix):
+                continue
+
+            module_key, module = LoraPatcher._get_submodule(
+                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
+            )
+
+            # Initialize the LoRA sidecar layer.
+            lora_sidecar_layer = LoraPatcher._initialize_lora_sidecar_layer(module, layer, patch_weight)
+
+            # Move the LoRA sidecar layer to the same device/dtype as the orig module.
+            # TODO(ryand): Experiment with moving to the device first, then casting. This could be faster.
+            # HACK(ryand): Set the dtype properly here. We want to set it to the *compute* dtype of the original module.
+            # In the case of quantized layers, this may be different than the weight dtype.
+            lora_sidecar_layer.to(device=module.weight.device, dtype=torch.bfloat16)
+
+            if module_key in original_modules:
+                # The module has already been patched with a LoRASidecarModule. Append to it.
+                assert isinstance(module, LoRASidecarModule)
+                module.add_lora_layer(lora_sidecar_layer)
+            else:
+                # The module has not yet been patched with a LoRASidecarModule. Create one.
+                lora_sidecar_module = LoRASidecarModule(module, [lora_sidecar_layer])
+                original_modules[module_key] = module
+                module_parent_key, module_name = module_key.rsplit(".", 1)
+                module_parent = model.get_submodule(module_parent_key)
+                LoraPatcher._set_submodule(module_parent, module_name, lora_sidecar_module)
+
+    @staticmethod
+    def _initialize_lora_sidecar_layer(orig_layer: torch.nn.Module, lora_layer: AnyLoRALayer, patch_weight: float):
+        if isinstance(orig_layer, torch.nn.Linear):
+            if isinstance(lora_layer, LoRALayer):
+                return LoRALinearSidecarLayer(lora_layer=lora_layer, weight=patch_weight)
+            elif isinstance(lora_layer, ConcatenatedLoRALayer):
+                return ConcatenatedLoRALinearSidecarLayer(concatenated_lora_layer=lora_layer, weight=patch_weight)
+            else:
+                raise ValueError(f"Unsupported Linear LoRA layer type: {type(lora_layer)}")
+        elif isinstance(orig_layer, torch.nn.Conv1d):
+            if isinstance(lora_layer, LoRALayer):
+                return LoRAConv1dSidecarLayer.from_layers(orig_layer, lora_layer, patch_weight)
+            else:
+                raise ValueError(f"Unsupported Conv1D LoRA layer type: {type(lora_layer)}")
+        elif isinstance(orig_layer, torch.nn.Conv2d):
+            if isinstance(lora_layer, LoRALayer):
+                return LoRAConv2dSidecarLayer.from_layers(orig_layer, lora_layer, patch_weight)
+            else:
+                raise ValueError(f"Unsupported Conv2D LoRA layer type: {type(lora_layer)}")
+        elif isinstance(orig_layer, torch.nn.Conv3d):
+            if isinstance(lora_layer, LoRALayer):
+                return LoRAConv3dSidecarLayer.from_layers(orig_layer, lora_layer, patch_weight)
+            else:
+                raise ValueError(f"Unsupported Conv3D LoRA layer type: {type(lora_layer)}")
+        else:
+            raise ValueError(f"Unsupported layer type: {type(orig_layer)}")
+
+    @staticmethod
+    def _set_submodule(parent_module: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
+        try:
+            submodule_index = int(module_name)
+            # If the module name is an integer, then we use the __setitem__ method to set the submodule.
+            parent_module[submodule_index] = submodule
+        except ValueError:
+            # If the module name is not an integer, then we use the setattr method to set the submodule.
+            setattr(parent_module, module_name, submodule)
+
+    @staticmethod
+    def _get_submodule(
+        model: torch.nn.Module, layer_key: str, layer_key_is_flattened: bool
+    ) -> tuple[str, torch.nn.Module]:
+        """Get the submodule corresponding to the given layer key.
+        :param model: The model to search.
+        :param layer_key: The layer key to search for.
+        :param layer_key_is_flattened: Whether the layer key is flattened. If flattened, then all '.' have been replaced
+            with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly without
+            searching, but some legacy code still uses flattened keys.
+        :return: A tuple containing the module key and the submodule.
+        """
+        if not layer_key_is_flattened:
+            return layer_key, model.get_submodule(layer_key)
+
+        # Handle flattened keys.
+        assert "." not in layer_key
+
+        module = model
+        module_key = ""
+        key_parts = layer_key.split("_")
+
+        submodule_name = key_parts.pop(0)
+
+        while len(key_parts) > 0:
+            try:
+                module = module.get_submodule(submodule_name)
+                module_key += "." + submodule_name
+                submodule_name = key_parts.pop(0)
+            except Exception:
+                submodule_name += "_" + key_parts.pop(0)
+
+        module = module.get_submodule(submodule_name)
+        module_key = (module_key + "." + submodule_name).lstrip(".")
+
+        return module_key, module

invokeai/backend/lora/sidecar_layers/concatenated_lora/concatenated_lora_linear_sidecar_layer.py

@@ -0,0 +1,32 @@
+import torch
+
+from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+
+
+class ConcatenatedLoRALinearSidecarLayer(torch.nn.Module):
+    def __init__(
+        self,
+        concatenated_lora_layer: ConcatenatedLoRALayer,
+        weight: float,
+    ):
+        super().__init__()
+
+        self._concatenated_lora_layer = concatenated_lora_layer
+        self._weight = weight
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        assert input.ndim == 3
+
+        x_chunks: list[torch.Tensor] = []
+        for lora_layer in self._concatenated_lora_layer.lora_layers:
+            x_chunk = torch.nn.functional.linear(input, lora_layer.down)
+            if lora_layer.mid is not None:
+                x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.mid)
+            x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.up, bias=lora_layer.bias)
+            x_chunk *= self._weight * lora_layer.scale()
+            x_chunks.append(x_chunk)
+
+        # TODO(ryand): Generalize to support concat_axis != 0.
+        assert self._concatenated_lora_layer.concat_axis == 0
+        x = torch.cat(x_chunks, dim=-1)
+        return x

