Add support for FLUX ControlNet models (XLabs and InstantX) (#7070)

## Summary

Add support for FLUX ControlNet models (XLabs and InstantX).

## QA Instructions

- [x] SD1 and SDXL ControlNets, since the ModelLoaderRegistry calls were
changed.
- [x] Single Xlabs controlnet
- [x] Single InstantX union controlnet
- [x] Single InstantX controlnet
- [x] Single Shakker Labs Union controlnet
- [x] Multiple controlnets
- [x] Weight, start, end params all work as expected
- [x] Can be used with image-to-image and inpainting.
- [x] Clear error message if no VAE is passed when using InstantX
controlnet.
- [x] Install InstantX ControlNet in diffusers format from HF repo
(`InstantX/FLUX.1-dev-Controlnet-Union`)
- [x] Test all FLUX ControlNet starter models
## Merge Plan

No special instructions.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_

invokeai/app/invocations/fields.py

@@ -192,6 +192,7 @@ class FieldDescriptions:
     freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
     freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
     freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
+    instantx_control_mode = "The control mode for InstantX ControlNet union models. Ignored for other ControlNet models. The standard mapping is: canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6). Negative values will be treated as 'None'."
 
 
 class ImageField(BaseModel):

invokeai/app/invocations/flux_controlnet.py

@@ -0,0 +1,99 @@
+from pydantic import BaseModel, Field, field_validator, model_validator
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
+
+
+class FluxControlNetField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
+    control_weight: float | list[float] = Field(default=1, description="The weight given to the ControlNet")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+    instantx_control_mode: int | None = Field(default=-1, description=FieldDescriptions.instantx_control_mode)
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self):
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+
+@invocation_output("flux_controlnet_output")
+class FluxControlNetOutput(BaseInvocationOutput):
+    """FLUX ControlNet info"""
+
+    control: FluxControlNetField = OutputField(description=FieldDescriptions.control)
+
+
+@invocation(
+    "flux_controlnet",
+    title="FLUX ControlNet",
+    tags=["controlnet", "flux"],
+    category="controlnet",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxControlNetInvocation(BaseInvocation):
+    """Collect FLUX ControlNet info to pass to other nodes."""
+
+    image: ImageField = InputField(description="The control image")
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+    )
+    control_weight: float | list[float] = InputField(
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
+    # Note: We default to -1 instead of None, because in the workflow editor UI None is not currently supported.
+    instantx_control_mode: int | None = InputField(default=-1, description=FieldDescriptions.instantx_control_mode)
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self):
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+    def invoke(self, context: InvocationContext) -> FluxControlNetOutput:
+        return FluxControlNetOutput(
+            control=FluxControlNetField(
+                image=self.image,
+                control_model=self.control_model,
+                control_weight=self.control_weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                resize_mode=self.resize_mode,
+                instantx_control_mode=self.instantx_control_mode,
+            ),
+        )

invokeai/app/invocations/flux_denoise.py

@@ -16,11 +16,16 @@ from invokeai.app.invocations.fields import (
     WithBoard,
     WithMetadata,
 )
-from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.flux_controlnet import FluxControlNetField
+from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
+from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
 from invokeai.backend.flux.denoise import denoise
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.sampling_utils import (
     clip_timestep_schedule_fractional,
@@ -44,7 +49,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.0.0",
+    version="3.1.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -87,6 +92,13 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
     )
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+    control: FluxControlNetField | list[FluxControlNetField] | None = InputField(
+        default=None, input=Input.Connection, description="ControlNet models."
+    )
+    controlnet_vae: VAEField | None = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
@@ -167,8 +179,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
-        b, _c, h, w = x.shape
-        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
+        b, _c, latent_h, latent_w = x.shape
+        img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
 
         bs, t5_seq_len, _ = t5_embeddings.shape
         txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
@@ -192,12 +204,21 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
-        with (
-            transformer_info.model_on_device() as (cached_weights, transformer),
-            ExitStack() as exit_stack,
-        ):
-            assert isinstance(transformer, Flux)
+        with ExitStack() as exit_stack:
+            # Prepare ControlNet extensions.
+            # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
+            controlnet_extensions = self._prep_controlnet_extensions(
+                context=context,
+                exit_stack=exit_stack,
+                latent_height=latent_h,
+                latent_width=latent_w,
+                dtype=inference_dtype,
+                device=x.device,
+            )
 
+            # Load the transformer model.
+            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
+            assert isinstance(transformer, Flux)
             config = transformer_info.config
             assert config is not None
 
@@ -242,6 +263,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
                 inpaint_extension=inpaint_extension,
+                controlnet_extensions=controlnet_extensions,
             )
 
         x = unpack(x.float(), self.height, self.width)
@@ -288,6 +310,104 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # `latents`.
         return mask.expand_as(latents)
 
+    def _prep_controlnet_extensions(
+        self,
+        context: InvocationContext,
+        exit_stack: ExitStack,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> list[XLabsControlNetExtension | InstantXControlNetExtension]:
+        # Normalize the controlnet input to list[ControlField].
+        controlnets: list[FluxControlNetField]
+        if self.control is None:
+            controlnets = []
+        elif isinstance(self.control, FluxControlNetField):
+            controlnets = [self.control]
+        elif isinstance(self.control, list):
+            controlnets = self.control
+        else:
+            raise ValueError(f"Unsupported controlnet type: {type(self.control)}")
+
+        # TODO(ryand): Add a field to the model config so that we can distinguish between XLabs and InstantX ControlNets
+        # before loading the models. Then make sure that all VAE encoding is done before loading the ControlNets to
+        # minimize peak memory.
+
+        # First, load the ControlNet models so that we can determine the ControlNet types.
+        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
+
+        # Calculate the controlnet conditioning tensors.
+        # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
+        # keep peak memory down.
+        controlnet_conds: list[torch.Tensor] = []
+        for controlnet, controlnet_model in zip(controlnets, controlnet_models, strict=True):
+            image = context.images.get_pil(controlnet.image.image_name)
+            if isinstance(controlnet_model.model, InstantXControlNetFlux):
+                if self.controlnet_vae is None:
+                    raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
+                vae_info = context.models.load(self.controlnet_vae.vae)
+                controlnet_conds.append(
+                    InstantXControlNetExtension.prepare_controlnet_cond(
+                        controlnet_image=image,
+                        vae_info=vae_info,
+                        latent_height=latent_height,
+                        latent_width=latent_width,
+                        dtype=dtype,
+                        device=device,
+                        resize_mode=controlnet.resize_mode,
+                    )
+                )
+            elif isinstance(controlnet_model.model, XLabsControlNetFlux):
+                controlnet_conds.append(
+                    XLabsControlNetExtension.prepare_controlnet_cond(
+                        controlnet_image=image,
+                        latent_height=latent_height,
+                        latent_width=latent_width,
+                        dtype=dtype,
+                        device=device,
+                        resize_mode=controlnet.resize_mode,
+                    )
+                )
+
+        # Finally, load the ControlNet models and initialize the ControlNet extensions.
+        controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension] = []
+        for controlnet, controlnet_cond, controlnet_model in zip(
+            controlnets, controlnet_conds, controlnet_models, strict=True
+        ):
+            model = exit_stack.enter_context(controlnet_model)
+
+            if isinstance(model, XLabsControlNetFlux):
+                controlnet_extensions.append(
+                    XLabsControlNetExtension(
+                        model=model,
+                        controlnet_cond=controlnet_cond,
+                        weight=controlnet.control_weight,
+                        begin_step_percent=controlnet.begin_step_percent,
+                        end_step_percent=controlnet.end_step_percent,
+                    )
+                )
+            elif isinstance(model, InstantXControlNetFlux):
+                instantx_control_mode: torch.Tensor | None = None
+                if controlnet.instantx_control_mode is not None and controlnet.instantx_control_mode >= 0:
+                    instantx_control_mode = torch.tensor(controlnet.instantx_control_mode, dtype=torch.long)
+                    instantx_control_mode = instantx_control_mode.reshape([-1, 1])
+
+                controlnet_extensions.append(
+                    InstantXControlNetExtension(
+                        model=model,
+                        controlnet_cond=controlnet_cond,
+                        instantx_control_mode=instantx_control_mode,
+                        weight=controlnet.control_weight,
+                        begin_step_percent=controlnet.begin_step_percent,
+                        end_step_percent=controlnet.end_step_percent,
+                    )
+                )
+            else:
+                raise ValueError(f"Unsupported ControlNet model type: {type(model)}")
+
+        return controlnet_extensions
+
     def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.transformer.loras:
             lora_info = context.models.load(lora.lora)

invokeai/backend/flux/controlnet/controlnet_flux_output.py

@@ -0,0 +1,58 @@
+from dataclasses import dataclass
+
+import torch
+
+
+@dataclass
+class ControlNetFluxOutput:
+    single_block_residuals: list[torch.Tensor] | None
+    double_block_residuals: list[torch.Tensor] | None
+
+    def apply_weight(self, weight: float):
+        if self.single_block_residuals is not None:
+            for i in range(len(self.single_block_residuals)):
+                self.single_block_residuals[i] = self.single_block_residuals[i] * weight
+        if self.double_block_residuals is not None:
+            for i in range(len(self.double_block_residuals)):
+                self.double_block_residuals[i] = self.double_block_residuals[i] * weight
+
+
+def add_tensor_lists_elementwise(
+    list1: list[torch.Tensor] | None, list2: list[torch.Tensor] | None
+) -> list[torch.Tensor] | None:
+    """Add two tensor lists elementwise that could be None."""
+    if list1 is None and list2 is None:
+        return None
+    if list1 is None:
+        return list2
+    if list2 is None:
+        return list1
+
+    new_list: list[torch.Tensor] = []
+    for list1_tensor, list2_tensor in zip(list1, list2, strict=True):
+        new_list.append(list1_tensor + list2_tensor)
+    return new_list
+
+
+def add_controlnet_flux_outputs(
+    controlnet_output_1: ControlNetFluxOutput, controlnet_output_2: ControlNetFluxOutput
+) -> ControlNetFluxOutput:
+    return ControlNetFluxOutput(
+        single_block_residuals=add_tensor_lists_elementwise(
+            controlnet_output_1.single_block_residuals, controlnet_output_2.single_block_residuals
+        ),
+        double_block_residuals=add_tensor_lists_elementwise(
+            controlnet_output_1.double_block_residuals, controlnet_output_2.double_block_residuals
+        ),
+    )
+
+
+def sum_controlnet_flux_outputs(
+    controlnet_outputs: list[ControlNetFluxOutput],
+) -> ControlNetFluxOutput:
+    controlnet_output_sum = ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+    for controlnet_output in controlnet_outputs:
+        controlnet_output_sum = add_controlnet_flux_outputs(controlnet_output_sum, controlnet_output)
+
+    return controlnet_output_sum

