pipelines/flux_pipeline/transformer.py

# https://github.com/bghira/SimpleTuner/blob/d0b5f37913a80aabdb0cac893937072dfa3e6a4b/helpers/models/flux/transformer.py#L404
# Copyright 2024 Stability AI, The HuggingFace Team, The InstantX Team, and Terminus Research Group. All rights reserved.
#
# Originally licensed under the Apache License, Version 2.0 (the "License");
# Updated to "Affero GENERAL PUBLIC LICENSE Version 3, 19 November 2007" via extensive updates to attn_mask usage.

import math
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from peft.tuners.lora.layer import LoraLayer

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from diffusers.models.attention import FeedForward
from diffusers.models.attention_processor import Attention, AttentionProcessor
from diffusers.models.embeddings import (
    CombinedTimestepGuidanceTextProjEmbeddings,
    CombinedTimestepTextProjEmbeddings,
    FluxPosEmbed,
)
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import (
    AdaLayerNormContinuous,
    AdaLayerNormZero,
    AdaLayerNormZeroSingle,
)
from diffusers.utils import (
    USE_PEFT_BACKEND,
    is_torch_version,
    logging,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import maybe_allow_in_graph

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

def log_scale_masking(value, min_value=1, max_value=10):
    # Convert the value into a positive domain for the logarithmic function
    normalized_value = 1*value

    # Apply logarithmic scaling
    # log_scaled_value = 1-np.exp(-normalized_value)
    log_scaled_value = 2.0* math.log(normalized_value+1, 2) / math.log(2, 2)  # np.log1p(x) = log(1 + x)
    # print(log_scaled_value)

    # Rescale to original range
    scaled_value = log_scaled_value * (max_value - min_value) + min_value

    return min(max_value, int(scaled_value))

class FluxAttnProcessor2_0:
    """Attention processor used typically in processing the SD3-like self-attention projections."""

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )
        self.name = None

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
        shared_attn: bool=False, num=2,
        mode="a", 
        ref_dict: dict = None,
        single: bool=False,
        scale: float = 1.0,
        timestep: float = 0,
        val: bool = False,
    ) -> torch.FloatTensor:
        if mode == 'w': # and single:
            ref_dict[self.name] = hidden_states.detach()
        
        batch_size, _, _ = (
            hidden_states.shape
            if encoder_hidden_states is None
            else encoder_hidden_states.shape
        )
        end_of_hidden_states = hidden_states.shape[1]
        text_seq = 512
        mask = None
        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
        if encoder_hidden_states is not None:
            # `context` projections.
            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

            if attn.norm_added_q is not None:
                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
            if attn.norm_added_k is not None:
                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

            # attention
            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
        
        if image_rotary_emb is not None:
            from diffusers.models.embeddings import apply_rotary_emb
            query = apply_rotary_emb(query, image_rotary_emb)
            key = apply_rotary_emb(key, image_rotary_emb)

        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False, attn_mask=mask if timestep < 1. else None)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        hidden_states = hidden_states.to(query.dtype)

        if encoder_hidden_states is not None:
            encoder_hidden_states, hidden_states = (
                hidden_states[:, : encoder_hidden_states.shape[1]],
                hidden_states[:, encoder_hidden_states.shape[1] : ],
            )
            hidden_states = hidden_states[:, :end_of_hidden_states]

            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)
            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
            return hidden_states, encoder_hidden_states
        else:
            return hidden_states[:, :end_of_hidden_states]


def expand_flux_attention_mask(
    hidden_states: torch.Tensor,
    attn_mask: torch.Tensor,
) -> torch.Tensor:
    """
    Expand a mask so that the image is included.
    """
    bsz = attn_mask.shape[0]
    assert bsz == hidden_states.shape[0]
    residual_seq_len = hidden_states.shape[1]
    mask_seq_len = attn_mask.shape[1]

    expanded_mask = torch.ones(bsz, residual_seq_len)
    expanded_mask[:, :mask_seq_len] = attn_mask

    return expanded_mask


@maybe_allow_in_graph
class FluxSingleTransformerBlock(nn.Module):
    def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
        super().__init__()
        self.mlp_hidden_dim = int(dim * mlp_ratio)