invokeai/backend/lora/sidecar_layers/lora/lora_conv_sidecar_layer.py

@@ -0,0 +1,140 @@
+import typing
+
+import torch
+
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+
+
+class LoRAConvSidecarLayer(torch.nn.Module):
+    """An implementation of a conv LoRA layer based on the paper 'LoRA: Low-Rank Adaptation of Large Language Models'.
+    (https://arxiv.org/pdf/2106.09685.pdf)
+    """
+
+    @property
+    def conv_module(self) -> type[torch.nn.Conv1d | torch.nn.Conv2d | torch.nn.Conv3d]:
+        """The conv module to be set by child classes. One of torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d."""
+        raise NotImplementedError(
+            "LoRAConvLayer cannot be used directly. Use LoRAConv1dLayer, LoRAConv2dLayer, or LoRAConv3dLayer instead."
+        )
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        include_mid: bool,
+        rank: int,
+        alpha: float,
+        weight: float,
+        kernel_size: typing.Union[int, tuple[int]] = 1,
+        stride: typing.Union[int, tuple[int]] = 1,
+        padding: typing.Union[str, int, tuple[int]] = 0,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        super().__init__()
+
+        if rank > min(in_channels, out_channels):
+            raise ValueError(f"LoRA rank {rank} must be less than or equal to {min(in_channels, out_channels)}")
+
+        self._down = self.conv_module(
+            in_channels,
+            rank,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+            device=device,
+            dtype=dtype,
+        )
+        self._up = self.conv_module(rank, out_channels, kernel_size=1, stride=1, bias=False, device=device, dtype=dtype)
+        self._mid = None
+        if include_mid:
+            self._mid = self.conv_module(rank, rank, kernel_size=1, stride=1, bias=False, device=device, dtype=dtype)
+
+        # Register alpha as a buffer so that it is not trained, but still gets saved to the state_dict.
+        self.register_buffer("alpha", torch.tensor(alpha, device=device, dtype=dtype))
+
+        self._weight = weight
+        self._rank = rank
+
+    @classmethod
+    def from_layers(cls, orig_layer: torch.nn.Module, lora_layer: LoRALayer, weight: float):
+        # Initialize the LoRA layer.
+        with torch.device("meta"):
+            model = cls.from_orig_layer(
+                orig_layer,
+                include_mid=lora_layer.mid is not None,
+                rank=lora_layer.rank,
+                # TODO(ryand): Is this the right default in case of missing alpha?
+                alpha=lora_layer.alpha if lora_layer.alpha is not None else lora_layer.rank,
+                weight=weight,
+            )
+
+        # TODO(ryand): Are there cases where we need to reshape the weight matrices to match the conv layers?
+
+        # Inject weight into the LoRA layer.
+        assert model._up.weight.shape == lora_layer.up.shape
+        assert model._down.weight.shape == lora_layer.down.shape
+        model._up.weight.data = lora_layer.up
+        model._down.weight.data = lora_layer.down
+        if lora_layer.mid is not None:
+            assert model._mid is not None
+            assert model._mid.weight.shape == lora_layer.mid.shape
+            model._mid.weight.data = lora_layer.mid
+
+        return model
+
+    @classmethod
+    def from_orig_layer(
+        cls,
+        layer: torch.nn.Module,
+        include_mid: bool,
+        rank: int,
+        alpha: float,
+        weight: float,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        if not isinstance(layer, cls.conv_module):
+            raise TypeError(f"'{__class__.__name__}' cannot be initialized from a layer of type '{type(layer)}'.")
+
+        return cls(
+            in_channels=layer.in_channels,
+            out_channels=layer.out_channels,
+            include_mid=include_mid,
+            weight=weight,
+            kernel_size=layer.kernel_size,
+            stride=layer.stride,
+            padding=layer.padding,
+            rank=rank,
+            alpha=alpha,
+            device=layer.weight.device if device is None else device,
+            dtype=layer.weight.dtype if dtype is None else dtype,
+        )
+
+    def forward(self, x: torch.Tensor):
+        x = self._down(x)
+        if self._mid is not None:
+            x = self._mid(x)
+        x = self._up(x)
+
+        x *= self._weight * self.alpha / self._rank
+        return x
+
+
+class LoRAConv1dSidecarLayer(LoRAConvSidecarLayer):
+    @property
+    def conv_module(self):
+        return torch.nn.Conv1d
+
+
+class LoRAConv2dSidecarLayer(LoRAConvSidecarLayer):
+    @property
+    def conv_module(self):
+        return torch.nn.Conv2d
+
+
+class LoRAConv3dSidecarLayer(LoRAConvSidecarLayer):
+    @property
+    def conv_module(self):
+        return torch.nn.Conv3d

invokeai/backend/lora/sidecar_layers/lora/lora_linear_sidecar_layer.py

@@ -0,0 +1,23 @@
+import torch
+
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+
+
+class LoRALinearSidecarLayer(torch.nn.Module):
+    def __init__(
+        self,
+        lora_layer: LoRALayer,
+        weight: float,
+    ):
+        super().__init__()
+
+        self._lora_layer = lora_layer
+        self._weight = weight
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = torch.nn.functional.linear(x, self._lora_layer.down)
+        if self._lora_layer.mid is not None:
+            x = torch.nn.functional.linear(x, self._lora_layer.mid)
+        x = torch.nn.functional.linear(x, self._lora_layer.up, bias=self._lora_layer.bias)
+        x *= self._weight * self._lora_layer.scale()
+        return x

invokeai/backend/lora/sidecar_layers/lora_sidecar_module.py

@@ -0,0 +1,17 @@
+import torch
+
+
+class LoRASidecarModule(torch.nn.Module):
+    def __init__(self, orig_module: torch.nn.Module, lora_layers: list[torch.nn.Module]):
+        super().__init__()
+        self._orig_module = orig_module
+        self._lora_layers = lora_layers
+
+    def add_lora_layer(self, lora_layer: torch.nn.Module):
+        self._lora_layers.append(lora_layer)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        x = self._orig_module(input)
+        for lora_layer in self._lora_layers:
+            x += lora_layer(input)
+        return x

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -5,8 +5,18 @@ from logging import Logger
 from pathlib import Path
 from typing import Optional
 
+import torch
+from safetensors.torch import load_file
+
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
+    lora_model_from_flux_diffusers_state_dict,
+)
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
+    lora_model_from_flux_kohya_state_dict,
+)
+from invokeai.backend.lora.conversions.sd_lora_conversion_utils import lora_model_from_sd_state_dict
+from invokeai.backend.lora.conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -45,14 +55,39 @@ class LoRALoader(ModelLoader):
             raise ValueError("There are no submodels in a LoRA model.")
         model_path = Path(config.path)
         assert self._model_base is not None
-        model = LoRAModelRaw.from_checkpoint(
-            file_path=model_path,
-            dtype=self._torch_dtype,
-            base_model=self._model_base,
-        )
+
+        # Load the state dict from the model file.
+        if model_path.suffix == ".safetensors":
+            state_dict = load_file(model_path.absolute().as_posix(), device="cpu")
+        else:
+            state_dict = torch.load(model_path, map_location="cpu")
+
+        # Apply state_dict key conversions, if necessary.
+        if self._model_base == BaseModelType.StableDiffusionXL:
+            state_dict = convert_sdxl_keys_to_diffusers_format(state_dict)
+            model = lora_model_from_sd_state_dict(state_dict=state_dict)
+        elif self._model_base == BaseModelType.Flux:
+            if config.format == ModelFormat.Diffusers:
+                # HACK(ryand): We assume alpha=8 for diffusers PEFT format models. These models are typically
+                # distributed as a single file without the associated metadata containing the alpha value. We chose
+                # alpha=8, because this is the default value in the PEFT library:
+                # https://github.com/huggingface/peft/blob/7868d0372b86a6b9ac5f365b8f0eef2f2f5dedce/src/peft/tuners/lora/config.py#L169
+                # Other reasonable defaults for alpha could be 1.0 or the rank of the LoRA. If our assumption is wrong,
+                # the user will need to adjust the weight accordingly to account for the difference.
+                model = lora_model_from_flux_diffusers_state_dict(state_dict=state_dict, alpha=8)
+            elif config.format == ModelFormat.LyCORIS:
+                model = lora_model_from_flux_kohya_state_dict(state_dict=state_dict)
+            else:
+                raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
+        elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
+            # Currently, we don't apply any conversions for SD1 and SD2 LoRA models.
+            model = lora_model_from_sd_state_dict(state_dict=state_dict)
+        else:
+            raise ValueError(f"Unsupported LoRA base model: {self._model_base}")
+
+        model.to(dtype=self._torch_dtype)
         return model
 