invokeai/backend/flux/controlnet/instantx_controlnet_flux.py

@@ -0,0 +1,180 @@
+# This file was initially copied from:
+# https://github.com/huggingface/diffusers/blob/99f608218caa069a2f16dcf9efab46959b15aec0/src/diffusers/models/controlnet_flux.py
+
+
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+
+from invokeai.backend.flux.controlnet.zero_module import zero_module
+from invokeai.backend.flux.model import FluxParams
+from invokeai.backend.flux.modules.layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+
+@dataclass
+class InstantXControlNetFluxOutput:
+    controlnet_block_samples: list[torch.Tensor] | None
+    controlnet_single_block_samples: list[torch.Tensor] | None
+
+
+# NOTE(ryand): Mapping between diffusers FLUX transformer params and BFL FLUX transformer params:
+# - Diffusers: BFL
+# - in_channels: in_channels
+# - num_layers: depth
+# - num_single_layers: depth_single_blocks
+# - attention_head_dim: hidden_size // num_heads
+# - num_attention_heads: num_heads
+# - joint_attention_dim: context_in_dim
+# - pooled_projection_dim: vec_in_dim
+# - guidance_embeds: guidance_embed
+# - axes_dims_rope: axes_dim
+
+
+class InstantXControlNetFlux(torch.nn.Module):
+    def __init__(self, params: FluxParams, num_control_modes: int | None = None):
+        """
+        Args:
+            params (FluxParams): The parameters for the FLUX model.
+            num_control_modes (int | None, optional): The number of controlnet modes. If non-None, then the model is a
+                'union controlnet' model and expects a mode conditioning input at runtime.
+        """
+        super().__init__()
+
+        # The following modules mirror the base FLUX transformer model.
+        # -------------------------------------------------------------
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        # The following modules are specific to the ControlNet model.
+        # -----------------------------------------------------------
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.double_blocks)):
+            self.controlnet_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(len(self.single_blocks)):
+            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
+
+        self.is_union = False
+        if num_control_modes is not None:
+            self.is_union = True
+            self.controlnet_mode_embedder = nn.Embedding(num_control_modes, self.hidden_size)
+
+        self.controlnet_x_embedder = zero_module(torch.nn.Linear(self.in_channels, self.hidden_size))
+
+    def forward(
+        self,
+        controlnet_cond: torch.Tensor,
+        controlnet_mode: torch.Tensor | None,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: torch.Tensor,
+        guidance: torch.Tensor | None = None,
+    ) -> InstantXControlNetFluxOutput:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        img = self.img_in(img)
+
+        # Add controlnet_cond embedding.
+        img = img + self.controlnet_x_embedder(controlnet_cond)
+
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        # If this is a union ControlNet, then concat the control mode embedding to the T5 text embedding.
+        if self.is_union:
+            if controlnet_mode is None:
+                # We allow users to enter 'None' as the controlnet_mode if they don't want to worry about this input.
+                # We've chosen to use a zero-embedding in this case.
+                zero_index = torch.zeros([1, 1], dtype=torch.long, device=txt.device)
+                controlnet_mode_emb = torch.zeros_like(self.controlnet_mode_embedder(zero_index))
+            else:
+                controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
+            txt = torch.cat([controlnet_mode_emb, txt], dim=1)
+            txt_ids = torch.cat([txt_ids[:, :1, :], txt_ids], dim=1)
+        else:
+            assert controlnet_mode is None
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        double_block_samples: list[torch.Tensor] = []
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            double_block_samples.append(img)
+
+        img = torch.cat((txt, img), 1)
+
+        single_block_samples: list[torch.Tensor] = []
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+            single_block_samples.append(img[:, txt.shape[1] :])
+
+        # ControlNet Block
+        controlnet_double_block_samples: list[torch.Tensor] = []
+        for double_block_sample, controlnet_block in zip(double_block_samples, self.controlnet_blocks, strict=True):
+            double_block_sample = controlnet_block(double_block_sample)
+            controlnet_double_block_samples.append(double_block_sample)
+
+        controlnet_single_block_samples: list[torch.Tensor] = []
+        for single_block_sample, controlnet_block in zip(
+            single_block_samples, self.controlnet_single_blocks, strict=True
+        ):
+            single_block_sample = controlnet_block(single_block_sample)
+            controlnet_single_block_samples.append(single_block_sample)
+
+        return InstantXControlNetFluxOutput(
+            controlnet_block_samples=controlnet_double_block_samples or None,
+            controlnet_single_block_samples=controlnet_single_block_samples or None,
+        )

