controlnet_flux.py

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.attention_processor import AttentionProcessor
from diffusers.utils import (
    USE_PEFT_BACKEND,
    is_torch_version,
    logging,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.models.controlnet import BaseOutput, zero_module
from diffusers.models.embeddings import (
    CombinedTimestepGuidanceTextProjEmbeddings,
    CombinedTimestepTextProjEmbeddings,
)
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from transformer_flux import (
    EmbedND,
    FluxSingleTransformerBlock,
    FluxTransformerBlock,
)


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class FluxControlNetOutput(BaseOutput):
    controlnet_block_samples: Tuple[torch.Tensor]
    controlnet_single_block_samples: Tuple[torch.Tensor]


class FluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(

        self,

        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: List[int] = [16, 56, 56],

        extra_condition_channels: int = 1 * 4,

    ):
        super().__init__()
        self.out_channels = in_channels
        self.inner_dim = num_attention_heads * attention_head_dim

        self.pos_embed = EmbedND(
            dim=self.inner_dim, theta=10000, axes_dim=axes_dims_rope
        )
        text_time_guidance_cls = (
            CombinedTimestepGuidanceTextProjEmbeddings
            if guidance_embeds
            else CombinedTimestepTextProjEmbeddings
        )
        self.time_text_embed = text_time_guidance_cls(
            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
        )

        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
        self.x_embedder = nn.Linear(in_channels, self.inner_dim)

        self.transformer_blocks = nn.ModuleList(
            [
                FluxTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                )
                for _ in range(num_layers)
            ]
        )

        self.single_transformer_blocks = nn.ModuleList(
            [
                FluxSingleTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                )
                for _ in range(num_single_layers)
            ]
        )

        # controlnet_blocks
        self.controlnet_blocks = nn.ModuleList([])
        for _ in range(len(self.transformer_blocks)):
            self.controlnet_blocks.append(
                zero_module(nn.Linear(self.inner_dim, self.inner_dim))
            )

        self.controlnet_single_blocks = nn.ModuleList([])
        for _ in range(len(self.single_transformer_blocks)):
            self.controlnet_single_blocks.append(
                zero_module(nn.Linear(self.inner_dim, self.inner_dim))
            )

        self.controlnet_x_embedder = zero_module(
            torch.nn.Linear(in_channels + extra_condition_channels, self.inner_dim)
        )

        self.gradient_checkpointing = False

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self):
        r"""

        Returns:

            `dict` of attention processors: A dictionary containing all attention processors used in the model with

            indexed by its weight name.

        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor()

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor):
        r"""

        Sets the attention processor to use to compute attention.



        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.



        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    @classmethod
    def from_transformer(

        cls,

        transformer,

        num_layers: int = 4,

        num_single_layers: int = 10,

        attention_head_dim: int = 128,

        num_attention_heads: int = 24,

        load_weights_from_transformer=True,

    ):
        config = transformer.config
        config["num_layers"] = num_layers
        config["num_single_layers"] = num_single_layers
        config["attention_head_dim"] = attention_head_dim
        config["num_attention_heads"] = num_attention_heads

        controlnet = cls(**config)

        if load_weights_from_transformer:
            controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
            controlnet.time_text_embed.load_state_dict(
                transformer.time_text_embed.state_dict()
            )
            controlnet.context_embedder.load_state_dict(
                transformer.context_embedder.state_dict()
            )
            controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
            controlnet.transformer_blocks.load_state_dict(
                transformer.transformer_blocks.state_dict(), strict=False
            )
            controlnet.single_transformer_blocks.load_state_dict(
                transformer.single_transformer_blocks.state_dict(), strict=False
            )

            controlnet.controlnet_x_embedder = zero_module(
                controlnet.controlnet_x_embedder
            )

        return controlnet

    def forward(

        self,

        hidden_states: torch.Tensor,

        controlnet_cond: torch.Tensor,

        conditioning_scale: float = 1.0,

        encoder_hidden_states: torch.Tensor = None,

        pooled_projections: torch.Tensor = None,

        timestep: torch.LongTensor = None,

        img_ids: torch.Tensor = None,

        txt_ids: torch.Tensor = None,

        guidance: torch.Tensor = None,

        joint_attention_kwargs: Optional[Dict[str, Any]] = None,

        return_dict: bool = True,

    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
        """

        The [`FluxTransformer2DModel`] forward method.



        Args:

            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):

                Input `hidden_states`.

            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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

            return_dict (`bool`, *optional*, defaults to `True`):

                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain

                tuple.



        Returns:

            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a

            `tuple` where the first element is the sample tensor.

        """
        if joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()
            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)
        else:
            if (
                joint_attention_kwargs is not None
                and joint_attention_kwargs.get("scale", None) is not None
            ):
                logger.warning(
                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
                )
        hidden_states = self.x_embedder(hidden_states)

        # add condition
        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)

        timestep = timestep.to(hidden_states.dtype) * 1000
        if guidance is not None:
            guidance = guidance.to(hidden_states.dtype) * 1000
        else:
            guidance = None
        temb = (
            self.time_text_embed(timestep, pooled_projections)
            if guidance is None
            else self.time_text_embed(timestep, guidance, pooled_projections)
        )
        encoder_hidden_states = self.context_embedder(encoder_hidden_states)

        txt_ids = txt_ids.expand(img_ids.size(0), -1, -1)
        ids = torch.cat((txt_ids, img_ids), dim=1)
        image_rotary_emb = self.pos_embed(ids)

        block_samples = ()
        for _, block in enumerate(self.transformer_blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = (
                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                )
                (
                    encoder_hidden_states,
                    hidden_states,
                ) = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    image_rotary_emb,
                    **ckpt_kwargs,
                )

            else:
                encoder_hidden_states, hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
                )
            block_samples = block_samples + (hidden_states,)

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        single_block_samples = ()
        for _, block in enumerate(self.single_transformer_blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = (
                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                )
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    temb,
                    image_rotary_emb,
                    **ckpt_kwargs,
                )

            else:
                hidden_states = block(
                    hidden_states=hidden_states,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
                )
            single_block_samples = single_block_samples + (
                hidden_states[:, encoder_hidden_states.shape[1] :],
            )

        # controlnet block
        controlnet_block_samples = ()
        for block_sample, controlnet_block in zip(
            block_samples, self.controlnet_blocks
        ):
            block_sample = controlnet_block(block_sample)
            controlnet_block_samples = controlnet_block_samples + (block_sample,)

        controlnet_single_block_samples = ()
        for single_block_sample, controlnet_block in zip(
            single_block_samples, self.controlnet_single_blocks
        ):
            single_block_sample = controlnet_block(single_block_sample)
            controlnet_single_block_samples = controlnet_single_block_samples + (
                single_block_sample,
            )

        # scaling
        controlnet_block_samples = [
            sample * conditioning_scale for sample in controlnet_block_samples
        ]
        controlnet_single_block_samples = [
            sample * conditioning_scale for sample in controlnet_single_block_samples
        ]

        #
        controlnet_block_samples = (
            None if len(controlnet_block_samples) == 0 else controlnet_block_samples
        )
        controlnet_single_block_samples = (
            None
            if len(controlnet_single_block_samples) == 0
            else controlnet_single_block_samples
        )

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (controlnet_block_samples, controlnet_single_block_samples)

        return FluxControlNetOutput(
            controlnet_block_samples=controlnet_block_samples,
            controlnet_single_block_samples=controlnet_single_block_samples,
        )