-    # override
     def _get_model_path(self, config: AnyModelConfig) -> Path:
         # cheating a little - we remember this variable for using in the subsequent call to _load_model()
         self._model_base = config.base

invokeai/backend/model_manager/load/model_util.py

@@ -15,11 +15,12 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
 
 
 def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
@@ -83,10 +84,9 @@ def _calc_pipeline_by_data(pipeline: DiffusionPipeline) -> int:
 
 def calc_module_size(model: torch.nn.Module) -> int:
     """Calculate the size (in bytes) of a torch.nn.Module."""
-    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
-    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
-    mem: int = mem_params + mem_bufs  # in bytes
-    return mem
+    mem_params = sum([calc_tensor_size(param) for param in model.parameters()])
+    mem_bufs = sum([calc_tensor_size(buf) for buf in model.buffers()])
+    return mem_params + mem_bufs
 
 
 def _calc_onnx_model_by_data(model: IAIOnnxRuntimeModel) -> int:

invokeai/backend/model_manager/probe.py

@@ -10,6 +10,10 @@ from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
+from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_diffusers_format,
+)
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import is_state_dict_likely_in_flux_kohya_format
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -244,7 +248,9 @@ class ModelProbe(object):
                 return ModelType.VAE
             elif key.startswith(("lora_te_", "lora_unet_")):
                 return ModelType.LoRA
-            elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight")):
+            # "lora_A.weight" and "lora_B.weight" are associated with models in PEFT format. We don't support all PEFT
+            # LoRA models, but as of the time of writing, we support Diffusers FLUX PEFT LoRA models.
+            elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight", "lora_A.weight", "lora_B.weight")):
                 return ModelType.LoRA
             elif key.startswith(("controlnet", "control_model", "input_blocks")):
                 return ModelType.ControlNet
@@ -554,12 +560,21 @@ class LoRACheckpointProbe(CheckpointProbeBase):
     """Class for LoRA checkpoints."""
 
     def get_format(self) -> ModelFormat:
-        return ModelFormat("lycoris")
+        if is_state_dict_likely_in_flux_diffusers_format(self.checkpoint):
+            # TODO(ryand): This is an unusual case. In other places throughout the codebase, we treat
+            # ModelFormat.Diffusers as meaning that the model is in a directory. In this case, the model is a single
+            # file, but the weight keys are in the diffusers format.
+            return ModelFormat.Diffusers
+        return ModelFormat.LyCORIS
 
     def get_base_type(self) -> BaseModelType:
-        checkpoint = self.checkpoint
-        token_vector_length = lora_token_vector_length(checkpoint)
+        if is_state_dict_likely_in_flux_kohya_format(self.checkpoint) or is_state_dict_likely_in_flux_diffusers_format(
+            self.checkpoint
+        ):
+            return BaseModelType.Flux
 
+        # If we've gotten here, we assume that the model is a Stable Diffusion model.
+        token_vector_length = lora_token_vector_length(self.checkpoint)
         if token_vector_length == 768:
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:

invokeai/backend/model_patcher.py

@@ -5,32 +5,18 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
-from diffusers import OnnxRuntimeModel, UNet2DConditionModel
+from diffusers import UNet2DConditionModel
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import AnyModel
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
-from invokeai.backend.stable_diffusion.extensions.lora import LoRAExt
 from invokeai.backend.textual_inversion import TextualInversionManager, TextualInversionModelRaw
-from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
-
-"""
-loras = [
-    (lora_model1, 0.7),
-    (lora_model2, 0.4),
-]
-with LoRAHelper.apply_lora_unet(unet, loras):
-    # unet with applied loras
-# unmodified unet
-
-"""
 
 
 class ModelPatcher:
@@ -54,95 +40,6 @@ class ModelPatcher:
         finally:
             unet.set_attn_processor(unet_orig_processors)
 
-    @staticmethod
-    def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
-        assert "." not in lora_key
-
-        if not lora_key.startswith(prefix):
-            raise Exception(f"lora_key with invalid prefix: {lora_key}, {prefix}")
-
-        module = model
-        module_key = ""
-        key_parts = lora_key[len(prefix) :].split("_")
-
-        submodule_name = key_parts.pop(0)
-
-        while len(key_parts) > 0:
-            try:
-                module = module.get_submodule(submodule_name)
-                module_key += "." + submodule_name
-                submodule_name = key_parts.pop(0)
-            except Exception:
-                submodule_name += "_" + key_parts.pop(0)
-
-        module = module.get_submodule(submodule_name)
-        module_key = (module_key + "." + submodule_name).lstrip(".")
-
-        return (module_key, module)
-
-    @classmethod
-    @contextmanager
-    def apply_lora_unet(
-        cls,
-        unet: UNet2DConditionModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ) -> Generator[None, None, None]:
-        with cls.apply_lora(
-            unet,
-            loras=loras,
-            prefix="lora_unet_",
-            cached_weights=cached_weights,
-        ):
-            yield
-
-    @classmethod
-    @contextmanager
-    def apply_lora_text_encoder(
-        cls,
-        text_encoder: CLIPTextModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ) -> Generator[None, None, None]:
-        with cls.apply_lora(text_encoder, loras=loras, prefix="lora_te_", cached_weights=cached_weights):
-            yield
-
-    @classmethod
-    @contextmanager
-    def apply_lora(
-        cls,
-        model: AnyModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ) -> Generator[None, None, None]:
-        """
-        Apply one or more LoRAs to a model.
-
-        :param model: The model to patch.
-        :param loras: An iterator that returns the LoRA to patch in and its patch weight.
-        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
-        :cached_weights: Read-only copy of the model's state dict in CPU, for unpatching purposes.
-        """
-        original_weights = OriginalWeightsStorage(cached_weights)
-        try:
-            for lora_model, lora_weight in loras:
-                LoRAExt.patch_model(
-                    model=model,
-                    prefix=prefix,
-                    lora=lora_model,
-                    lora_weight=lora_weight,
-                    original_weights=original_weights,
-                )
-                del lora_model
-
-            yield
-
-        finally:
-            with torch.no_grad():
-                for param_key, weight in original_weights.get_changed_weights():
-                    model.get_parameter(param_key).copy_(weight)
-
     @classmethod
     @contextmanager
     def apply_ti(
@@ -282,26 +179,6 @@ class ModelPatcher:
 
 
 class ONNXModelPatcher:
-    @classmethod
-    @contextmanager
-    def apply_lora_unet(
-        cls,
-        unet: OnnxRuntimeModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
-        with cls.apply_lora(unet, loras, "lora_unet_"):
-            yield
-
-    @classmethod
-    @contextmanager
-    def apply_lora_text_encoder(
-        cls,
-        text_encoder: OnnxRuntimeModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
-        with cls.apply_lora(text_encoder, loras, "lora_te_"):
-            yield
-
     # based on
     # https://github.com/ssube/onnx-web/blob/ca2e436f0623e18b4cfe8a0363fcfcf10508acf7/api/onnx_web/convert/diffusion/lora.py#L323
     @classmethod

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -1,18 +1,17 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING
 