invokeai/backend/flux/controlnet/state_dict_utils.py

@@ -0,0 +1,295 @@
+from typing import Any, Dict
+
+import torch
+
+from invokeai.backend.flux.model import FluxParams
+
+
+def is_state_dict_xlabs_controlnet(sd: Dict[str, Any]) -> bool:
+    """Is the state dict for an XLabs ControlNet model?
+
+    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
+    """
+    # If all of the expected keys are present, then this is very likely an XLabs ControlNet model.
+    expected_keys = {
+        "controlnet_blocks.0.bias",
+        "controlnet_blocks.0.weight",
+        "input_hint_block.0.bias",
+        "input_hint_block.0.weight",
+        "pos_embed_input.bias",
+        "pos_embed_input.weight",
+    }
+
+    if expected_keys.issubset(sd.keys()):
+        return True
+    return False
+
+
+def is_state_dict_instantx_controlnet(sd: Dict[str, Any]) -> bool:
+    """Is the state dict for an InstantX ControlNet model?
+
+    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
+    """
+    # If all of the expected keys are present, then this is very likely an InstantX ControlNet model.
+    expected_keys = {
+        "controlnet_blocks.0.bias",
+        "controlnet_blocks.0.weight",
+        "controlnet_x_embedder.bias",
+        "controlnet_x_embedder.weight",
+    }
+
+    if expected_keys.issubset(sd.keys()):
+        return True
+    return False
+
+
+def _fuse_weights(*t: torch.Tensor) -> torch.Tensor:
+    """Fuse weights along dimension 0.
+
+    Used to fuse q, k, v attention weights into a single qkv tensor when converting from diffusers to BFL format.
+    """
+    # TODO(ryand): Double check dim=0 is correct.
+    return torch.cat(t, dim=0)
+
+
+def _convert_flux_double_block_sd_from_diffusers_to_bfl_format(
+    sd: Dict[str, torch.Tensor], double_block_index: int
+) -> Dict[str, torch.Tensor]:
+    """Convert the state dict for a double block from diffusers format to BFL format."""
+    to_prefix = f"double_blocks.{double_block_index}"
+    from_prefix = f"transformer_blocks.{double_block_index}"
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Check one key to determine if this block exists.
+    if f"{from_prefix}.attn.add_q_proj.bias" not in sd:
+        return new_sd
+
+    # txt_attn.qkv
+    new_sd[f"{to_prefix}.txt_attn.qkv.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.add_q_proj.bias"),
+        sd.pop(f"{from_prefix}.attn.add_k_proj.bias"),
+        sd.pop(f"{from_prefix}.attn.add_v_proj.bias"),
+    )
+    new_sd[f"{to_prefix}.txt_attn.qkv.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.add_q_proj.weight"),
+        sd.pop(f"{from_prefix}.attn.add_k_proj.weight"),
+        sd.pop(f"{from_prefix}.attn.add_v_proj.weight"),
+    )
+
+    # img_attn.qkv
+    new_sd[f"{to_prefix}.img_attn.qkv.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.bias"),
+        sd.pop(f"{from_prefix}.attn.to_k.bias"),
+        sd.pop(f"{from_prefix}.attn.to_v.bias"),
+    )
+    new_sd[f"{to_prefix}.img_attn.qkv.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.weight"),
+        sd.pop(f"{from_prefix}.attn.to_k.weight"),
+        sd.pop(f"{from_prefix}.attn.to_v.weight"),
+    )
+
+    # Handle basic 1-to-1 key conversions.
+    key_map = {
+        # img_attn
+        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+        "attn.to_out.0.weight": "img_attn.proj.weight",
+        "attn.to_out.0.bias": "img_attn.proj.bias",
+        # img_mlp
+        "ff.net.0.proj.weight": "img_mlp.0.weight",
+        "ff.net.0.proj.bias": "img_mlp.0.bias",
+        "ff.net.2.weight": "img_mlp.2.weight",
+        "ff.net.2.bias": "img_mlp.2.bias",
+        # img_mod
+        "norm1.linear.weight": "img_mod.lin.weight",
+        "norm1.linear.bias": "img_mod.lin.bias",
+        # txt_attn
+        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
+        "attn.to_add_out.weight": "txt_attn.proj.weight",
+        "attn.to_add_out.bias": "txt_attn.proj.bias",
+        # txt_mlp
+        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+        "ff_context.net.2.weight": "txt_mlp.2.weight",
+        "ff_context.net.2.bias": "txt_mlp.2.bias",
+        # txt_mod
+        "norm1_context.linear.weight": "txt_mod.lin.weight",
+        "norm1_context.linear.bias": "txt_mod.lin.bias",
+    }
+    for from_key, to_key in key_map.items():
+        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
+
+    return new_sd
+
+
+def _convert_flux_single_block_sd_from_diffusers_to_bfl_format(
+    sd: Dict[str, torch.Tensor], single_block_index: int
+) -> Dict[str, torch.Tensor]:
+    """Convert the state dict for a single block from diffusers format to BFL format."""
+    to_prefix = f"single_blocks.{single_block_index}"
+    from_prefix = f"single_transformer_blocks.{single_block_index}"
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Check one key to determine if this block exists.
+    if f"{from_prefix}.attn.to_q.bias" not in sd:
+        return new_sd
+
+    # linear1 (qkv)
+    new_sd[f"{to_prefix}.linear1.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.bias"),
+        sd.pop(f"{from_prefix}.attn.to_k.bias"),
+        sd.pop(f"{from_prefix}.attn.to_v.bias"),
+        sd.pop(f"{from_prefix}.proj_mlp.bias"),
+    )
+    new_sd[f"{to_prefix}.linear1.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.weight"),
+        sd.pop(f"{from_prefix}.attn.to_k.weight"),
+        sd.pop(f"{from_prefix}.attn.to_v.weight"),
+        sd.pop(f"{from_prefix}.proj_mlp.weight"),
+    )
+
+    # Handle basic 1-to-1 key conversions.
+    key_map = {
+        # linear2
+        "proj_out.weight": "linear2.weight",
+        "proj_out.bias": "linear2.bias",
+        # modulation
+        "norm.linear.weight": "modulation.lin.weight",
+        "norm.linear.bias": "modulation.lin.bias",
+        # norm
+        "attn.norm_k.weight": "norm.key_norm.scale",
+        "attn.norm_q.weight": "norm.query_norm.scale",
+    }
+    for from_key, to_key in key_map.items():
+        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
+
+    return new_sd
+
+
+def convert_diffusers_instantx_state_dict_to_bfl_format(sd: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """Convert an InstantX ControlNet state dict to the format that can be loaded by our internal
+    InstantXControlNetFlux model.
+
+    The original InstantX ControlNet model was developed to be used in diffusers. We have ported the original
+    implementation to InstantXControlNetFlux to make it compatible with BFL-style models. This function converts the
+    original state dict to the format expected by InstantXControlNetFlux.
+    """
+    # Shallow copy sd so that we can pop keys from it without modifying the original.
+    sd = sd.copy()
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Handle basic 1-to-1 key conversions.
+    basic_key_map = {
+        # Base model keys.
+        # ----------------
+        # txt_in keys.
+        "context_embedder.bias": "txt_in.bias",
+        "context_embedder.weight": "txt_in.weight",
+        # guidance_in MLPEmbedder keys.
+        "time_text_embed.guidance_embedder.linear_1.bias": "guidance_in.in_layer.bias",
+        "time_text_embed.guidance_embedder.linear_1.weight": "guidance_in.in_layer.weight",
+        "time_text_embed.guidance_embedder.linear_2.bias": "guidance_in.out_layer.bias",
+        "time_text_embed.guidance_embedder.linear_2.weight": "guidance_in.out_layer.weight",
+        # vector_in MLPEmbedder keys.
+        "time_text_embed.text_embedder.linear_1.bias": "vector_in.in_layer.bias",
+        "time_text_embed.text_embedder.linear_1.weight": "vector_in.in_layer.weight",
+        "time_text_embed.text_embedder.linear_2.bias": "vector_in.out_layer.bias",
+        "time_text_embed.text_embedder.linear_2.weight": "vector_in.out_layer.weight",
+        # time_in MLPEmbedder keys.
+        "time_text_embed.timestep_embedder.linear_1.bias": "time_in.in_layer.bias",
+        "time_text_embed.timestep_embedder.linear_1.weight": "time_in.in_layer.weight",
+        "time_text_embed.timestep_embedder.linear_2.bias": "time_in.out_layer.bias",
+        "time_text_embed.timestep_embedder.linear_2.weight": "time_in.out_layer.weight",
+        # img_in keys.
+        "x_embedder.bias": "img_in.bias",
+        "x_embedder.weight": "img_in.weight",
+    }
+    for old_key, new_key in basic_key_map.items():
+        v = sd.pop(old_key, None)
+        if v is not None:
+            new_sd[new_key] = v
+
+    # Handle the double_blocks.
+    block_index = 0
+    while True:
+        converted_double_block_sd = _convert_flux_double_block_sd_from_diffusers_to_bfl_format(sd, block_index)
+        if len(converted_double_block_sd) == 0:
+            break
+        new_sd.update(converted_double_block_sd)
+        block_index += 1
+
+    # Handle the single_blocks.
+    block_index = 0
+    while True:
+        converted_singe_block_sd = _convert_flux_single_block_sd_from_diffusers_to_bfl_format(sd, block_index)
+        if len(converted_singe_block_sd) == 0:
+            break
+        new_sd.update(converted_singe_block_sd)
+        block_index += 1
+
+    # Transfer controlnet keys as-is.
+    for k in list(sd.keys()):
+        if k.startswith("controlnet_"):
+            new_sd[k] = sd.pop(k)
+
+    # Assert that all keys have been handled.
+    assert len(sd) == 0
+    return new_sd
+
+
+def infer_flux_params_from_state_dict(sd: Dict[str, torch.Tensor]) -> FluxParams:
+    """Infer the FluxParams from the shape of a FLUX state dict. When a model is distributed in diffusers format, this
+    information is all contained in the config.json file that accompanies the model. However, being apple to infer the
+    params from the state dict enables us to load models (e.g. an InstantX ControlNet) from a single weight file.
+    """
+    hidden_size = sd["img_in.weight"].shape[0]
+    mlp_hidden_dim = sd["double_blocks.0.img_mlp.0.weight"].shape[0]
+    # mlp_ratio is a float, but we treat it as an int here to avoid having to think about possible float precision
+    # issues. In practice, mlp_ratio is usually 4.
+    mlp_ratio = mlp_hidden_dim // hidden_size
+
+    head_dim = sd["double_blocks.0.img_attn.norm.query_norm.scale"].shape[0]
+    num_heads = hidden_size // head_dim
+
+    # Count the number of double blocks.
+    double_block_index = 0
+    while f"double_blocks.{double_block_index}.img_attn.qkv.weight" in sd:
+        double_block_index += 1
+
+    # Count the number of single blocks.
+    single_block_index = 0
+    while f"single_blocks.{single_block_index}.linear1.weight" in sd:
+        single_block_index += 1
+
+    return FluxParams(
+        in_channels=sd["img_in.weight"].shape[1],
+        vec_in_dim=sd["vector_in.in_layer.weight"].shape[1],
+        context_in_dim=sd["txt_in.weight"].shape[1],
+        hidden_size=hidden_size,
+        mlp_ratio=mlp_ratio,
+        num_heads=num_heads,
+        depth=double_block_index,
+        depth_single_blocks=single_block_index,
+        # axes_dim cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
+        axes_dim=[16, 56, 56],
+        # theta cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
+        theta=10_000,
+        qkv_bias="double_blocks.0.img_attn.qkv.bias" in sd,
+        guidance_embed="guidance_in.in_layer.weight" in sd,
+    )
+
+
+def infer_instantx_num_control_modes_from_state_dict(sd: Dict[str, torch.Tensor]) -> int | None:
+    """Infer the number of ControlNet Union modes from the shape of a InstantX ControlNet state dict.
+
+    Returns None if the model is not a ControlNet Union model. Otherwise returns the number of modes.
+    """
+    mode_embedder_key = "controlnet_mode_embedder.weight"
+    if mode_embedder_key not in sd:
+        return None
+
+    return sd[mode_embedder_key].shape[0]

invokeai/backend/flux/controlnet/xlabs_controlnet_flux.py

@@ -0,0 +1,130 @@
+# This file was initially based on:
+# https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/controlnet.py
+
+
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+
+from invokeai.backend.flux.controlnet.zero_module import zero_module
+from invokeai.backend.flux.model import FluxParams
+from invokeai.backend.flux.modules.layers import DoubleStreamBlock, EmbedND, MLPEmbedder, timestep_embedding
+
+
+@dataclass
+class XLabsControlNetFluxOutput:
+    controlnet_double_block_residuals: list[torch.Tensor] | None
+
+
+class XLabsControlNetFlux(torch.nn.Module):
+    """A ControlNet model for FLUX.
+
+    The architecture is very similar to the base FLUX model, with the following differences:
+    - A `controlnet_depth` parameter is passed to control the number of double_blocks that the ControlNet is applied to.
+      In order to keep the ControlNet small, this is typically much less than the depth of the base FLUX model.
+    - There is a set of `controlnet_blocks` that are applied to the output of each double_block.
+    """
+
+    def __init__(self, params: FluxParams, controlnet_depth: int = 2):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else torch.nn.Identity()
+        )
+        self.txt_in = torch.nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = torch.nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(controlnet_depth)
+            ]
+        )
+
+        # Add ControlNet blocks.
+        self.controlnet_blocks = torch.nn.ModuleList([])
+        for _ in range(controlnet_depth):
+            controlnet_block = torch.nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = zero_module(controlnet_block)
+            self.controlnet_blocks.append(controlnet_block)
+        self.pos_embed_input = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.input_hint_block = torch.nn.Sequential(
+            torch.nn.Conv2d(3, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            zero_module(torch.nn.Conv2d(16, 16, 3, padding=1)),
+        )
+
+    def forward(
+        self,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: torch.Tensor,
+        guidance: torch.Tensor | None = None,
+    ) -> XLabsControlNetFluxOutput:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        controlnet_cond = self.input_hint_block(controlnet_cond)
+        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        block_res_samples: list[torch.Tensor] = []
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            block_res_samples.append(img)
+
+        controlnet_block_res_samples: list[torch.Tensor] = []
+        for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks, strict=True):
+            block_res_sample = controlnet_block(block_res_sample)
+            controlnet_block_res_samples.append(block_res_sample)
+
+        return XLabsControlNetFluxOutput(controlnet_double_block_residuals=controlnet_block_res_samples)

invokeai/backend/flux/controlnet/zero_module.py

@@ -0,0 +1,12 @@
+from typing import TypeVar
+
+import torch
+
+T = TypeVar("T", bound=torch.nn.Module)
+
+
+def zero_module(module: T) -> T:
+    """Initialize the parameters of a module to zero."""
+    for p in module.parameters():
+        torch.nn.init.zeros_(p)
+    return module

invokeai/backend/flux/denoise.py

@@ -3,7 +3,10 @@ from typing import Callable
 import torch
 from tqdm import tqdm
 
