unipicv2/transformer_sd3_kontext.py

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 FromOriginalModelMixin, PeftAdapterMixin, SD3Transformer2DLoadersMixin
from diffusers.models.attention import FeedForward, JointTransformerBlock
from diffusers.models.attention_processor import (
    Attention,
    AttentionProcessor,
    FusedJointAttnProcessor2_0,
    JointAttnProcessor2_0,
)
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero
from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from diffusers.models.modeling_outputs import Transformer2DModelOutput


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


@maybe_allow_in_graph
class SD3SingleTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
    ):
        super().__init__()

        self.norm1 = AdaLayerNormZero(dim)
        self.attn = Attention(
            query_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            bias=True,
            processor=JointAttnProcessor2_0(),
            eps=1e-6,
        )

        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")

    def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor):
        # 1. Attention
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
        attn_output = self.attn(hidden_states=norm_hidden_states, encoder_hidden_states=None)
        attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = hidden_states + attn_output

        # 2. Feed Forward
        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
        ff_output = self.ff(norm_hidden_states)
        ff_output = gate_mlp.unsqueeze(1) * ff_output
        hidden_states = hidden_states + ff_output

        return hidden_states


class SD3Transformer2DKontextModel(
    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, SD3Transformer2DLoadersMixin
):
    """
    The Transformer model introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206).

    Parameters:
        sample_size (`int`, defaults to `128`):
            The width/height of the latents. This is fixed during training since it is used to learn a number of
            position embeddings.
        patch_size (`int`, defaults to `2`):
            Patch size to turn the input data into small patches.
        in_channels (`int`, defaults to `16`):
            The number of latent channels in the input.
        num_layers (`int`, defaults to `18`):
            The number of layers of transformer blocks to use.
        attention_head_dim (`int`, defaults to `64`):
            The number of channels in each head.
        num_attention_heads (`int`, defaults to `18`):
            The number of heads to use for multi-head attention.
        joint_attention_dim (`int`, defaults to `4096`):
            The embedding dimension to use for joint text-image attention.
        caption_projection_dim (`int`, defaults to `1152`):
            The embedding dimension of caption embeddings.
        pooled_projection_dim (`int`, defaults to `2048`):
            The embedding dimension of pooled text projections.
        out_channels (`int`, defaults to `16`):
            The number of latent channels in the output.
        pos_embed_max_size (`int`, defaults to `96`):
            The maximum latent height/width of positional embeddings.
        dual_attention_layers (`Tuple[int, ...]`, defaults to `()`):
            The number of dual-stream transformer blocks to use.
        qk_norm (`str`, *optional*, defaults to `None`):
            The normalization to use for query and key in the attention layer. If `None`, no normalization is used.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["JointTransformerBlock"]
    _skip_layerwise_casting_patterns = ["pos_embed", "norm"]

    @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,
        dual_attention_layers: Tuple[
            int, ...
        ] = (),  # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
        qk_norm: Optional[str] = None,
    ):
        super().__init__()
        self.out_channels = out_channels if out_channels is not None else in_channels
        self.inner_dim = num_attention_heads * attention_head_dim

        self.pos_embed = PatchEmbed(
            height=sample_size,
            width=sample_size,
            patch_size=patch_size,
            in_channels=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=pooled_projection_dim
        )
        self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)

        self.transformer_blocks = nn.ModuleList(
            [
                JointTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    context_pre_only=i == num_layers - 1,
                    qk_norm=qk_norm,
                    use_dual_attention=True if i in dual_attention_layers else False,
                )
                for i in range(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

    # 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 forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor = None,
        ref_hidden_states: torch.Tensor = None,
        pooled_projections: torch.Tensor = None,
        timestep: torch.LongTensor = None,
        block_controlnet_hidden_states: List = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
        skip_layers: Optional[List[int]] = None,
    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
        """
        The [`SD3Transformer2DModel`] forward method.

        Args:
            hidden_states (`torch.Tensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            ref_hidden_states (`torch.Tensor` of shape `(batch size, channel, height, width)`):
                Input `ref_hidden_states`.
            encoder_hidden_states (`torch.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
            pooled_projections (`torch.Tensor` 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.
            skip_layers (`list` of `int`, *optional*):
                A list of layer indices to skip during the forward pass.

        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)  # takes care of adding positional embeddings too.
        if ref_hidden_states is not None:
            ref_hidden_states = self.pos_embed(ref_hidden_states)
            assert ref_hidden_states.shape == hidden_states.shape
            hidden_states = torch.cat([ref_hidden_states, hidden_states], dim=1)

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

        if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
            ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
            ip_hidden_states, ip_temb = self.image_proj(ip_adapter_image_embeds, timestep)

            joint_attention_kwargs.update(ip_hidden_states=ip_hidden_states, temb=ip_temb)

        for index_block, block in enumerate(self.transformer_blocks):
            # Skip specified layers
            is_skip = True if skip_layers is not None and index_block in skip_layers else False

            if torch.is_grad_enabled() and self.gradient_checkpointing and not is_skip:
                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
                    block,
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    joint_attention_kwargs,
                )
            elif not is_skip:
                encoder_hidden_states, hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=temb,
                    joint_attention_kwargs=joint_attention_kwargs,
                )

            # 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[int(index_block / interval_control)]

        patch_size = self.config.patch_size
        height = height // patch_size
        width = width // patch_size
        hidden_states = hidden_states[:, -height*width:, :]

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

        # unpatchify
        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)


if __name__ == "__main__":
    import torch
    import argparse
    import os

    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint", type=str, default=None)
    parser.add_argument("--output", type=str, default=None)

    args = parser.parse_args()

    pretrained_model_name_or_path = "stabilityai/stable-diffusion-3.5-medium"

    transformer = SD3Transformer2DKontextModel.from_pretrained(
        pretrained_model_name_or_path=pretrained_model_name_or_path,
        subfolder="transformer",
        torch_dtype=torch.bfloat16)

    checkpoint = torch.load(args.checkpoint)
    checkpoint = {k[len('transformer.'):]: v for k, v in checkpoint.items() if 'transformer.' in k}

    transformer.load_state_dict(checkpoint)

    os.makedirs(args.output, exist_ok=True)

    transformer.save_pretrained(args.output)