-import torch
 from diffusers import UNet2DConditionModel
 
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
-from invokeai.backend.util.devices import TorchDevice
 
 if TYPE_CHECKING:
     from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.shared.invocation_context import InvocationContext
-    from invokeai.backend.lora import LoRAModelRaw
     from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
@@ -31,107 +30,14 @@ class LoRAExt(ExtensionBase):
     @contextmanager
     def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         lora_model = self._node_context.models.load(self._model_id).model
-        self.patch_model(
+        assert isinstance(lora_model, LoRAModelRaw)
+        LoraPatcher.apply_lora_patch(
             model=unet,
             prefix="lora_unet_",
-            lora=lora_model,
-            lora_weight=self._weight,
+            patch=lora_model,
+            patch_weight=self._weight,
             original_weights=original_weights,
         )
         del lora_model
 
         yield
-
-    @classmethod
-    @torch.no_grad()
-    def patch_model(
-        cls,
-        model: torch.nn.Module,
-        prefix: str,
-        lora: LoRAModelRaw,
-        lora_weight: float,
-        original_weights: OriginalWeightsStorage,
-    ):
-        """
-        Apply one or more LoRAs to a model.
-        :param model: The model to patch.
-        :param lora: LoRA model to patch in.
-        :param lora_weight: LoRA patch weight.
-        :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
-        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
-        """
-
-        if lora_weight == 0:
-            return
-
-        # assert lora.device.type == "cpu"
-        for layer_key, layer in lora.layers.items():
-            if not layer_key.startswith(prefix):
-                continue
-
-            # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
-            # should be improved in the following ways:
-            # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a
-            #    LoRA model is applied.
-            # 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the
-            #    intricacies of Stable Diffusion key resolution. It should just expect the input LoRA
-            #    weights to have valid keys.
-            assert isinstance(model, torch.nn.Module)
-            module_key, module = cls._resolve_lora_key(model, layer_key, prefix)
-
-            # All of the LoRA weight calculations will be done on the same device as the module weight.
-            # (Performance will be best if this is a CUDA device.)
-            device = module.weight.device
-            dtype = module.weight.dtype
-
-            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-
-            # We intentionally move to the target device first, then cast. Experimentally, this was found to
-            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-            # same thing in a single call to '.to(...)'.
-            layer.to(device=device)
-            layer.to(dtype=torch.float32)
-
-            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-            for param_name, lora_param_weight in layer.get_parameters(module).items():
-                param_key = module_key + "." + param_name
-                module_param = module.get_parameter(param_name)
-
-                # save original weight
-                original_weights.save(param_key, module_param)
-
-                if module_param.shape != lora_param_weight.shape:
-                    # TODO: debug on lycoris
-                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
-
-                lora_param_weight *= lora_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
-
-            layer.to(device=TorchDevice.CPU_DEVICE)
-
-    @staticmethod
-    def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
-        assert "." not in lora_key
-
-        if not lora_key.startswith(prefix):
-            raise Exception(f"lora_key with invalid prefix: {lora_key}, {prefix}")
-
-        module = model
-        module_key = ""
-        key_parts = lora_key[len(prefix) :].split("_")
-
-        submodule_name = key_parts.pop(0)
-
-        while len(key_parts) > 0:
-            try:
-                module = module.get_submodule(submodule_name)
-                module_key += "." + submodule_name
-                submodule_name = key_parts.pop(0)
-            except Exception:
-                submodule_name += "_" + key_parts.pop(0)
-
-        module = module.get_submodule(submodule_name)
-        module_key = (module_key + "." + submodule_name).lstrip(".")
-
-        return (module_key, module)

invokeai/backend/textual_inversion.py

@@ -10,6 +10,7 @@ from transformers import CLIPTokenizer
 from typing_extensions import Self
 
 from invokeai.backend.raw_model import RawModel
+from invokeai.backend.util.calc_tensor_size import calc_tensors_size
 
 
 class TextualInversionModelRaw(RawModel):
@@ -74,11 +75,7 @@ class TextualInversionModelRaw(RawModel):
 
     def calc_size(self) -> int:
         """Get the size of this model in bytes."""
-        embedding_size = self.embedding.element_size() * self.embedding.nelement()
-        embedding_2_size = 0
-        if self.embedding_2 is not None:
-            embedding_2_size = self.embedding_2.element_size() * self.embedding_2.nelement()
-        return embedding_size + embedding_2_size
+        return calc_tensors_size([self.embedding, self.embedding_2])
 
 
 class TextualInversionManager(BaseTextualInversionManager):

invokeai/backend/util/calc_tensor_size.py

@@ -0,0 +1,11 @@
+import torch
+
+
+def calc_tensor_size(t: torch.Tensor) -> int:
+    """Calculate the size of a tensor in bytes."""
+    return t.nelement() * t.element_size()
+
+
+def calc_tensors_size(tensors: list[torch.Tensor | None]) -> int:
+    """Calculate the size of a list of tensors in bytes."""
+    return sum(calc_tensor_size(t) for t in tensors if t is not None)

tests/backend/lora/conversions/lora_state_dicts/flux_lora_diffusers_format.py

@@ -0,0 +1,993 @@
+# A sample state dict in the Diffusers FLUX LoRA format.
+# These keys are based on the LoRA model here:
+# https://civitai.com/models/200255/hands-xl-sd-15-flux1-dev?modelVersionId=781855
+state_dict_keys = [
+    "transformer.single_transformer_blocks.0.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.0.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.0.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.0.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.0.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.0.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.0.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.0.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.0.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.0.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.0.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.1.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.1.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.1.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.1.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.1.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.1.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.1.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.1.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.1.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.1.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.1.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.1.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.10.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.10.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.10.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.10.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.10.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.10.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.10.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.10.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.10.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.10.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.10.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.10.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.11.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.11.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.11.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.11.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.11.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.11.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.11.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.11.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.11.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.11.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.11.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.11.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.12.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.12.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.12.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.12.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.12.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.12.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.12.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.12.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.12.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.12.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.12.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.12.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.13.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.13.attn.to_k.lora_B.weight",
+    "transformer.single_transformer_blocks.13.attn.to_q.lora_A.weight",
+    "transformer.single_transformer_blocks.13.attn.to_q.lora_B.weight",
+    "transformer.single_transformer_blocks.13.attn.to_v.lora_A.weight",
+    "transformer.single_transformer_blocks.13.attn.to_v.lora_B.weight",
+    "transformer.single_transformer_blocks.13.norm.linear.lora_A.weight",
+    "transformer.single_transformer_blocks.13.norm.linear.lora_B.weight",
+    "transformer.single_transformer_blocks.13.proj_mlp.lora_A.weight",
+    "transformer.single_transformer_blocks.13.proj_mlp.lora_B.weight",
+    "transformer.single_transformer_blocks.13.proj_out.lora_A.weight",
+    "transformer.single_transformer_blocks.13.proj_out.lora_B.weight",
+    "transformer.single_transformer_blocks.14.attn.to_k.lora_A.weight",
+    "transformer.single_transformer_blocks.14.attn.to_k.lora_B.weight",
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+    "transformer.transformer_blocks.9.norm1_context.linear.lora_B.weight",
+]