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
+from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
@@ -21,6 +24,7 @@ def denoise(
     step_callback: Callable[[PipelineIntermediateState], None],
     guidance: float,
     inpaint_extension: InpaintExtension | None,
+    controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
 ):
     # step 0 is the initial state
     total_steps = len(timesteps) - 1
@@ -38,6 +42,30 @@ def denoise(
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
     for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+
+        # Run ControlNet models.
+        controlnet_residuals: list[ControlNetFluxOutput] = []
+        for controlnet_extension in controlnet_extensions:
+            controlnet_residuals.append(
+                controlnet_extension.run_controlnet(
+                    timestep_index=step - 1,
+                    total_num_timesteps=total_steps,
+                    img=img,
+                    img_ids=img_ids,
+                    txt=txt,
+                    txt_ids=txt_ids,
+                    y=vec,
+                    timesteps=t_vec,
+                    guidance=guidance_vec,
+                )
+            )
+
+        # Merge the ControlNet residuals from multiple ControlNets.
+        # TODO(ryand): We may want to alculate the sum just-in-time to keep peak memory low. Keep in mind, that the
+        # controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
+        # tensors. Calculating the sum materializes each tensor into its own instance.
+        merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
+
         pred = model(
             img=img,
             img_ids=img_ids,
@@ -46,6 +74,8 @@ def denoise(
             y=vec,
             timesteps=t_vec,
             guidance=guidance_vec,
+            controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
+            controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
         )
 
         preview_img = img - t_curr * pred

invokeai/backend/flux/extensions/base_controlnet_extension.py

@@ -0,0 +1,45 @@
+import math
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+import torch
+
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+
+
+class BaseControlNetExtension(ABC):
+    def __init__(
+        self,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        self._weight = weight
+        self._begin_step_percent = begin_step_percent
+        self._end_step_percent = end_step_percent
+
+    def _get_weight(self, timestep_index: int, total_num_timesteps: int) -> float:
+        first_step = math.floor(self._begin_step_percent * total_num_timesteps)
+        last_step = math.ceil(self._end_step_percent * total_num_timesteps)
+
+        if timestep_index < first_step or timestep_index > last_step:
+            return 0.0
+
+        if isinstance(self._weight, list):
+            return self._weight[timestep_index]
+
+        return self._weight
+
+    @abstractmethod
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput: ...

invokeai/backend/flux/extensions/instantx_controlnet_extension.py

@@ -0,0 +1,194 @@
+import math
+from typing import List, Union
+
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import (
+    InstantXControlNetFlux,
+    InstantXControlNetFluxOutput,
+)
+from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
+from invokeai.backend.flux.sampling_utils import pack
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+
+
+class InstantXControlNetExtension(BaseControlNetExtension):
+    def __init__(
+        self,
+        model: InstantXControlNetFlux,
+        controlnet_cond: torch.Tensor,
+        instantx_control_mode: torch.Tensor | None,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        super().__init__(
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+        self._model = model
+        # The VAE-encoded and 'packed' control image to pass to the ControlNet model.
+        self._controlnet_cond = controlnet_cond
+        # TODO(ryand): Should we define an enum for the instantx_control_mode? Is it likely to change for future models?
+        # The control mode for InstantX ControlNet union models.
+        # See the values defined here: https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union#control-mode
+        # Expected shape: (batch_size, 1), Expected dtype: torch.long
+        # If None, a zero-embedding will be used.
+        self._instantx_control_mode = instantx_control_mode
+
+        # TODO(ryand): Pass in these params if a new base transformer / InstantX ControlNet pair get released.
+        self._flux_transformer_num_double_blocks = 19
+        self._flux_transformer_num_single_blocks = 38
+
+    @classmethod
+    def prepare_controlnet_cond(
+        cls,
+        controlnet_image: Image,
+        vae_info: LoadedModel,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        resized_controlnet_image = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Shift the image from [0, 1] to [-1, 1].
+        resized_controlnet_image = resized_controlnet_image * 2 - 1
+
+        # Run VAE encoder.
+        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
+        controlnet_cond = pack(controlnet_cond)
+
+        return controlnet_cond
+
+    @classmethod
+    def from_controlnet_image(
+        cls,
+        model: InstantXControlNetFlux,
+        controlnet_image: Image,
+        instantx_control_mode: torch.Tensor | None,
+        vae_info: LoadedModel,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        resized_controlnet_image = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Shift the image from [0, 1] to [-1, 1].
+        resized_controlnet_image = resized_controlnet_image * 2 - 1
+
+        # Run VAE encoder.
+        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
+        controlnet_cond = pack(controlnet_cond)
+
+        return cls(
+            model=model,
+            controlnet_cond=controlnet_cond,
+            instantx_control_mode=instantx_control_mode,
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+    def _instantx_output_to_controlnet_output(
+        self, instantx_output: InstantXControlNetFluxOutput
+    ) -> ControlNetFluxOutput:
+        # The `interval_control` logic here is based on
+        # https://github.com/huggingface/diffusers/blob/31058cdaef63ca660a1a045281d156239fba8192/src/diffusers/models/transformers/transformer_flux.py#L507-L511
+
+        # Handle double block residuals.
+        double_block_residuals: list[torch.Tensor] = []
+        double_block_samples = instantx_output.controlnet_block_samples
+        if double_block_samples:
+            interval_control = self._flux_transformer_num_double_blocks / len(double_block_samples)
+            interval_control = int(math.ceil(interval_control))
+            for i in range(self._flux_transformer_num_double_blocks):
+                double_block_residuals.append(double_block_samples[i // interval_control])
+
+        # Handle single block residuals.
+        single_block_residuals: list[torch.Tensor] = []
+        single_block_samples = instantx_output.controlnet_single_block_samples
+        if single_block_samples:
+            interval_control = self._flux_transformer_num_single_blocks / len(single_block_samples)
+            interval_control = int(math.ceil(interval_control))
+            for i in range(self._flux_transformer_num_single_blocks):
+                single_block_residuals.append(single_block_samples[i // interval_control])
+
+        return ControlNetFluxOutput(
+            double_block_residuals=double_block_residuals or None,
+            single_block_residuals=single_block_residuals or None,
+        )
+
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput:
+        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
+        if weight < 1e-6:
+            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+        # Make sure inputs have correct device and dtype.
+        self._controlnet_cond = self._controlnet_cond.to(device=img.device, dtype=img.dtype)
+        self._instantx_control_mode = (
+            self._instantx_control_mode.to(device=img.device) if self._instantx_control_mode is not None else None
+        )
+
+        instantx_output: InstantXControlNetFluxOutput = self._model(
+            controlnet_cond=self._controlnet_cond,
+            controlnet_mode=self._instantx_control_mode,
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            timesteps=timesteps,
+            y=y,
+            guidance=guidance,
+        )
+
+        controlnet_output = self._instantx_output_to_controlnet_output(instantx_output)
+        controlnet_output.apply_weight(weight)
+        return controlnet_output

invokeai/backend/flux/extensions/xlabs_controlnet_extension.py

@@ -0,0 +1,150 @@
+from typing import List, Union
+
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux, XLabsControlNetFluxOutput
+from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
+
+
+class XLabsControlNetExtension(BaseControlNetExtension):
+    def __init__(
+        self,
+        model: XLabsControlNetFlux,
+        controlnet_cond: torch.Tensor,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        super().__init__(
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+        self._model = model
+        # _controlnet_cond is the control image passed to the ControlNet model.
+        # Pixel values are in the range [-1, 1]. Shape: (batch_size, 3, height, width).
+        self._controlnet_cond = controlnet_cond
+
+        # TODO(ryand): Pass in these params if a new base transformer / XLabs ControlNet pair get released.
+        self._flux_transformer_num_double_blocks = 19
+        self._flux_transformer_num_single_blocks = 38
+
+    @classmethod
+    def prepare_controlnet_cond(
+        cls,
+        controlnet_image: Image,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        controlnet_cond = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Map pixel values from [0, 1] to [-1, 1].
+        controlnet_cond = controlnet_cond * 2 - 1
+
+        return controlnet_cond
+
+    @classmethod
+    def from_controlnet_image(
+        cls,
+        model: XLabsControlNetFlux,
+        controlnet_image: Image,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        controlnet_cond = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Map pixel values from [0, 1] to [-1, 1].
+        controlnet_cond = controlnet_cond * 2 - 1
+
+        return cls(
+            model=model,
+            controlnet_cond=controlnet_cond,
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+    def _xlabs_output_to_controlnet_output(self, xlabs_output: XLabsControlNetFluxOutput) -> ControlNetFluxOutput:
+        # The modulo index logic used here is based on:
+        # https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/model.py#L198-L200
+
+        # Handle double block residuals.
+        double_block_residuals: list[torch.Tensor] = []
+        xlabs_double_block_residuals = xlabs_output.controlnet_double_block_residuals
+        if xlabs_double_block_residuals is not None:
+            for i in range(self._flux_transformer_num_double_blocks):
+                double_block_residuals.append(xlabs_double_block_residuals[i % len(xlabs_double_block_residuals)])
+
+        return ControlNetFluxOutput(
+            double_block_residuals=double_block_residuals,
+            single_block_residuals=None,
+        )
+
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput:
+        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
+        if weight < 1e-6:
+            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+        xlabs_output: XLabsControlNetFluxOutput = self._model(
+            img=img,
+            img_ids=img_ids,
+            controlnet_cond=self._controlnet_cond,
+            txt=txt,
+            txt_ids=txt_ids,
+            timesteps=timesteps,
+            y=y,
+            guidance=guidance,
+        )
+
+        controlnet_output = self._xlabs_output_to_controlnet_output(xlabs_output)
+        controlnet_output.apply_weight(weight)
+        return controlnet_output

invokeai/backend/flux/model.py

@@ -87,7 +87,9 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None = None,
+        guidance: Tensor | None,
+        controlnet_double_block_residuals: list[Tensor] | None,
+        controlnet_single_block_residuals: list[Tensor] | None,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -105,12 +107,27 @@ class Flux(nn.Module):
         ids = torch.cat((txt_ids, img_ids), dim=1)
         pe = self.pe_embedder(ids)
 