        self.norm = AdaLayerNormZeroSingle(dim)
        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
        self.act_mlp = nn.GELU(approximate="tanh")
        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)

        processor = FluxAttnProcessor2_0()
        # processor = FluxSingleAttnProcessor3_0()

        self.attn = Attention(
            query_dim=dim,
            cross_attention_dim=None,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            bias=True,
            processor=processor,
            qk_norm="rms_norm",
            eps=1e-6,
            pre_only=True,
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: torch.FloatTensor,
        image_rotary_emb=None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        residual = hidden_states
        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))

        attn_output = self.attn(
            hidden_states=norm_hidden_states,
            image_rotary_emb=image_rotary_emb,
            **joint_attention_kwargs,
            single=True,
        )

        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
        gate = gate.unsqueeze(1)
        hidden_states = gate * self.proj_out(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


@maybe_allow_in_graph
class FluxTransformerBlock(nn.Module):
    def __init__(
        self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6
    ):
        super().__init__()

        self.norm1 = AdaLayerNormZero(dim)

        self.norm1_context = AdaLayerNormZero(dim)

        if hasattr(F, "scaled_dot_product_attention"):
            processor = FluxAttnProcessor2_0()
        else:
            raise ValueError(
                "The current PyTorch version does not support the `scaled_dot_product_attention` function."
            )
        self.attn = Attention(
            query_dim=dim,
            cross_attention_dim=None,
            added_kv_proj_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            context_pre_only=False,
            bias=True,
            processor=processor,
            qk_norm=qk_norm,
            eps=eps,
        )

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

        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff_context = FeedForward(
            dim=dim, dim_out=dim, activation_fn="gelu-approximate"
        )

        # let chunk size default to None
        self._chunk_size = None
        self._chunk_dim = 0

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
        temb: torch.FloatTensor,
        image_rotary_emb=None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None
    ):
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (self.norm1_context(encoder_hidden_states, emb=temb))

        # Attention.
        attn_output, context_attn_output = self.attn(
            hidden_states=norm_hidden_states,
            encoder_hidden_states=norm_encoder_hidden_states,
            image_rotary_emb=image_rotary_emb,
            **joint_attention_kwargs,
            single=False,
        )

        # Process attention outputs for the `hidden_states`.
        attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = hidden_states + attn_output

        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = (norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None])

        ff_output = self.ff(norm_hidden_states)
        ff_output = gate_mlp.unsqueeze(1) * ff_output

        hidden_states = hidden_states + ff_output

        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
        encoder_hidden_states = encoder_hidden_states + context_attn_output

        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
        norm_encoder_hidden_states = (
            norm_encoder_hidden_states * (1 + c_scale_mlp[:, None])
            + c_shift_mlp[:, None]
        )

        context_ff_output = self.ff_context(norm_encoder_hidden_states)
        encoder_hidden_states = (
            encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
        )

        return encoder_hidden_states, hidden_states


@contextmanager
def set_adapter_scale(model, alpha):
    original_scaling = {}
    for module in model.modules():
        if isinstance(module, LoraLayer):
            original_scaling[module] = module.scaling.copy()
            module.scaling = {k: v * alpha for k, v in module.scaling.items()}

    # check whether scaling is prohibited on model
    # the original scaling dictionary should be empty
    # if there were no lora layers
    if not original_scaling:
        raise ValueError("scaling is only supported for models with `LoraLayer`s")
    try:
        yield

    finally:
        # restore original scaling values after exiting the context
        for module, scaling in original_scaling.items():
            module.scaling = scaling
            
class FluxTransformer2DModelWithMasking(
    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin
):
    """
    The Transformer model introduced in Flux.