tests/backend/lora/conversions/lora_state_dicts/flux_lora_kohya_format.py

@@ -0,0 +1,917 @@
+# A sample state dict in the Kohya FLUX LoRA format.
+# These keys are based on the LoRA model here:
+# https://civitai.com/models/159333/pokemon-trainer-sprite-pixelart?modelVersionId=779247
+state_dict_keys = [
+    "lora_unet_double_blocks_0_img_attn_proj.alpha",
+    "lora_unet_double_blocks_0_img_attn_proj.lora_down.weight",
+    "lora_unet_double_blocks_0_img_attn_proj.lora_up.weight",
+    "lora_unet_double_blocks_0_img_attn_qkv.alpha",
+    "lora_unet_double_blocks_0_img_attn_qkv.lora_down.weight",
+    "lora_unet_double_blocks_0_img_attn_qkv.lora_up.weight",
+    "lora_unet_double_blocks_0_img_mlp_0.alpha",
+    "lora_unet_double_blocks_0_img_mlp_0.lora_down.weight",
+    "lora_unet_double_blocks_0_img_mlp_0.lora_up.weight",
+    "lora_unet_double_blocks_0_img_mlp_2.alpha",
+    "lora_unet_double_blocks_0_img_mlp_2.lora_down.weight",
+    "lora_unet_double_blocks_0_img_mlp_2.lora_up.weight",
+    "lora_unet_double_blocks_0_img_mod_lin.alpha",
+    "lora_unet_double_blocks_0_img_mod_lin.lora_down.weight",
+    "lora_unet_double_blocks_0_img_mod_lin.lora_up.weight",
+    "lora_unet_double_blocks_0_txt_attn_proj.alpha",
+    "lora_unet_double_blocks_0_txt_attn_proj.lora_down.weight",
+    "lora_unet_double_blocks_0_txt_attn_proj.lora_up.weight",
+    "lora_unet_double_blocks_0_txt_attn_qkv.alpha",
+    "lora_unet_double_blocks_0_txt_attn_qkv.lora_down.weight",
+    "lora_unet_double_blocks_0_txt_attn_qkv.lora_up.weight",
+    "lora_unet_double_blocks_0_txt_mlp_0.alpha",
+    "lora_unet_double_blocks_0_txt_mlp_0.lora_down.weight",
+    "lora_unet_double_blocks_0_txt_mlp_0.lora_up.weight",
+    "lora_unet_double_blocks_0_txt_mlp_2.alpha",
+    "lora_unet_double_blocks_0_txt_mlp_2.lora_down.weight",
+    "lora_unet_double_blocks_0_txt_mlp_2.lora_up.weight",
+    "lora_unet_double_blocks_0_txt_mod_lin.alpha",
+    "lora_unet_double_blocks_0_txt_mod_lin.lora_down.weight",
+    "lora_unet_double_blocks_0_txt_mod_lin.lora_up.weight",
+    "lora_unet_double_blocks_10_img_attn_proj.alpha",
+    "lora_unet_double_blocks_10_img_attn_proj.lora_down.weight",
+    "lora_unet_double_blocks_10_img_attn_proj.lora_up.weight",
+    "lora_unet_double_blocks_10_img_attn_qkv.alpha",
+    "lora_unet_double_blocks_10_img_attn_qkv.lora_down.weight",
+    "lora_unet_double_blocks_10_img_attn_qkv.lora_up.weight",
+    "lora_unet_double_blocks_10_img_mlp_0.alpha",
+    "lora_unet_double_blocks_10_img_mlp_0.lora_down.weight",
+    "lora_unet_double_blocks_10_img_mlp_0.lora_up.weight",
+    "lora_unet_double_blocks_10_img_mlp_2.alpha",
+    "lora_unet_double_blocks_10_img_mlp_2.lora_down.weight",
+    "lora_unet_double_blocks_10_img_mlp_2.lora_up.weight",
+    "lora_unet_double_blocks_10_img_mod_lin.alpha",
+    "lora_unet_double_blocks_10_img_mod_lin.lora_down.weight",
+    "lora_unet_double_blocks_10_img_mod_lin.lora_up.weight",
+    "lora_unet_double_blocks_10_txt_attn_proj.alpha",
+    "lora_unet_double_blocks_10_txt_attn_proj.lora_down.weight",
+    "lora_unet_double_blocks_10_txt_attn_proj.lora_up.weight",
+    "lora_unet_double_blocks_10_txt_attn_qkv.alpha",
+    "lora_unet_double_blocks_10_txt_attn_qkv.lora_down.weight",
+    "lora_unet_double_blocks_10_txt_attn_qkv.lora_up.weight",
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+    "lora_unet_single_blocks_13_modulation_lin.alpha",
+    "lora_unet_single_blocks_13_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_13_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_14_linear1.alpha",
+    "lora_unet_single_blocks_14_linear1.lora_down.weight",
+    "lora_unet_single_blocks_14_linear1.lora_up.weight",
+    "lora_unet_single_blocks_14_linear2.alpha",
+    "lora_unet_single_blocks_14_linear2.lora_down.weight",
+    "lora_unet_single_blocks_14_linear2.lora_up.weight",
+    "lora_unet_single_blocks_14_modulation_lin.alpha",
+    "lora_unet_single_blocks_14_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_14_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_15_linear1.alpha",
+    "lora_unet_single_blocks_15_linear1.lora_down.weight",
+    "lora_unet_single_blocks_15_linear1.lora_up.weight",
+    "lora_unet_single_blocks_15_linear2.alpha",
+    "lora_unet_single_blocks_15_linear2.lora_down.weight",
+    "lora_unet_single_blocks_15_linear2.lora_up.weight",
+    "lora_unet_single_blocks_15_modulation_lin.alpha",
+    "lora_unet_single_blocks_15_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_15_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_16_linear1.alpha",
+    "lora_unet_single_blocks_16_linear1.lora_down.weight",
+    "lora_unet_single_blocks_16_linear1.lora_up.weight",
+    "lora_unet_single_blocks_16_linear2.alpha",
+    "lora_unet_single_blocks_16_linear2.lora_down.weight",
+    "lora_unet_single_blocks_16_linear2.lora_up.weight",
+    "lora_unet_single_blocks_16_modulation_lin.alpha",
+    "lora_unet_single_blocks_16_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_16_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_17_linear1.alpha",
+    "lora_unet_single_blocks_17_linear1.lora_down.weight",
+    "lora_unet_single_blocks_17_linear1.lora_up.weight",
+    "lora_unet_single_blocks_17_linear2.alpha",
+    "lora_unet_single_blocks_17_linear2.lora_down.weight",
+    "lora_unet_single_blocks_17_linear2.lora_up.weight",
+    "lora_unet_single_blocks_17_modulation_lin.alpha",
+    "lora_unet_single_blocks_17_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_17_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_18_linear1.alpha",
+    "lora_unet_single_blocks_18_linear1.lora_down.weight",
+    "lora_unet_single_blocks_18_linear1.lora_up.weight",
+    "lora_unet_single_blocks_18_linear2.alpha",
+    "lora_unet_single_blocks_18_linear2.lora_down.weight",
+    "lora_unet_single_blocks_18_linear2.lora_up.weight",
+    "lora_unet_single_blocks_18_modulation_lin.alpha",
+    "lora_unet_single_blocks_18_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_18_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_19_linear1.alpha",
+    "lora_unet_single_blocks_19_linear1.lora_down.weight",
+    "lora_unet_single_blocks_19_linear1.lora_up.weight",
+    "lora_unet_single_blocks_19_linear2.alpha",
+    "lora_unet_single_blocks_19_linear2.lora_down.weight",
+    "lora_unet_single_blocks_19_linear2.lora_up.weight",
+    "lora_unet_single_blocks_19_modulation_lin.alpha",
+    "lora_unet_single_blocks_19_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_19_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_1_linear1.alpha",
+    "lora_unet_single_blocks_1_linear1.lora_down.weight",
+    "lora_unet_single_blocks_1_linear1.lora_up.weight",
+    "lora_unet_single_blocks_1_linear2.alpha",
+    "lora_unet_single_blocks_1_linear2.lora_down.weight",
+    "lora_unet_single_blocks_1_linear2.lora_up.weight",
+    "lora_unet_single_blocks_1_modulation_lin.alpha",
+    "lora_unet_single_blocks_1_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_1_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_20_linear1.alpha",
+    "lora_unet_single_blocks_20_linear1.lora_down.weight",
+    "lora_unet_single_blocks_20_linear1.lora_up.weight",
+    "lora_unet_single_blocks_20_linear2.alpha",
+    "lora_unet_single_blocks_20_linear2.lora_down.weight",
+    "lora_unet_single_blocks_20_linear2.lora_up.weight",
+    "lora_unet_single_blocks_20_modulation_lin.alpha",
+    "lora_unet_single_blocks_20_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_20_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_21_linear1.alpha",
+    "lora_unet_single_blocks_21_linear1.lora_down.weight",
+    "lora_unet_single_blocks_21_linear1.lora_up.weight",
+    "lora_unet_single_blocks_21_linear2.alpha",
+    "lora_unet_single_blocks_21_linear2.lora_down.weight",
+    "lora_unet_single_blocks_21_linear2.lora_up.weight",
+    "lora_unet_single_blocks_21_modulation_lin.alpha",
+    "lora_unet_single_blocks_21_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_21_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_22_linear1.alpha",
+    "lora_unet_single_blocks_22_linear1.lora_down.weight",
+    "lora_unet_single_blocks_22_linear1.lora_up.weight",
+    "lora_unet_single_blocks_22_linear2.alpha",
+    "lora_unet_single_blocks_22_linear2.lora_down.weight",
+    "lora_unet_single_blocks_22_linear2.lora_up.weight",
+    "lora_unet_single_blocks_22_modulation_lin.alpha",
+    "lora_unet_single_blocks_22_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_22_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_23_linear1.alpha",
+    "lora_unet_single_blocks_23_linear1.lora_down.weight",
+    "lora_unet_single_blocks_23_linear1.lora_up.weight",
+    "lora_unet_single_blocks_23_linear2.alpha",
+    "lora_unet_single_blocks_23_linear2.lora_down.weight",
+    "lora_unet_single_blocks_23_linear2.lora_up.weight",
+    "lora_unet_single_blocks_23_modulation_lin.alpha",
+    "lora_unet_single_blocks_23_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_23_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_24_linear1.alpha",
+    "lora_unet_single_blocks_24_linear1.lora_down.weight",
+    "lora_unet_single_blocks_24_linear1.lora_up.weight",
+    "lora_unet_single_blocks_24_linear2.alpha",
+    "lora_unet_single_blocks_24_linear2.lora_down.weight",
+    "lora_unet_single_blocks_24_linear2.lora_up.weight",
+    "lora_unet_single_blocks_24_modulation_lin.alpha",
+    "lora_unet_single_blocks_24_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_24_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_25_linear1.alpha",
+    "lora_unet_single_blocks_25_linear1.lora_down.weight",
+    "lora_unet_single_blocks_25_linear1.lora_up.weight",
+    "lora_unet_single_blocks_25_linear2.alpha",
+    "lora_unet_single_blocks_25_linear2.lora_down.weight",
+    "lora_unet_single_blocks_25_linear2.lora_up.weight",
+    "lora_unet_single_blocks_25_modulation_lin.alpha",
+    "lora_unet_single_blocks_25_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_25_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_26_linear1.alpha",