-        for block in self.double_blocks:
+        # Validate double_block_residuals shape.
+        if controlnet_double_block_residuals is not None:
+            assert len(controlnet_double_block_residuals) == len(self.double_blocks)
+        for block_index, block in enumerate(self.double_blocks):
             img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
 
+            if controlnet_double_block_residuals is not None:
+                img += controlnet_double_block_residuals[block_index]
+
         img = torch.cat((txt, img), 1)
-        for block in self.single_blocks:
+
+        # Validate single_block_residuals shape.
+        if controlnet_single_block_residuals is not None:
+            assert len(controlnet_single_block_residuals) == len(self.single_blocks)
+
+        for block_index, block in enumerate(self.single_blocks):
             img = block(img, vec=vec, pe=pe)
+
+            if controlnet_single_block_residuals is not None:
+                img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]
+
         img = img[:, txt.shape[1] :, ...]
 
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)

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

@@ -8,17 +8,36 @@ from diffusers import ControlNetModel
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
+)
+from invokeai.backend.model_manager.config import (
     BaseModelType,
+    ControlNetCheckpointConfig,
     ModelFormat,
     ModelType,
+    SubModelType,
 )
-from invokeai.backend.model_manager.config import ControlNetCheckpointConfig, SubModelType
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
 class ControlNetLoader(GenericDiffusersLoader):
     """Class to load ControlNet models."""
 

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

@@ -10,6 +10,15 @@ from safetensors.torch import load_file
 from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
+from invokeai.backend.flux.controlnet.state_dict_utils import (
+    convert_diffusers_instantx_state_dict_to_bfl_format,
+    infer_flux_params_from_state_dict,
+    infer_instantx_num_control_modes_from_state_dict,
+    is_state_dict_instantx_controlnet,
+    is_state_dict_xlabs_controlnet,
+)
+from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.util import ae_params, params
@@ -24,6 +33,8 @@ from invokeai.backend.model_manager import (
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     CLIPEmbedDiffusersConfig,
+    ControlNetCheckpointConfig,
+    ControlNetDiffusersConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
     MainGGUFCheckpointConfig,
@@ -293,3 +304,51 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 sd = convert_bundle_to_flux_transformer_checkpoint(sd)
             model.load_state_dict(sd, assign=True)
         return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
+class FluxControlnetModel(ModelLoader):
+    """Class to load FLUX ControlNet models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, ControlNetCheckpointConfig):
+            model_path = Path(config.path)
+        elif isinstance(config, ControlNetDiffusersConfig):
+            # If this is a diffusers directory, we simply ignore the config file and load from the weight file.
+            model_path = Path(config.path) / "diffusion_pytorch_model.safetensors"
+        else:
+            raise ValueError(f"Unexpected ControlNet model config type: {type(config)}")
+
+        sd = load_file(model_path)
+
+        # Detect the FLUX ControlNet model type from the state dict.
+        if is_state_dict_xlabs_controlnet(sd):
+            return self._load_xlabs_controlnet(sd)
+        elif is_state_dict_instantx_controlnet(sd):
+            return self._load_instantx_controlnet(sd)
+        else:
+            raise ValueError("Do not recognize the state dict as an XLabs or InstantX ControlNet model.")
+
+    def _load_xlabs_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
+        with accelerate.init_empty_weights():
+            # HACK(ryand): Is it safe to assume dev here?
+            model = XLabsControlNetFlux(params["flux-dev"])
+
+        model.load_state_dict(sd, assign=True)
+        return model
+
+    def _load_instantx_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
+        sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+        flux_params = infer_flux_params_from_state_dict(sd)
+        num_control_modes = infer_instantx_num_control_modes_from_state_dict(sd)
+
+        with accelerate.init_empty_weights():
+            model = InstantXControlNetFlux(flux_params, num_control_modes)
+
+        model.load_state_dict(sd, assign=True)
+        return model

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.flux.controlnet.state_dict_utils import (
+    is_state_dict_instantx_controlnet,
+    is_state_dict_xlabs_controlnet,
+)
 from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
     is_state_dict_likely_in_flux_diffusers_format,
 )
@@ -116,6 +120,7 @@ class ModelProbe(object):
         "CLIPModel": ModelType.CLIPEmbed,
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
+        "FluxControlNetModel": ModelType.ControlNet,
     }
 
     @classmethod
@@ -255,7 +260,19 @@ class ModelProbe(object):
             # 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")):
+            elif key.startswith(
+                (
+                    "controlnet",
+                    "control_model",
+                    "input_blocks",
+                    # XLabs FLUX ControlNet models have keys starting with "controlnet_blocks."
+                    # For example: https://huggingface.co/XLabs-AI/flux-controlnet-collections/blob/86ab1e915a389d5857135c00e0d350e9e38a9048/flux-canny-controlnet_v2.safetensors
+                    # TODO(ryand): This is very fragile. XLabs FLUX ControlNet models also contain keys starting with
+                    # "double_blocks.", which we check for above. But, I'm afraid to modify this logic because it is so
+                    # delicate.
+                    "controlnet_blocks",
+                )
+            ):
                 return ModelType.ControlNet
             elif key.startswith(("image_proj.", "ip_adapter.")):
                 return ModelType.IPAdapter
@@ -438,6 +455,7 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "lineart": "lineart_image_processor",
     "lineart_anime": "lineart_anime_image_processor",
     "softedge": "hed_image_processor",
+    "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
     "pose": "dw_openpose_image_processor",
     "mediapipe": "mediapipe_face_processor",
@@ -449,7 +467,8 @@ MODEL_NAME_TO_PREPROCESSOR = {
 
 def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
     for k, v in MODEL_NAME_TO_PREPROCESSOR.items():
-        if k in model_name:
+        model_name_lower = model_name.lower()
+        if k in model_name_lower:
             return ControlAdapterDefaultSettings(preprocessor=v)
     return None
 
@@ -623,6 +642,11 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
+        if is_state_dict_xlabs_controlnet(checkpoint) or is_state_dict_instantx_controlnet(checkpoint):
+            # TODO(ryand): Should I distinguish between XLabs, InstantX and other ControlNet models by implementing
+            # get_format()?
+            return BaseModelType.Flux
+
         for key_name in (
             "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
             "controlnet_mid_block.bias",
@@ -844,22 +868,19 @@ class ControlNetFolderProbe(FolderProbeBase):
             raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
         with open(config_file, "r") as file:
             config = json.load(file)
+
+        if config.get("_class_name", None) == "FluxControlNetModel":
+            return BaseModelType.Flux
+
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
-        base_model = (
-            BaseModelType.StableDiffusion1
-            if dimension == 768
-            else (
-                BaseModelType.StableDiffusion2
-                if dimension == 1024
-                else BaseModelType.StableDiffusionXL
-                if dimension == 2048
-                else None
-            )
-        )
-        if not base_model:
-            raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
-        return base_model
+        if dimension == 768:
+            return BaseModelType.StableDiffusion1
+        if dimension == 1024:
+            return BaseModelType.StableDiffusion2
+        if dimension == 2048:
+            return BaseModelType.StableDiffusionXL
+        raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
 
 
 class LoRAFolderProbe(FolderProbeBase):

invokeai/backend/model_manager/starter_models.py

@@ -422,6 +422,13 @@ STARTER_MODELS: list[StarterModel] = [
         description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
         type=ModelType.ControlNet,
     ),
+    StarterModel(
+        name="FLUX.1-dev-Controlnet-Union-Pro",
+        base=BaseModelType.Flux,
+        source="Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+        description="A unified ControlNet for FLUX.1-dev model that supports 7 control modes, including canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6)",
+        type=ModelType.ControlNet,
+    ),
     # endregion
     # region T2I Adapter
     StarterModel(

invokeai/frontend/web/src/features/controlLayers/components/CanvasAddEntityButtons.tsx

@@ -80,7 +80,6 @@ export const CanvasAddEntityButtons = memo(() => {
             justifyContent="flex-start"
             leftIcon={<PiPlusBold />}
             onClick={addControlLayer}
-            isDisabled={isFLUX}
           >
             {t('controlLayers.controlLayer')}
           </Button>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -56,7 +56,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           </MenuItem>
         </MenuGroup>
         <MenuGroup title={t('controlLayers.layer_other')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer} isDisabled={isFLUX}>
+          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
             {t('controlLayers.controlLayer')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerControlAdapter.tsx

@@ -16,6 +16,7 @@ import {
   controlLayerModelChanged,
   controlLayerWeightChanged,
 } from 'features/controlLayers/store/canvasSlice';
+import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
 import type { CanvasEntityIdentifier, ControlModeV2 } from 'features/controlLayers/store/types';
 import { memo, useCallback, useMemo } from 'react';
@@ -42,6 +43,7 @@ export const ControlLayerControlAdapter = memo(() => {
   const entityIdentifier = useEntityIdentifierContext('control_layer');
   const controlAdapter = useControlLayerControlAdapter(entityIdentifier);
   const filter = useEntityFilter(entityIdentifier);
+  const isFLUX = useAppSelector(selectIsFLUX);
 
   const onChangeBeginEndStepPct = useCallback(
     (beginEndStepPct: [number, number]) => {
@@ -117,7 +119,7 @@ export const ControlLayerControlAdapter = memo(() => {
       </Flex>
       <Weight weight={controlAdapter.weight} onChange={onChangeWeight} />
       <BeginEndStepPct beginEndStepPct={controlAdapter.beginEndStepPct} onChange={onChangeBeginEndStepPct} />
-      {controlAdapter.type === 'controlnet' && (
+      {controlAdapter.type === 'controlnet' && !isFLUX && (
         <ControlLayerControlAdapterControlMode
           controlMode={controlAdapter.controlMode}
           onChange={onChangeControlMode}

invokeai/frontend/web/src/features/nodes/util/graph/generation/addControlAdapters.ts

