#
FluxControlNetModel

FluxControlNetModel is an implementation of ControlNet for Flux.1.

The ControlNet model was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.

The abstract from the paper is:

We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with “zero convolutions” (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

##
Loading from the original format

By default the FluxControlNetModel should be loaded with from_pretrained().

    from diffusers import FluxControlNetPipeline
    from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel

    controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
    pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)

    controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
    controlnet = FluxMultiControlNetModel([controlnet])
    pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)

##
FluxControlNetModel

###
class diffusers.FluxControlNetModel

< source >

( patch_size: int = 1 in_channels: int = 64 num_layers: int = 19 num_single_layers: int = 38 attention_head_dim: int = 128 num_attention_heads: int = 24 joint_attention_dim: int = 4096 pooled_projection_dim: int = 768 guidance_embeds: bool = False axes_dims_rope: typing.List[int] = [16, 56, 56] num_mode: int = None conditioning_embedding_channels: int = None )

####
forward

< source >

( hidden_states: Tensor controlnet_cond: Tensor controlnet_mode: Tensor = None conditioning_scale: float = 1.0 encoder_hidden_states: Tensor = None pooled_projections: Tensor = None timestep: LongTensor = None img_ids: Tensor = None txt_ids: Tensor = None guidance: Tensor = None joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None return_dict: bool = True )

Parameters

- hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`) — Input `hidden_states`.
- controlnet_cond (`torch.Tensor`) — The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
- controlnet_mode (`torch.Tensor`) — The mode tensor of shape `(batch_size, 1)`.
- conditioning_scale (`float`, defaults to `1.0`) — The scale factor for ControlNet outputs.
- encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`) — Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
- pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`) — Embeddings projected from the embeddings of input conditions.
- timestep ( `torch.LongTensor`) — Used to indicate denoising step.
- block_controlnet_hidden_states — (`list` of `torch.Tensor`): A list of tensors that if specified are added to the residuals of transformer blocks.
- joint_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain tuple.

The FluxTransformer2DModel forward method.

####
set_attn_processor

< source >

( processor )

Parameters

- processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`) — The instantiated processor class or a dictionary of processor classes that will be set as the processor for all `Attention` layers.

  If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.

Sets the attention processor to use to compute attention.

##
FluxControlNetOutput

###
class diffusers.models.controlnet_flux.FluxControlNetOutput

< source >

( *args **kwargs )

Update on GitHub