+    "lora_unet_single_blocks_26_linear1.lora_down.weight",
+    "lora_unet_single_blocks_26_linear1.lora_up.weight",
+    "lora_unet_single_blocks_26_linear2.alpha",
+    "lora_unet_single_blocks_26_linear2.lora_down.weight",
+    "lora_unet_single_blocks_26_linear2.lora_up.weight",
+    "lora_unet_single_blocks_26_modulation_lin.alpha",
+    "lora_unet_single_blocks_26_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_26_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_27_linear1.alpha",
+    "lora_unet_single_blocks_27_linear1.lora_down.weight",
+    "lora_unet_single_blocks_27_linear1.lora_up.weight",
+    "lora_unet_single_blocks_27_linear2.alpha",
+    "lora_unet_single_blocks_27_linear2.lora_down.weight",
+    "lora_unet_single_blocks_27_linear2.lora_up.weight",
+    "lora_unet_single_blocks_27_modulation_lin.alpha",
+    "lora_unet_single_blocks_27_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_27_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_28_linear1.alpha",
+    "lora_unet_single_blocks_28_linear1.lora_down.weight",
+    "lora_unet_single_blocks_28_linear1.lora_up.weight",
+    "lora_unet_single_blocks_28_linear2.alpha",
+    "lora_unet_single_blocks_28_linear2.lora_down.weight",
+    "lora_unet_single_blocks_28_linear2.lora_up.weight",
+    "lora_unet_single_blocks_28_modulation_lin.alpha",
+    "lora_unet_single_blocks_28_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_28_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_29_linear1.alpha",
+    "lora_unet_single_blocks_29_linear1.lora_down.weight",
+    "lora_unet_single_blocks_29_linear1.lora_up.weight",
+    "lora_unet_single_blocks_29_linear2.alpha",
+    "lora_unet_single_blocks_29_linear2.lora_down.weight",
+    "lora_unet_single_blocks_29_linear2.lora_up.weight",
+    "lora_unet_single_blocks_29_modulation_lin.alpha",
+    "lora_unet_single_blocks_29_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_29_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_2_linear1.alpha",
+    "lora_unet_single_blocks_2_linear1.lora_down.weight",
+    "lora_unet_single_blocks_2_linear1.lora_up.weight",
+    "lora_unet_single_blocks_2_linear2.alpha",
+    "lora_unet_single_blocks_2_linear2.lora_down.weight",
+    "lora_unet_single_blocks_2_linear2.lora_up.weight",
+    "lora_unet_single_blocks_2_modulation_lin.alpha",
+    "lora_unet_single_blocks_2_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_2_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_30_linear1.alpha",
+    "lora_unet_single_blocks_30_linear1.lora_down.weight",
+    "lora_unet_single_blocks_30_linear1.lora_up.weight",
+    "lora_unet_single_blocks_30_linear2.alpha",
+    "lora_unet_single_blocks_30_linear2.lora_down.weight",
+    "lora_unet_single_blocks_30_linear2.lora_up.weight",
+    "lora_unet_single_blocks_30_modulation_lin.alpha",
+    "lora_unet_single_blocks_30_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_30_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_31_linear1.alpha",
+    "lora_unet_single_blocks_31_linear1.lora_down.weight",
+    "lora_unet_single_blocks_31_linear1.lora_up.weight",
+    "lora_unet_single_blocks_31_linear2.alpha",
+    "lora_unet_single_blocks_31_linear2.lora_down.weight",
+    "lora_unet_single_blocks_31_linear2.lora_up.weight",
+    "lora_unet_single_blocks_31_modulation_lin.alpha",
+    "lora_unet_single_blocks_31_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_31_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_32_linear1.alpha",
+    "lora_unet_single_blocks_32_linear1.lora_down.weight",
+    "lora_unet_single_blocks_32_linear1.lora_up.weight",
+    "lora_unet_single_blocks_32_linear2.alpha",
+    "lora_unet_single_blocks_32_linear2.lora_down.weight",
+    "lora_unet_single_blocks_32_linear2.lora_up.weight",
+    "lora_unet_single_blocks_32_modulation_lin.alpha",
+    "lora_unet_single_blocks_32_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_32_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_33_linear1.alpha",
+    "lora_unet_single_blocks_33_linear1.lora_down.weight",
+    "lora_unet_single_blocks_33_linear1.lora_up.weight",
+    "lora_unet_single_blocks_33_linear2.alpha",
+    "lora_unet_single_blocks_33_linear2.lora_down.weight",
+    "lora_unet_single_blocks_33_linear2.lora_up.weight",
+    "lora_unet_single_blocks_33_modulation_lin.alpha",
+    "lora_unet_single_blocks_33_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_33_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_34_linear1.alpha",
+    "lora_unet_single_blocks_34_linear1.lora_down.weight",
+    "lora_unet_single_blocks_34_linear1.lora_up.weight",
+    "lora_unet_single_blocks_34_linear2.alpha",
+    "lora_unet_single_blocks_34_linear2.lora_down.weight",
+    "lora_unet_single_blocks_34_linear2.lora_up.weight",
+    "lora_unet_single_blocks_34_modulation_lin.alpha",
+    "lora_unet_single_blocks_34_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_34_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_35_linear1.alpha",
+    "lora_unet_single_blocks_35_linear1.lora_down.weight",
+    "lora_unet_single_blocks_35_linear1.lora_up.weight",
+    "lora_unet_single_blocks_35_linear2.alpha",
+    "lora_unet_single_blocks_35_linear2.lora_down.weight",
+    "lora_unet_single_blocks_35_linear2.lora_up.weight",
+    "lora_unet_single_blocks_35_modulation_lin.alpha",
+    "lora_unet_single_blocks_35_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_35_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_36_linear1.alpha",
+    "lora_unet_single_blocks_36_linear1.lora_down.weight",
+    "lora_unet_single_blocks_36_linear1.lora_up.weight",
+    "lora_unet_single_blocks_36_linear2.alpha",
+    "lora_unet_single_blocks_36_linear2.lora_down.weight",
+    "lora_unet_single_blocks_36_linear2.lora_up.weight",
+    "lora_unet_single_blocks_36_modulation_lin.alpha",
+    "lora_unet_single_blocks_36_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_36_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_37_linear1.alpha",
+    "lora_unet_single_blocks_37_linear1.lora_down.weight",
+    "lora_unet_single_blocks_37_linear1.lora_up.weight",
+    "lora_unet_single_blocks_37_linear2.alpha",
+    "lora_unet_single_blocks_37_linear2.lora_down.weight",
+    "lora_unet_single_blocks_37_linear2.lora_up.weight",
+    "lora_unet_single_blocks_37_modulation_lin.alpha",
+    "lora_unet_single_blocks_37_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_37_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_3_linear1.alpha",
+    "lora_unet_single_blocks_3_linear1.lora_down.weight",
+    "lora_unet_single_blocks_3_linear1.lora_up.weight",
+    "lora_unet_single_blocks_3_linear2.alpha",
+    "lora_unet_single_blocks_3_linear2.lora_down.weight",
+    "lora_unet_single_blocks_3_linear2.lora_up.weight",
+    "lora_unet_single_blocks_3_modulation_lin.alpha",
+    "lora_unet_single_blocks_3_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_3_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_4_linear1.alpha",
+    "lora_unet_single_blocks_4_linear1.lora_down.weight",
+    "lora_unet_single_blocks_4_linear1.lora_up.weight",
+    "lora_unet_single_blocks_4_linear2.alpha",
+    "lora_unet_single_blocks_4_linear2.lora_down.weight",
+    "lora_unet_single_blocks_4_linear2.lora_up.weight",
+    "lora_unet_single_blocks_4_modulation_lin.alpha",
+    "lora_unet_single_blocks_4_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_4_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_5_linear1.alpha",
+    "lora_unet_single_blocks_5_linear1.lora_down.weight",
+    "lora_unet_single_blocks_5_linear1.lora_up.weight",
+    "lora_unet_single_blocks_5_linear2.alpha",
+    "lora_unet_single_blocks_5_linear2.lora_down.weight",
+    "lora_unet_single_blocks_5_linear2.lora_up.weight",
+    "lora_unet_single_blocks_5_modulation_lin.alpha",
+    "lora_unet_single_blocks_5_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_5_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_6_linear1.alpha",
+    "lora_unet_single_blocks_6_linear1.lora_down.weight",
+    "lora_unet_single_blocks_6_linear1.lora_up.weight",
+    "lora_unet_single_blocks_6_linear2.alpha",
+    "lora_unet_single_blocks_6_linear2.lora_down.weight",
+    "lora_unet_single_blocks_6_linear2.lora_up.weight",
+    "lora_unet_single_blocks_6_modulation_lin.alpha",
+    "lora_unet_single_blocks_6_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_6_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_7_linear1.alpha",
+    "lora_unet_single_blocks_7_linear1.lora_down.weight",
+    "lora_unet_single_blocks_7_linear1.lora_up.weight",
+    "lora_unet_single_blocks_7_linear2.alpha",
+    "lora_unet_single_blocks_7_linear2.lora_down.weight",
+    "lora_unet_single_blocks_7_linear2.lora_up.weight",
+    "lora_unet_single_blocks_7_modulation_lin.alpha",
+    "lora_unet_single_blocks_7_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_7_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_8_linear1.alpha",
+    "lora_unet_single_blocks_8_linear1.lora_down.weight",
+    "lora_unet_single_blocks_8_linear1.lora_up.weight",
+    "lora_unet_single_blocks_8_linear2.alpha",
+    "lora_unet_single_blocks_8_linear2.lora_down.weight",
+    "lora_unet_single_blocks_8_linear2.lora_up.weight",
+    "lora_unet_single_blocks_8_modulation_lin.alpha",
+    "lora_unet_single_blocks_8_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_8_modulation_lin.lora_up.weight",
+    "lora_unet_single_blocks_9_linear1.alpha",
+    "lora_unet_single_blocks_9_linear1.lora_down.weight",
+    "lora_unet_single_blocks_9_linear1.lora_up.weight",
+    "lora_unet_single_blocks_9_linear2.alpha",
+    "lora_unet_single_blocks_9_linear2.lora_down.weight",
+    "lora_unet_single_blocks_9_linear2.lora_up.weight",
+    "lora_unet_single_blocks_9_modulation_lin.alpha",
+    "lora_unet_single_blocks_9_modulation_lin.lora_down.weight",
+    "lora_unet_single_blocks_9_modulation_lin.lora_up.weight",
+]