@@ -110,10 +110,10 @@ const addControlNetToGraph = (
 
   const controlNet = g.addNode({
     id: `control_net_${id}`,
-    type: 'controlnet',
+    type: model.base === 'flux' ? 'flux_controlnet' : 'controlnet',
     begin_step_percent: beginEndStepPct[0],
     end_step_percent: beginEndStepPct[1],
-    control_mode: controlMode,
+    control_mode: model.base === 'flux' ? undefined : controlMode,
     resize_mode: 'just_resize',
     control_model: model,
     control_weight: weight,

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildFLUXGraph.ts

@@ -19,6 +19,8 @@ import type { Invocation } from 'services/api/types';
 import { isNonRefinerMainModelConfig } from 'services/api/types';
 import { assert } from 'tsafe';
 
+import { addControlNets } from './addControlAdapters';
+
 const log = logger('system');
 
 export const buildFLUXGraph = async (
@@ -93,6 +95,7 @@ export const buildFLUXGraph = async (
   > = l2i;
 
   g.addEdge(modelLoader, 'transformer', noise, 'transformer');
+  g.addEdge(modelLoader, 'vae', noise, 'controlnet_vae');
   g.addEdge(modelLoader, 'vae', l2i, 'vae');
 
   g.addEdge(modelLoader, 'clip', posCond, 'clip');
@@ -177,6 +180,24 @@ export const buildFLUXGraph = async (
     );
   }
 
+  const controlNetCollector = g.addNode({
+    type: 'collect',
+    id: getPrefixedId('control_net_collector'),
+  });
+  const controlNetResult = await addControlNets(
+    manager,
+    canvas.controlLayers.entities,
+    g,
+    canvas.bbox.rect,
+    controlNetCollector,
+    modelConfig.base
+  );
+  if (controlNetResult.addedControlNets > 0) {
+    g.addEdge(controlNetCollector, 'collection', noise, 'control');
+  } else {
+    g.deleteNode(controlNetCollector.id);
+  }
+
   if (state.system.shouldUseNSFWChecker) {
     canvasOutput = addNSFWChecker(g, canvasOutput);
   }

scripts/extract_sd_keys_and_shapes.py

@@ -0,0 +1,30 @@
+import argparse
+import json
+
+from safetensors.torch import load_file
+
+
+def extract_sd_keys_and_shapes(safetensors_file: str):
+    sd = load_file(safetensors_file)
+
+    keys_to_shapes = {k: v.shape for k, v in sd.items()}
+
+    out_file = "keys_and_shapes.json"
+    with open(out_file, "w") as f:
+        json.dump(keys_to_shapes, f, indent=4)
+
+    print(f"Keys and shapes written to '{out_file}'.")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Extracts the keys and shapes from the state dict in a safetensors file. Intended for creating "
+        + "dummy state dicts for use in unit tests."
+    )
+    parser.add_argument("safetensors_file", type=str, help="Path to the safetensors file.")
+    args = parser.parse_args()
+    extract_sd_keys_and_shapes(args.safetensors_file)
+
+
+if __name__ == "__main__":
+    main()

