[authored by @Anghellia) Add support of Xlabs Controlnets #9638

src/diffusers/models/controlnet_flux.py

@@ -23,7 +23,7 @@
 from ..models.attention_processor import AttentionProcessor
 from ..models.modeling_utils import ModelMixin
 from ..utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
-from .controlnet import BaseOutput, zero_module
+from .controlnet import BaseOutput, ControlNetConditioningEmbedding, zero_module
 from .embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
 from .modeling_outputs import Transformer2DModelOutput
 from .transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
@@ -55,6 +55,7 @@ def __init__(
         guidance_embeds: bool = False,
         axes_dims_rope: List[int] = [16, 56, 56],
         num_mode: int = None,
+        conditioning_embedding_channels: int = None,
     ):
         super().__init__()
         self.out_channels = in_channels
@@ -106,7 +107,14 @@ def __init__(
         if self.union:
             self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
 
-        self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
+        if conditioning_embedding_channels is not None:
+            self.input_hint_block = ControlNetConditioningEmbedding(
+                conditioning_embedding_channels=conditioning_embedding_channels, block_out_channels=(16, 16, 16, 16)
+            )
+            self.controlnet_x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
+        else:
+            self.input_hint_block = None
+            self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
 
         self.gradient_checkpointing = False
 
@@ -269,6 +277,16 @@ def forward(
                 )
         hidden_states = self.x_embedder(hidden_states)
 
+        if self.input_hint_block is not None:
+            controlnet_cond = self.input_hint_block(controlnet_cond)
+            batch_size, channels, height_pw, width_pw = controlnet_cond.shape
+            height = height_pw // self.config.patch_size
+            width = width_pw // self.config.patch_size
+            controlnet_cond = controlnet_cond.reshape(
+                batch_size, channels, height, self.config.patch_size, width, self.config.patch_size
+            )
+            controlnet_cond = controlnet_cond.permute(0, 2, 4, 1, 3, 5)
+            controlnet_cond = controlnet_cond.reshape(batch_size, height * width, -1)
         # add
         hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
 

src/diffusers/models/transformers/transformer_flux.py

@@ -402,6 +402,7 @@ def forward(
         controlnet_block_samples=None,
         controlnet_single_block_samples=None,
         return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
     ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
         """
         The [`FluxTransformer2DModel`] forward method.
@@ -508,7 +509,13 @@ def custom_forward(*inputs):
             if controlnet_block_samples is not None:
                 interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
                 interval_control = int(np.ceil(interval_control))
-                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
 
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
 

src/diffusers/pipelines/flux/pipeline_flux_controlnet.py

@@ -752,19 +752,22 @@ def __call__(
             )
             height, width = control_image.shape[-2:]
 
-            # vae encode
-            control_image = self.vae.encode(control_image).latent_dist.sample()
-            control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-            # pack
-            height_control_image, width_control_image = control_image.shape[2:]
-            control_image = self._pack_latents(
-                control_image,
-                batch_size * num_images_per_prompt,
-                num_channels_latents,
-                height_control_image,
-                width_control_image,
-            )
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.input_hint_block is None else True
+            if self.controlnet.input_hint_block is None:
+                # vae encode
+                control_image = self.vae.encode(control_image).latent_dist.sample()
+                control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                # pack
+                height_control_image, width_control_image = control_image.shape[2:]
+                control_image = self._pack_latents(
+                    control_image,
+                    batch_size * num_images_per_prompt,
+                    num_channels_latents,
+                    height_control_image,
+                    width_control_image,
+                )
 
             # Here we ensure that `control_mode` has the same length as the control_image.
             if control_mode is not None:
@@ -775,8 +778,9 @@ def __call__(
 
         elif isinstance(self.controlnet, FluxMultiControlNetModel):
             control_images = []
-
-            for control_image_ in control_image:
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.nets[0].input_hint_block is None else True
+            for i, control_image_ in enumerate(control_image):
                 control_image_ = self.prepare_image(
                     image=control_image_,
                     width=width,
@@ -788,20 +792,20 @@ def __call__(
                 )
                 height, width = control_image_.shape[-2:]
 
-                # vae encode
-                control_image_ = self.vae.encode(control_image_).latent_dist.sample()
-                control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-                # pack
-                height_control_image, width_control_image = control_image_.shape[2:]
-                control_image_ = self._pack_latents(
-                    control_image_,
-                    batch_size * num_images_per_prompt,
-                    num_channels_latents,
-                    height_control_image,
-                    width_control_image,
-                )
-
+                if self.controlnet.nets[0].input_hint_block is None:
+                    # vae encode
+                    control_image_ = self.vae.encode(control_image_).latent_dist.sample()
+                    control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                    # pack
+                    height_control_image, width_control_image = control_image_.shape[2:]
+                    control_image_ = self._pack_latents(
+                        control_image_,
+                        batch_size * num_images_per_prompt,
+                        num_channels_latents,
+                        height_control_image,
+                        width_control_image,
+                    )
                 control_images.append(control_image_)
 
             control_image = control_images
@@ -925,6 +929,7 @@ def __call__(
                     img_ids=latent_image_ids,
                     joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
+                    controlnet_blocks_repeat=controlnet_blocks_repeat,
                 )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1