tests/backend/lora/conversions/lora_state_dicts/utils.py

@@ -0,0 +1,8 @@
+import torch
+
+
+def keys_to_mock_state_dict(keys: list[str]) -> dict[str, torch.Tensor]:
+    state_dict: dict[str, torch.Tensor] = {}
+    for k in keys:
+        state_dict[k] = torch.empty(1)
+    return state_dict

tests/backend/lora/conversions/test_flux_diffusers_lora_conversion_utils.py

@@ -0,0 +1,66 @@
+import pytest
+import torch
+
+from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_diffusers_format,
+    lora_model_from_flux_diffusers_state_dict,
+)
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
+    state_dict_keys as flux_diffusers_state_dict_keys,
+)
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_kohya_format import (
+    state_dict_keys as flux_kohya_state_dict_keys,
+)
+from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
+
+
+def test_is_state_dict_likely_in_flux_diffusers_format_true():
+    """Test that is_state_dict_likely_in_flux_diffusers_format() can identify a state dict in the Diffusers FLUX LoRA format."""
+    # Construct a state dict that is in the Diffusers FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+
+    assert is_state_dict_likely_in_flux_diffusers_format(state_dict)
+
+
+def test_is_state_dict_likely_in_flux_diffusers_format_false():
+    """Test that is_state_dict_likely_in_flux_diffusers_format() returns False for a state dict that is not in the Kohya
+    FLUX LoRA format.
+    """
+    # Construct a state dict that is not in the Kohya FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_kohya_state_dict_keys)
+
+    assert not is_state_dict_likely_in_flux_diffusers_format(state_dict)
+
+
+def test_lora_model_from_flux_diffusers_state_dict():
+    """Test that lora_model_from_flux_diffusers_state_dict() can load a state dict in the Diffusers FLUX LoRA format."""
+    # Construct a state dict that is in the Diffusers FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+    # Load the state dict into a LoRAModelRaw object.
+    model = lora_model_from_flux_diffusers_state_dict(state_dict)
+
+    # Check that the model has the correct number of LoRA layers.
+    expected_lora_layers: set[str] = set()
+    for k in flux_diffusers_state_dict_keys:
+        k = k.replace("lora_A.weight", "")
+        k = k.replace("lora_B.weight", "")
+        expected_lora_layers.add(k)
+    # Drop the K/V/proj_mlp weights because these are all concatenated into a single layer in the BFL format (we keep
+    # the Q weights so that we count these layers once).
+    concatenated_weights = ["to_k", "to_v", "proj_mlp", "add_k_proj", "add_v_proj"]
+    expected_lora_layers = {k for k in expected_lora_layers if not any(w in k for w in concatenated_weights)}
+    assert len(model.layers) == len(expected_lora_layers)
+
+
+def test_lora_model_from_flux_diffusers_state_dict_extra_keys_error():
+    """Test that lora_model_from_flux_diffusers_state_dict() raises an error if the input state_dict contains unexpected
+    keys that we don't handle.
+    """
+    # Construct a state dict that is in the Diffusers FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+    # Add an unexpected key.
+    state_dict["transformer.single_transformer_blocks.0.unexpected_key.lora_A.weight"] = torch.empty(1)
+
+    # Check that an error is raised.
+    with pytest.raises(AssertionError):
+        lora_model_from_flux_diffusers_state_dict(state_dict)