tests/backend/flux/controlnet/instantx_flux_controlnet_state_dict.py

@@ -0,0 +1,374 @@
+# State dict keys and shapes for an InstantX FLUX ControlNet Union model. Intended to be used for unit tests.
+# These keys were extracted from:
+# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union/blob/4f32d6f2b220f8873d49bb8acc073e1df180c994/diffusion_pytorch_model.safetensors
+instantx_sd_shapes = {
+    "context_embedder.bias": [3072],
+    "context_embedder.weight": [3072, 4096],
+    "controlnet_blocks.0.bias": [3072],
+    "controlnet_blocks.0.weight": [3072, 3072],
+    "controlnet_blocks.1.bias": [3072],
+    "controlnet_blocks.1.weight": [3072, 3072],
+    "controlnet_blocks.2.bias": [3072],
+    "controlnet_blocks.2.weight": [3072, 3072],
+    "controlnet_blocks.3.bias": [3072],
+    "controlnet_blocks.3.weight": [3072, 3072],
+    "controlnet_blocks.4.bias": [3072],
+    "controlnet_blocks.4.weight": [3072, 3072],
+    "controlnet_mode_embedder.weight": [10, 3072],
+    "controlnet_single_blocks.0.bias": [3072],
+    "controlnet_single_blocks.0.weight": [3072, 3072],
+    "controlnet_single_blocks.1.bias": [3072],
+    "controlnet_single_blocks.1.weight": [3072, 3072],
+    "controlnet_single_blocks.2.bias": [3072],
+    "controlnet_single_blocks.2.weight": [3072, 3072],
+    "controlnet_single_blocks.3.bias": [3072],
+    "controlnet_single_blocks.3.weight": [3072, 3072],
+    "controlnet_single_blocks.4.bias": [3072],
+    "controlnet_single_blocks.4.weight": [3072, 3072],
+    "controlnet_single_blocks.5.bias": [3072],
+    "controlnet_single_blocks.5.weight": [3072, 3072],
+    "controlnet_single_blocks.6.bias": [3072],
+    "controlnet_single_blocks.6.weight": [3072, 3072],
+    "controlnet_single_blocks.7.bias": [3072],
+    "controlnet_single_blocks.7.weight": [3072, 3072],
+    "controlnet_single_blocks.8.bias": [3072],
+    "controlnet_single_blocks.8.weight": [3072, 3072],
+    "controlnet_single_blocks.9.bias": [3072],
+    "controlnet_single_blocks.9.weight": [3072, 3072],
+    "controlnet_x_embedder.bias": [3072],
+    "controlnet_x_embedder.weight": [3072, 64],
+    "single_transformer_blocks.0.attn.norm_k.weight": [128],
+    "single_transformer_blocks.0.attn.norm_q.weight": [128],
+    "single_transformer_blocks.0.attn.to_k.bias": [3072],
+    "single_transformer_blocks.0.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.0.attn.to_q.bias": [3072],
+    "single_transformer_blocks.0.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.0.attn.to_v.bias": [3072],
+    "single_transformer_blocks.0.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.0.norm.linear.bias": [9216],
+    "single_transformer_blocks.0.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.0.proj_mlp.bias": [12288],
+    "single_transformer_blocks.0.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.0.proj_out.bias": [3072],
+    "single_transformer_blocks.0.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.1.attn.norm_k.weight": [128],
+    "single_transformer_blocks.1.attn.norm_q.weight": [128],
+    "single_transformer_blocks.1.attn.to_k.bias": [3072],
+    "single_transformer_blocks.1.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.1.attn.to_q.bias": [3072],
+    "single_transformer_blocks.1.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.1.attn.to_v.bias": [3072],
+    "single_transformer_blocks.1.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.1.norm.linear.bias": [9216],
+    "single_transformer_blocks.1.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.1.proj_mlp.bias": [12288],
+    "single_transformer_blocks.1.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.1.proj_out.bias": [3072],
+    "single_transformer_blocks.1.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.2.attn.norm_k.weight": [128],
+    "single_transformer_blocks.2.attn.norm_q.weight": [128],
+    "single_transformer_blocks.2.attn.to_k.bias": [3072],
+    "single_transformer_blocks.2.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.2.attn.to_q.bias": [3072],
+    "single_transformer_blocks.2.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.2.attn.to_v.bias": [3072],
+    "single_transformer_blocks.2.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.2.norm.linear.bias": [9216],
+    "single_transformer_blocks.2.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.2.proj_mlp.bias": [12288],
+    "single_transformer_blocks.2.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.2.proj_out.bias": [3072],
+    "single_transformer_blocks.2.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.3.attn.norm_k.weight": [128],
+    "single_transformer_blocks.3.attn.norm_q.weight": [128],
+    "single_transformer_blocks.3.attn.to_k.bias": [3072],
+    "single_transformer_blocks.3.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.3.attn.to_q.bias": [3072],
+    "single_transformer_blocks.3.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.3.attn.to_v.bias": [3072],
+    "single_transformer_blocks.3.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.3.norm.linear.bias": [9216],
+    "single_transformer_blocks.3.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.3.proj_mlp.bias": [12288],
+    "single_transformer_blocks.3.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.3.proj_out.bias": [3072],
+    "single_transformer_blocks.3.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.4.attn.norm_k.weight": [128],
+    "single_transformer_blocks.4.attn.norm_q.weight": [128],
+    "single_transformer_blocks.4.attn.to_k.bias": [3072],
+    "single_transformer_blocks.4.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.4.attn.to_q.bias": [3072],
+    "single_transformer_blocks.4.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.4.attn.to_v.bias": [3072],
+    "single_transformer_blocks.4.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.4.norm.linear.bias": [9216],
+    "single_transformer_blocks.4.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.4.proj_mlp.bias": [12288],
+    "single_transformer_blocks.4.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.4.proj_out.bias": [3072],
+    "single_transformer_blocks.4.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.5.attn.norm_k.weight": [128],
+    "single_transformer_blocks.5.attn.norm_q.weight": [128],
+    "single_transformer_blocks.5.attn.to_k.bias": [3072],
+    "single_transformer_blocks.5.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.5.attn.to_q.bias": [3072],
+    "single_transformer_blocks.5.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.5.attn.to_v.bias": [3072],
+    "single_transformer_blocks.5.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.5.norm.linear.bias": [9216],
+    "single_transformer_blocks.5.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.5.proj_mlp.bias": [12288],
+    "single_transformer_blocks.5.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.5.proj_out.bias": [3072],
+    "single_transformer_blocks.5.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.6.attn.norm_k.weight": [128],
+    "single_transformer_blocks.6.attn.norm_q.weight": [128],
+    "single_transformer_blocks.6.attn.to_k.bias": [3072],
+    "single_transformer_blocks.6.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.6.attn.to_q.bias": [3072],
+    "single_transformer_blocks.6.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.6.attn.to_v.bias": [3072],
+    "single_transformer_blocks.6.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.6.norm.linear.bias": [9216],
+    "single_transformer_blocks.6.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.6.proj_mlp.bias": [12288],
+    "single_transformer_blocks.6.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.6.proj_out.bias": [3072],
+    "single_transformer_blocks.6.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.7.attn.norm_k.weight": [128],
+    "single_transformer_blocks.7.attn.norm_q.weight": [128],
+    "single_transformer_blocks.7.attn.to_k.bias": [3072],
+    "single_transformer_blocks.7.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.7.attn.to_q.bias": [3072],
+    "single_transformer_blocks.7.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.7.attn.to_v.bias": [3072],
+    "single_transformer_blocks.7.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.7.norm.linear.bias": [9216],
+    "single_transformer_blocks.7.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.7.proj_mlp.bias": [12288],
+    "single_transformer_blocks.7.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.7.proj_out.bias": [3072],
+    "single_transformer_blocks.7.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.8.attn.norm_k.weight": [128],
+    "single_transformer_blocks.8.attn.norm_q.weight": [128],
+    "single_transformer_blocks.8.attn.to_k.bias": [3072],
+    "single_transformer_blocks.8.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.8.attn.to_q.bias": [3072],
+    "single_transformer_blocks.8.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.8.attn.to_v.bias": [3072],
+    "single_transformer_blocks.8.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.8.norm.linear.bias": [9216],
+    "single_transformer_blocks.8.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.8.proj_mlp.bias": [12288],
+    "single_transformer_blocks.8.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.8.proj_out.bias": [3072],
+    "single_transformer_blocks.8.proj_out.weight": [3072, 15360],
+    "single_transformer_blocks.9.attn.norm_k.weight": [128],
+    "single_transformer_blocks.9.attn.norm_q.weight": [128],
+    "single_transformer_blocks.9.attn.to_k.bias": [3072],
+    "single_transformer_blocks.9.attn.to_k.weight": [3072, 3072],
+    "single_transformer_blocks.9.attn.to_q.bias": [3072],
+    "single_transformer_blocks.9.attn.to_q.weight": [3072, 3072],
+    "single_transformer_blocks.9.attn.to_v.bias": [3072],
+    "single_transformer_blocks.9.attn.to_v.weight": [3072, 3072],
+    "single_transformer_blocks.9.norm.linear.bias": [9216],
+    "single_transformer_blocks.9.norm.linear.weight": [9216, 3072],
+    "single_transformer_blocks.9.proj_mlp.bias": [12288],
+    "single_transformer_blocks.9.proj_mlp.weight": [12288, 3072],
+    "single_transformer_blocks.9.proj_out.bias": [3072],
+    "single_transformer_blocks.9.proj_out.weight": [3072, 15360],
+    "time_text_embed.guidance_embedder.linear_1.bias": [3072],
+    "time_text_embed.guidance_embedder.linear_1.weight": [3072, 256],
+    "time_text_embed.guidance_embedder.linear_2.bias": [3072],
+    "time_text_embed.guidance_embedder.linear_2.weight": [3072, 3072],
+    "time_text_embed.text_embedder.linear_1.bias": [3072],
+    "time_text_embed.text_embedder.linear_1.weight": [3072, 768],
+    "time_text_embed.text_embedder.linear_2.bias": [3072],
+    "time_text_embed.text_embedder.linear_2.weight": [3072, 3072],
+    "time_text_embed.timestep_embedder.linear_1.bias": [3072],
+    "time_text_embed.timestep_embedder.linear_1.weight": [3072, 256],
+    "time_text_embed.timestep_embedder.linear_2.bias": [3072],
+    "time_text_embed.timestep_embedder.linear_2.weight": [3072, 3072],
+    "transformer_blocks.0.attn.add_k_proj.bias": [3072],
+    "transformer_blocks.0.attn.add_k_proj.weight": [3072, 3072],
+    "transformer_blocks.0.attn.add_q_proj.bias": [3072],
+    "transformer_blocks.0.attn.add_q_proj.weight": [3072, 3072],
+    "transformer_blocks.0.attn.add_v_proj.bias": [3072],
+    "transformer_blocks.0.attn.add_v_proj.weight": [3072, 3072],
+    "transformer_blocks.0.attn.norm_added_k.weight": [128],
+    "transformer_blocks.0.attn.norm_added_q.weight": [128],
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+    "transformer_blocks.3.attn.norm_k.weight": [128],
+    "transformer_blocks.3.attn.norm_q.weight": [128],
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+    "transformer_blocks.3.attn.to_add_out.weight": [3072, 3072],
+    "transformer_blocks.3.attn.to_k.bias": [3072],
+    "transformer_blocks.3.attn.to_k.weight": [3072, 3072],
+    "transformer_blocks.3.attn.to_out.0.bias": [3072],
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+    "transformer_blocks.3.attn.to_q.bias": [3072],
+    "transformer_blocks.3.attn.to_q.weight": [3072, 3072],
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+    "transformer_blocks.3.ff.net.0.proj.weight": [12288, 3072],
+    "transformer_blocks.3.ff.net.2.bias": [3072],
+    "transformer_blocks.3.ff.net.2.weight": [3072, 12288],
+    "transformer_blocks.3.ff_context.net.0.proj.bias": [12288],
+    "transformer_blocks.3.ff_context.net.0.proj.weight": [12288, 3072],
+    "transformer_blocks.3.ff_context.net.2.bias": [3072],
+    "transformer_blocks.3.ff_context.net.2.weight": [3072, 12288],
+    "transformer_blocks.3.norm1.linear.bias": [18432],
+    "transformer_blocks.3.norm1.linear.weight": [18432, 3072],
+    "transformer_blocks.3.norm1_context.linear.bias": [18432],
+    "transformer_blocks.3.norm1_context.linear.weight": [18432, 3072],
+    "transformer_blocks.4.attn.add_k_proj.bias": [3072],
+    "transformer_blocks.4.attn.add_k_proj.weight": [3072, 3072],
+    "transformer_blocks.4.attn.add_q_proj.bias": [3072],
+    "transformer_blocks.4.attn.add_q_proj.weight": [3072, 3072],
+    "transformer_blocks.4.attn.add_v_proj.bias": [3072],
+    "transformer_blocks.4.attn.add_v_proj.weight": [3072, 3072],
+    "transformer_blocks.4.attn.norm_added_k.weight": [128],
+    "transformer_blocks.4.attn.norm_added_q.weight": [128],
+    "transformer_blocks.4.attn.norm_k.weight": [128],
+    "transformer_blocks.4.attn.norm_q.weight": [128],
+    "transformer_blocks.4.attn.to_add_out.bias": [3072],
+    "transformer_blocks.4.attn.to_add_out.weight": [3072, 3072],
+    "transformer_blocks.4.attn.to_k.bias": [3072],
+    "transformer_blocks.4.attn.to_k.weight": [3072, 3072],
+    "transformer_blocks.4.attn.to_out.0.bias": [3072],
+    "transformer_blocks.4.attn.to_out.0.weight": [3072, 3072],
+    "transformer_blocks.4.attn.to_q.bias": [3072],
+    "transformer_blocks.4.attn.to_q.weight": [3072, 3072],
+    "transformer_blocks.4.attn.to_v.bias": [3072],
+    "transformer_blocks.4.attn.to_v.weight": [3072, 3072],
+    "transformer_blocks.4.ff.net.0.proj.bias": [12288],
+    "transformer_blocks.4.ff.net.0.proj.weight": [12288, 3072],
+    "transformer_blocks.4.ff.net.2.bias": [3072],
+    "transformer_blocks.4.ff.net.2.weight": [3072, 12288],
+    "transformer_blocks.4.ff_context.net.0.proj.bias": [12288],
+    "transformer_blocks.4.ff_context.net.0.proj.weight": [12288, 3072],
+    "transformer_blocks.4.ff_context.net.2.bias": [3072],
+    "transformer_blocks.4.ff_context.net.2.weight": [3072, 12288],
+    "transformer_blocks.4.norm1.linear.bias": [18432],
+    "transformer_blocks.4.norm1.linear.weight": [18432, 3072],
+    "transformer_blocks.4.norm1_context.linear.bias": [18432],
+    "transformer_blocks.4.norm1_context.linear.weight": [18432, 3072],
+    "x_embedder.bias": [3072],
+    "x_embedder.weight": [3072, 64],
+}
+
+
+# InstantX FLUX ControlNet config for unit tests.
+# Copied from https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union/blob/main/config.json
+instantx_config = {
+    "_class_name": "FluxControlNetModel",
+    "_diffusers_version": "0.30.0.dev0",
+    "_name_or_path": "/mnt/wangqixun/",
+    "attention_head_dim": 128,
+    "axes_dims_rope": [16, 56, 56],
+    "guidance_embeds": True,
+    "in_channels": 64,
+    "joint_attention_dim": 4096,
+    "num_attention_heads": 24,
+    "num_layers": 5,
+    "num_mode": 10,
+    "num_single_layers": 10,
+    "patch_size": 1,
+    "pooled_projection_dim": 768,
+}

