src/models/transformer_sd3_SiamLayout.py

# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from typing import Any, Dict, List, Optional, Union

import torch
import torch.nn as nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from src.models.attention_SiamLayout import SiamLayoutJointTransformerBlock
from diffusers.models.attention_processor import Attention, AttentionProcessor, FusedJointAttnProcessor2_0
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormContinuous
from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.activations import FP32SiLU, get_activation
logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

def get_fourier_embeds_from_boundingbox(embed_dim, box):
    """
    Args:
        embed_dim: int
        box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline
    Returns:
        [B x N x embed_dim] tensor of positional embeddings
    """

    batch_size, num_boxes = box.shape[:2]

    emb = 100 ** (torch.arange(embed_dim) / embed_dim)
    emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)
    emb = emb * box.unsqueeze(-1)

    emb = torch.stack((emb.sin(), emb.cos()), dim=-1)
    emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)

    return emb

class PixArtAlphaTextProjection(nn.Module):
    """
    Projects caption embeddings. Also handles dropout for classifier-free guidance.

    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
    """

    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh"):
        super().__init__()
        if out_features is None:
            out_features = hidden_size
        self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
        if act_fn == "gelu_tanh":
            self.act_1 = nn.GELU(approximate="tanh")
        elif act_fn == "silu":
            self.act_1 = nn.SiLU()
        elif act_fn == "silu_fp32":
            self.act_1 = FP32SiLU()
        else:
            raise ValueError(f"Unknown activation function: {act_fn}")
        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)

    def forward(self, caption):
        hidden_states = self.linear_1(caption)
        hidden_states = self.act_1(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states

class TextBoundingboxProjection(nn.Module):
    def __init__(self, pooled_projection_dim,positive_len, out_dim, fourier_freqs=8):
        super().__init__()
        self.positive_len = positive_len
        self.out_dim = out_dim

        self.fourier_embedder_dim = fourier_freqs
        self.position_dim = fourier_freqs * 2 * 4  # 2: sin/cos, 4: xyxy #64

        if isinstance(out_dim, tuple):
            out_dim = out_dim[0]

        self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, positive_len, act_fn="silu")
        self.linears = PixArtAlphaTextProjection(in_features=self.positive_len + self.position_dim,hidden_size=out_dim//2,out_features=out_dim, act_fn="silu")
        self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
        self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))

    def forward(
        self,
        boxes,
        masks,
        positive_embeddings, 
        phrases_masks=None,
        image_masks=None,
        phrases_embeddings=None,
        image_embeddings=None,
    ):
        
        masks = masks.unsqueeze(-1) 

        # embedding position (it may includes padding as placeholder)
        xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes) 

        # learnable null embedding
        xyxy_null = self.null_position_feature.view(1, 1, -1) 

        # replace padding with learnable null embedding
        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null 

        # learnable null embedding
        positive_null = self.null_positive_feature.view(1, 1, -1)

        positive_embeddings = self.text_embedder(positive_embeddings) 

        # replace padding with learnable null embedding
        positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null 

        objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))


        return objs



class SiamLayoutSD3Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
    """
    The Transformer model introduced in Stable Diffusion 3.

    Reference: https://arxiv.org/abs/2403.03206

    Parameters:
        sample_size (`int`): The width of the latent images. This is fixed during training since
            it is used to learn a number of position embeddings.
        patch_size (`int`): Patch size to turn the input data into small patches.
        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
        num_layers (`int`, *optional*, defaults to 18): The number of layers of Transformer blocks to use.
        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
        out_channels (`int`, defaults to 16): Number of output channels.

    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: int = 128,
        patch_size: int = 2,
        in_channels: int = 16,
        num_layers: int = 18,
        attention_head_dim: int = 64,
        num_attention_heads: int = 18,
        joint_attention_dim: int = 4096,
        caption_projection_dim: int = 1152,
        pooled_projection_dim: int = 2048,
        out_channels: int = 16,
        pos_embed_max_size: int = 96,
        attention_type = "layout",
        max_boxes_per_image =10
    ):
        super().__init__()
        default_out_channels = in_channels
        self.out_channels = out_channels if out_channels is not None else default_out_channels
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim

        self.pos_embed = PatchEmbed(
            height=self.config.sample_size,
            width=self.config.sample_size,
            patch_size=self.config.patch_size,
            in_channels=self.config.in_channels,
            embed_dim=self.inner_dim,
            pos_embed_max_size=pos_embed_max_size,  # hard-code for now.
        )
        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
        )
        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.caption_projection_dim)

        # `attention_head_dim` is doubled to account for the mixing.
        # It needs to crafted when we get the actual checkpoints.
        self.transformer_blocks = nn.ModuleList(
            [
                SiamLayoutJointTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                    context_pre_only=i == num_layers - 1,
                    attention_type=attention_type,
                    bbox_pre_only= i == num_layers - 1, 
                    bbox_with_temb= True, 
                )
                for i in range(self.config.num_layers)
            ]
        )


        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)

        self.gradient_checkpointing = False

        self.attention_type = attention_type
        self.max_boxes_per_image = max_boxes_per_image
        if self.attention_type == "layout":
            self.position_net = TextBoundingboxProjection(
                pooled_projection_dim=self.config.pooled_projection_dim,positive_len=self.inner_dim, out_dim=self.inner_dim
            )



    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, chunk_size, dim)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
    def disable_forward_chunking(self):
        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, None, 0)

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        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: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        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)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedJointAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

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

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        pooled_projections: torch.FloatTensor = None,
        timestep: torch.LongTensor = None,
        block_controlnet_hidden_states: List = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
        layout_kwargs = None,
        bbox_scale=1.0,
    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
        """
        The [`SD3Transformer2DModel`] 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."
                )
        
        height, width = hidden_states.shape[-2:]

        hidden_states = self.pos_embed(hidden_states)  
        temb = self.time_text_embed(timestep, pooled_projections) 
        encoder_hidden_states = self.context_embedder(encoder_hidden_states) 


        if self.attention_type=="layout" and layout_kwargs is not None and layout_kwargs.get("layout", None) is not None:
            
            layout_args = layout_kwargs["layout"]
            bbox_raw = layout_args["boxes"] 
            bbox_text_embeddings = layout_args["positive_embeddings"].to(dtype=hidden_states.dtype,device=hidden_states.device) 
            bbox_masks = layout_args["masks"] 
            bbox_hidden_states = self.position_net(boxes=bbox_raw,masks=bbox_masks,positive_embeddings=bbox_text_embeddings) 
            
        else:
            N = hidden_states.shape[0]
            bbox_hidden_states = torch.zeros(N, self.max_boxes_per_image,self.inner_dim, dtype=hidden_states.dtype, device=hidden_states.device)
            bbox_masks = torch.zeros(N, self.max_boxes_per_image, dtype=hidden_states.dtype, device=hidden_states.device)

        for index_block, 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,bbox_hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    bbox_hidden_states,
                    **ckpt_kwargs,
                )

            else:
                encoder_hidden_states, hidden_states,bbox_hidden_states = block(
                    hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb,bbox_hidden_states= bbox_hidden_states,bbox_scale=bbox_scale
                )

            # controlnet residual
            if block_controlnet_hidden_states is not None and block.context_pre_only is False:
                interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
                hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]

        hidden_states = self.norm_out(hidden_states, temb)
        hidden_states = self.proj_out(hidden_states)

        # unpatchify
        patch_size = self.config.patch_size
        height = height // patch_size
        width = width // patch_size

        hidden_states = hidden_states.reshape(
            shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
        )
        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
        )

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

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)