    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

    Parameters:
        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 MMDiT blocks to use.
        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT 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.
        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
    """

    _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: Tuple[int] = (16, 56, 56),
        ##
    ):
        super().__init__()
        self.out_channels = in_channels
        self.inner_dim = (
            self.config.num_attention_heads * self.config.attention_head_dim
        )

        self.pos_embed = FluxPosEmbed(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=self.config.pooled_projection_dim,
        )

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

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

        self.single_transformer_blocks = nn.ModuleList(
            [
                FluxSingleTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                )
                for i in range(self.config.num_single_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

    @property
    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
    
    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)

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        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 [`FluxTransformer2DModelWithMasking`] 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)

        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)

        if txt_ids.ndim == 3:
            txt_ids = txt_ids[0]
        if img_ids.ndim == 3:
            img_ids = img_ids[0]

        
        # txt_ids = torch.zeros((1024,3)).to(txt_ids.device, dtype=txt_ids.dtype)
        ids = torch.cat((txt_ids, img_ids), dim=0)

        image_rotary_emb = self.pos_embed(ids)

        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 = (
                    torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        hidden_states,
                        encoder_hidden_states,
                        temb,
                        image_rotary_emb,
                        joint_attention_kwargs,
                        **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,
                    joint_attention_kwargs=joint_attention_kwargs,
                )

        # Flux places the text tokens in front of the image tokens in the
        # sequence.
        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        for index_block, 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,
                    joint_attention_kwargs,
                    **ckpt_kwargs,
                )

            else:
                hidden_states = block(
                    hidden_states=hidden_states,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
                    joint_attention_kwargs=joint_attention_kwargs,
                )

        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]

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

        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__":
    dtype = torch.bfloat16
    bsz = 2
    img = torch.rand((bsz, 16, 64, 64)).to("cuda", dtype=dtype)
    timestep = torch.tensor([0.5, 0.5]).to("cuda", dtype=torch.float32)
    pooled = torch.rand(bsz, 768).to("cuda", dtype=dtype)
    text = torch.rand((bsz, 512, 4096)).to("cuda", dtype=dtype)
    attn_mask = torch.tensor([[1.0] * 384 + [0.0] * 128] * bsz).to(
        "cuda", dtype=dtype
    )  # Last 128 positions are masked

    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
        latents = latents.view(
            batch_size, num_channels_latents, height // 2, 2, width // 2, 2
        )
        latents = latents.permute(0, 2, 4, 1, 3, 5)
        latents = latents.reshape(
            batch_size, (height // 2) * (width // 2), num_channels_latents * 4
        )

        return latents

    def _prepare_latent_image_ids(
        batch_size, height, width, device="cuda", dtype=dtype
    ):
        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
        latent_image_ids[..., 1] = (
            latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
        )
        latent_image_ids[..., 2] = (
            latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
        )

        latent_image_id_height, latent_image_id_width, latent_image_id_channels = (
            latent_image_ids.shape
        )

        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
        latent_image_ids = latent_image_ids.reshape(
            batch_size,
            latent_image_id_height * latent_image_id_width,
            latent_image_id_channels,
        )

        return latent_image_ids.to(device=device, dtype=dtype)

    txt_ids = torch.zeros(bsz, text.shape[1], 3).to(device="cuda", dtype=dtype)

    vae_scale_factor = 16
    height = 2 * (int(512) // vae_scale_factor)
    width = 2 * (int(512) // vae_scale_factor)
    img_ids = _prepare_latent_image_ids(bsz, height, width)
    img = _pack_latents(img, img.shape[0], 16, height, width)

    # Gotta go fast
    transformer = FluxTransformer2DModelWithMasking.from_config(
        {
            "attention_head_dim": 128,
            "guidance_embeds": True,
            "in_channels": 64,
            "joint_attention_dim": 4096,
            "num_attention_heads": 24,
            "num_layers": 4,
            "num_single_layers": 8,
            "patch_size": 1,
            "pooled_projection_dim": 768,
        }
    ).to("cuda", dtype=dtype)

    guidance = torch.tensor([2.0], device="cuda")
    guidance = guidance.expand(bsz)

    with torch.no_grad():
        no_mask = transformer(
            img,
            encoder_hidden_states=text,
            pooled_projections=pooled,
            timestep=timestep,
            img_ids=img_ids,
            txt_ids=txt_ids,
            guidance=guidance,
        )
        mask = transformer(
            img,
            encoder_hidden_states=text,
            pooled_projections=pooled,
            timestep=timestep,
            img_ids=img_ids,
            txt_ids=txt_ids,
            guidance=guidance,
            attention_mask=attn_mask,
        )

    assert torch.allclose(no_mask.sample, mask.sample) is False
    print("Attention masking test ran OK. Differences in output were detected.")