tests/backend/flux/controlnet/test_state_dict_utils.py

@@ -0,0 +1,108 @@
+import sys
+
+import pytest
+import torch
+
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
+from invokeai.backend.flux.controlnet.state_dict_utils import (
+    convert_diffusers_instantx_state_dict_to_bfl_format,
+    infer_flux_params_from_state_dict,
+    infer_instantx_num_control_modes_from_state_dict,
+    is_state_dict_instantx_controlnet,
+    is_state_dict_xlabs_controlnet,
+)
+from tests.backend.flux.controlnet.instantx_flux_controlnet_state_dict import instantx_config, instantx_sd_shapes
+from tests.backend.flux.controlnet.xlabs_flux_controlnet_state_dict import xlabs_sd_shapes
+
+
+@pytest.mark.parametrize(
+    ["sd_shapes", "expected"],
+    [
+        (xlabs_sd_shapes, True),
+        (instantx_sd_shapes, False),
+        (["foo"], False),
+    ],
+)
+def test_is_state_dict_xlabs_controlnet(sd_shapes: dict[str, list[int]], expected: bool):
+    sd = {k: None for k in sd_shapes}
+    assert is_state_dict_xlabs_controlnet(sd) == expected
+
+
+@pytest.mark.parametrize(
+    ["sd_keys", "expected"],
+    [
+        (instantx_sd_shapes, True),
+        (xlabs_sd_shapes, False),
+        (["foo"], False),
+    ],
+)
+def test_is_state_dict_instantx_controlnet(sd_keys: list[str], expected: bool):
+    sd = {k: None for k in sd_keys}
+    assert is_state_dict_instantx_controlnet(sd) == expected
+
+
+def test_convert_diffusers_instantx_state_dict_to_bfl_format():
+    """Smoke test convert_diffusers_instantx_state_dict_to_bfl_format() to ensure that it handles all of the keys."""
+    sd = {k: torch.zeros(1) for k in instantx_sd_shapes}
+    bfl_sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+    assert bfl_sd is not None
+
+
+# TODO(ryand): Figure out why some tests in this file are failing on the MacOS CI runners. It seems to be related to
+# using the meta device. I can't reproduce the issue on my local MacOS system.
+
+
+@pytest.mark.skipif(sys.platform == "darwin", reason="Skipping on macOS")
+def test_infer_flux_params_from_state_dict():
+    # Construct a dummy state_dict with tensors of the correct shape on the meta device.
+    with torch.device("meta"):
+        sd = {k: torch.zeros(v) for k, v in instantx_sd_shapes.items()}
+
+    sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+    flux_params = infer_flux_params_from_state_dict(sd)
+
+    assert flux_params.in_channels == instantx_config["in_channels"]
+    assert flux_params.vec_in_dim == instantx_config["pooled_projection_dim"]
+    assert flux_params.context_in_dim == instantx_config["joint_attention_dim"]
+    assert flux_params.hidden_size // flux_params.num_heads == instantx_config["attention_head_dim"]
+    assert flux_params.num_heads == instantx_config["num_attention_heads"]
+    assert flux_params.mlp_ratio == 4
+    assert flux_params.depth == instantx_config["num_layers"]
+    assert flux_params.depth_single_blocks == instantx_config["num_single_layers"]
+    assert flux_params.axes_dim == instantx_config["axes_dims_rope"]
+    assert flux_params.theta == 10000
+    assert flux_params.qkv_bias
+    assert flux_params.guidance_embed == instantx_config["guidance_embeds"]
+
+
+@pytest.mark.skipif(sys.platform == "darwin", reason="Skipping on macOS")
+def test_infer_instantx_num_control_modes_from_state_dict():
+    # Construct a dummy state_dict with tensors of the correct shape on the meta device.
+    with torch.device("meta"):
+        sd = {k: torch.zeros(v) for k, v in instantx_sd_shapes.items()}
+
+    sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+    num_control_modes = infer_instantx_num_control_modes_from_state_dict(sd)
+
+    assert num_control_modes == instantx_config["num_mode"]
+
+
+@pytest.mark.skipif(sys.platform == "darwin", reason="Skipping on macOS")
+def test_load_instantx_from_state_dict():
+    # Construct a dummy state_dict with tensors of the correct shape on the meta device.
+    with torch.device("meta"):
+        sd = {k: torch.zeros(v) for k, v in instantx_sd_shapes.items()}
+
+    sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+    flux_params = infer_flux_params_from_state_dict(sd)
+    num_control_modes = infer_instantx_num_control_modes_from_state_dict(sd)
+
+    with torch.device("meta"):
+        model = InstantXControlNetFlux(flux_params, num_control_modes)
+
+    model_sd = model.state_dict()
+
+    assert set(model_sd.keys()) == set(sd.keys())
+    for key, tensor in model_sd.items():
+        assert isinstance(tensor, torch.Tensor)
+        assert tensor.shape == sd[key].shape

tests/backend/flux/controlnet/xlabs_flux_controlnet_state_dict.py

@@ -0,0 +1,91 @@
+# State dict keys and shapes for an XLabs FLUX ControlNet model. Intended to be used for unit tests.
+# These keys were extracted from:
+# https://huggingface.co/XLabs-AI/flux-controlnet-collections/blob/86ab1e915a389d5857135c00e0d350e9e38a9048/flux-canny-controlnet_v2.safetensors
+xlabs_sd_shapes = {
+    "controlnet_blocks.0.bias": [3072],
+    "controlnet_blocks.0.weight": [3072, 3072],
+    "controlnet_blocks.1.bias": [3072],
+    "controlnet_blocks.1.weight": [3072, 3072],
+    "double_blocks.0.img_attn.norm.key_norm.scale": [128],
+    "double_blocks.0.img_attn.norm.query_norm.scale": [128],
+    "double_blocks.0.img_attn.proj.bias": [3072],
+    "double_blocks.0.img_attn.proj.weight": [3072, 3072],
+    "double_blocks.0.img_attn.qkv.bias": [9216],
+    "double_blocks.0.img_attn.qkv.weight": [9216, 3072],
+    "double_blocks.0.img_mlp.0.bias": [12288],
+    "double_blocks.0.img_mlp.0.weight": [12288, 3072],
+    "double_blocks.0.img_mlp.2.bias": [3072],
+    "double_blocks.0.img_mlp.2.weight": [3072, 12288],
+    "double_blocks.0.img_mod.lin.bias": [18432],
+    "double_blocks.0.img_mod.lin.weight": [18432, 3072],
+    "double_blocks.0.txt_attn.norm.key_norm.scale": [128],
+    "double_blocks.0.txt_attn.norm.query_norm.scale": [128],
+    "double_blocks.0.txt_attn.proj.bias": [3072],
+    "double_blocks.0.txt_attn.proj.weight": [3072, 3072],
+    "double_blocks.0.txt_attn.qkv.bias": [9216],
+    "double_blocks.0.txt_attn.qkv.weight": [9216, 3072],
+    "double_blocks.0.txt_mlp.0.bias": [12288],
+    "double_blocks.0.txt_mlp.0.weight": [12288, 3072],
+    "double_blocks.0.txt_mlp.2.bias": [3072],
+    "double_blocks.0.txt_mlp.2.weight": [3072, 12288],
+    "double_blocks.0.txt_mod.lin.bias": [18432],
+    "double_blocks.0.txt_mod.lin.weight": [18432, 3072],
+    "double_blocks.1.img_attn.norm.key_norm.scale": [128],
+    "double_blocks.1.img_attn.norm.query_norm.scale": [128],
+    "double_blocks.1.img_attn.proj.bias": [3072],
+    "double_blocks.1.img_attn.proj.weight": [3072, 3072],
+    "double_blocks.1.img_attn.qkv.bias": [9216],
+    "double_blocks.1.img_attn.qkv.weight": [9216, 3072],
+    "double_blocks.1.img_mlp.0.bias": [12288],
+    "double_blocks.1.img_mlp.0.weight": [12288, 3072],
+    "double_blocks.1.img_mlp.2.bias": [3072],
+    "double_blocks.1.img_mlp.2.weight": [3072, 12288],
+    "double_blocks.1.img_mod.lin.bias": [18432],
+    "double_blocks.1.img_mod.lin.weight": [18432, 3072],
+    "double_blocks.1.txt_attn.norm.key_norm.scale": [128],
+    "double_blocks.1.txt_attn.norm.query_norm.scale": [128],
+    "double_blocks.1.txt_attn.proj.bias": [3072],
+    "double_blocks.1.txt_attn.proj.weight": [3072, 3072],
+    "double_blocks.1.txt_attn.qkv.bias": [9216],
+    "double_blocks.1.txt_attn.qkv.weight": [9216, 3072],
+    "double_blocks.1.txt_mlp.0.bias": [12288],
+    "double_blocks.1.txt_mlp.0.weight": [12288, 3072],
+    "double_blocks.1.txt_mlp.2.bias": [3072],
+    "double_blocks.1.txt_mlp.2.weight": [3072, 12288],
+    "double_blocks.1.txt_mod.lin.bias": [18432],
+    "double_blocks.1.txt_mod.lin.weight": [18432, 3072],
+    "guidance_in.in_layer.bias": [3072],
+    "guidance_in.in_layer.weight": [3072, 256],
+    "guidance_in.out_layer.bias": [3072],
+    "guidance_in.out_layer.weight": [3072, 3072],
+    "img_in.bias": [3072],
+    "img_in.weight": [3072, 64],
+    "input_hint_block.0.bias": [16],
+    "input_hint_block.0.weight": [16, 3, 3, 3],
+    "input_hint_block.10.bias": [16],
+    "input_hint_block.10.weight": [16, 16, 3, 3],
+    "input_hint_block.12.bias": [16],
+    "input_hint_block.12.weight": [16, 16, 3, 3],
+    "input_hint_block.14.bias": [16],
+    "input_hint_block.14.weight": [16, 16, 3, 3],
+    "input_hint_block.2.bias": [16],
+    "input_hint_block.2.weight": [16, 16, 3, 3],
+    "input_hint_block.4.bias": [16],
+    "input_hint_block.4.weight": [16, 16, 3, 3],
+    "input_hint_block.6.bias": [16],
+    "input_hint_block.6.weight": [16, 16, 3, 3],
+    "input_hint_block.8.bias": [16],
+    "input_hint_block.8.weight": [16, 16, 3, 3],
+    "pos_embed_input.bias": [3072],
+    "pos_embed_input.weight": [3072, 64],
+    "time_in.in_layer.bias": [3072],
+    "time_in.in_layer.weight": [3072, 256],
+    "time_in.out_layer.bias": [3072],
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+}