tests/backend/lora/conversions/test_flux_kohya_lora_conversion_utils.py

@@ -0,0 +1,98 @@
+import pytest
+import torch
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.util import params
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
+    convert_flux_kohya_state_dict_to_invoke_format,
+    is_state_dict_likely_in_flux_kohya_format,
+    lora_model_from_flux_kohya_state_dict,
+)
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
+    state_dict_keys as flux_diffusers_state_dict_keys,
+)
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_kohya_format import (
+    state_dict_keys as flux_kohya_state_dict_keys,
+)
+from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
+
+
+def test_is_state_dict_likely_in_flux_kohya_format_true():
+    """Test that is_state_dict_likely_in_flux_kohya_format() can identify a state dict in the Kohya FLUX LoRA format."""
+    # Construct a state dict that is in the Kohya FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_kohya_state_dict_keys)
+
+    assert is_state_dict_likely_in_flux_kohya_format(state_dict)
+
+
+def test_is_state_dict_likely_in_flux_kohya_format_false():
+    """Test that is_state_dict_likely_in_flux_kohya_format() returns False for a state dict that is in the Diffusers
+    FLUX LoRA format.
+    """
+    state_dict = keys_to_mock_state_dict(flux_diffusers_state_dict_keys)
+    assert not is_state_dict_likely_in_flux_kohya_format(state_dict)
+
+
+def test_convert_flux_kohya_state_dict_to_invoke_format():
+    # Construct state_dict from state_dict_keys.
+    state_dict = keys_to_mock_state_dict(flux_kohya_state_dict_keys)
+
+    converted_state_dict = convert_flux_kohya_state_dict_to_invoke_format(state_dict)
+
+    # Extract the prefixes from the converted state dict (i.e. without the .lora_up.weight, .lora_down.weight, and
+    # .alpha suffixes).
+    converted_key_prefixes: list[str] = []
+    for k in converted_state_dict.keys():
+        k = k.replace(".lora_up.weight", "")
+        k = k.replace(".lora_down.weight", "")
+        k = k.replace(".alpha", "")
+        converted_key_prefixes.append(k)
+
+    # Initialize a FLUX model on the meta device.
+    with torch.device("meta"):
+        model = Flux(params["flux-dev"])
+    model_keys = set(model.state_dict().keys())
+
+    # Assert that the converted state dict matches the keys in the actual model.
+    for converted_key_prefix in converted_key_prefixes:
+        found_match = False
+        for model_key in model_keys:
+            if model_key.startswith(converted_key_prefix):
+                found_match = True
+                break
+        if not found_match:
+            raise AssertionError(f"Could not find a match for the converted key prefix: {converted_key_prefix}")
+
+
+def test_convert_flux_kohya_state_dict_to_invoke_format_error():
+    """Test that an error is raised by convert_flux_kohya_state_dict_to_invoke_format() if the input state_dict contains
+    unexpected keys.
+    """
+    state_dict = {
+        "unexpected_key.lora_up.weight": torch.empty(1),
+    }
+
+    with pytest.raises(ValueError):
+        convert_flux_kohya_state_dict_to_invoke_format(state_dict)
+
+
+def test_lora_model_from_flux_kohya_state_dict():
+    """Test that a LoRAModelRaw can be created from a state dict in the Kohya FLUX LoRA format."""
+    # Construct a state dict that is in the Kohya FLUX LoRA format.
+    state_dict = keys_to_mock_state_dict(flux_kohya_state_dict_keys)
+
+    lora_model = lora_model_from_flux_kohya_state_dict(state_dict)
+
+    # Prepare expected layer keys.
+    expected_layer_keys: set[str] = set()
+    for k in flux_kohya_state_dict_keys:
+        k = k.replace("lora_unet_", "")
+        k = k.replace(".lora_up.weight", "")
+        k = k.replace(".lora_down.weight", "")
+        k = k.replace(".alpha", "")
+        expected_layer_keys.add(k)
+
+    # Assert that the lora_model has the expected layers.
+    lora_model_keys = set(lora_model.layers.keys())
+    lora_model_keys = {k.replace(".", "_") for k in lora_model_keys}
+    assert lora_model_keys == expected_layer_keys

tests/backend/lora/test_lora_patcher.py

@@ -1,12 +1,9 @@
-# test that if the model's device changes while the lora is applied, the weights can still be restored
-
-# test that LoRA patching works on both CPU and CUDA
-
 import pytest
 import torch
 
-from invokeai.backend.lora import LoRALayer, LoRAModelRaw
-from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.lora.layers.lora_layer import LoRALayer
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 
 
 @pytest.mark.parametrize(
@@ -17,7 +14,7 @@ from invokeai.backend.model_patcher import ModelPatcher
     ],
 )
 @torch.no_grad()
-def test_apply_lora(device):
+def test_apply_lora(device: str):
     """Test the basic behavior of ModelPatcher.apply_lora(...). Check that patching and unpatching produce the correct
     result, and that model/LoRA tensors are moved between devices as expected.
     """
@@ -31,20 +28,19 @@ def test_apply_lora(device):
 
     lora_layers = {
         "linear_layer_1": LoRALayer(
-            layer_key="linear_layer_1",
             values={
                 "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
                 "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
             },
         )
     }
-    lora = LoRAModelRaw("lora_name", lora_layers)
+    lora = LoRAModelRaw(lora_layers)
 
     lora_weight = 0.5
     orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
     expected_patched_linear_weight = orig_linear_weight + (lora_dim * lora_weight)
 
-    with ModelPatcher.apply_lora(model, [(lora, lora_weight)], prefix=""):
+    with LoraPatcher.apply_lora_patches(model=model, patches=[(lora, lora_weight)], prefix=""):
         # After patching, all LoRA layer weights should have been moved back to the cpu.
         assert lora_layers["linear_layer_1"].up.device.type == "cpu"
         assert lora_layers["linear_layer_1"].down.device.type == "cpu"
@@ -75,18 +71,17 @@ def test_apply_lora_change_device():
 
     lora_layers = {
         "linear_layer_1": LoRALayer(
-            layer_key="linear_layer_1",
             values={
                 "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
                 "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
             },
         )
     }
-    lora = LoRAModelRaw("lora_name", lora_layers)
+    lora = LoRAModelRaw(lora_layers)
 
     orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
 
-    with ModelPatcher.apply_lora(model, [(lora, 0.5)], prefix=""):
+    with LoraPatcher.apply_lora_patches(model=model, patches=[(lora, 0.5)], prefix=""):
         # After patching, all LoRA layer weights should have been moved back to the cpu.
         assert lora_layers["linear_layer_1"].up.device.type == "cpu"
         assert lora_layers["linear_layer_1"].down.device.type == "cpu"