# Copied and adapted from: https://github.com/Tencent-Hunyuan/Hunyuan3D-2
from __future__ import annotations

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

from sglang.multimodal_gen.configs.models.dits.hunyuan3d import (
    Hunyuan3DDiTArchConfig,
    Hunyuan3DDiTConfig,
)
from sglang.multimodal_gen.runtime.distributed import divide
from sglang.multimodal_gen.runtime.distributed.parallel_state import get_tp_world_size
from sglang.multimodal_gen.runtime.layers.attention import LocalAttention
from sglang.multimodal_gen.runtime.layers.layernorm import (
    LayerNormScaleShift,
    ScaleResidualLayerNormScaleShift,
    apply_qk_norm,
)
from sglang.multimodal_gen.runtime.layers.linear import (
    MergedColumnParallelLinear,
    RowParallelLinear,
)
from sglang.multimodal_gen.runtime.layers.mlp import MLP
from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
    LayerwiseOffloadableModuleMixin,
)
from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

logger = init_logger(__name__)


def _fused_add_gate(
    residual: torch.Tensor, x: torch.Tensor, gate: torch.Tensor
) -> torch.Tensor:
    return torch.addcmul(residual, x, gate)


class MixedRowParallelLinear(RowParallelLinear):
    """RowParallel for inputs concatenated from multiple separately-sharded sources."""

    def __init__(self, input_sizes: list[int], output_size: int, **kwargs):
        self.input_sizes = input_sizes
        super().__init__(sum(input_sizes), output_size, **kwargs)

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
        input_dim = getattr(param, "input_dim", None)
        if input_dim is not None:
            shards = []
            offset = 0
            for sz in self.input_sizes:
                part = loaded_weight.narrow(input_dim, offset, sz)
                per_rank = sz // self.tp_size
                shard = part.narrow(input_dim, self.tp_rank * per_rank, per_rank)
                shards.append(shard)
                offset += sz
            param.data.copy_(torch.cat(shards, dim=input_dim))
        else:
            param.data.copy_(loaded_weight)


def _flux_timestep_embedding(
    t: torch.Tensor, dim, max_period=10000, time_factor: float = 1000.0
):
    """Create sinusoidal timestep embeddings for Flux-style model."""
    t = time_factor * t
    half = dim // 2
    freqs = torch.exp(
        -math.log(max_period)
        * torch.arange(start=0, end=half, dtype=torch.float32)
        / half
    ).to(t.device)

    args = t[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    if torch.is_floating_point(t):
        embedding = embedding.to(t)
    return embedding


class _FluxGELU(nn.Module):
    def __init__(self, approximate="tanh"):
        super().__init__()
        self.approximate = approximate

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return F.gelu(x, approximate=self.approximate)


class _FluxMLPEmbedder(nn.Module):
    def __init__(self, in_dim: int, hidden_dim: int):
        super().__init__()
        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
        self.silu = nn.SiLU()
        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.out_layer(self.silu(self.in_layer(x)))


class _FluxRMSNorm(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.scale = nn.Parameter(torch.ones(dim))
        self.variance_epsilon = 1e-6
        self.hidden_size = dim

    @property
    def weight(self) -> nn.Parameter:
        # Keep the original checkpoint key (`scale`) while exposing the
        # interface expected by the fused QK-norm helper.
        return self.scale

    def forward(self, x: torch.Tensor):
        x_dtype = x.dtype
        x = x.float()
        rrms = torch.rsqrt(
            torch.mean(x**2, dim=-1, keepdim=True) + self.variance_epsilon
        )
        return (x * rrms).to(dtype=x_dtype) * self.scale


class _FluxQKNorm(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.dim = dim
        self.query_norm = _FluxRMSNorm(dim)
        self.key_norm = _FluxRMSNorm(dim)

    def forward(
        self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        q, k = apply_qk_norm(
            q=q.contiguous(),
            k=k.contiguous(),
            q_norm=self.query_norm,
            k_norm=self.key_norm,
            head_dim=self.dim,
            allow_inplace=True,
        )
        return q.to(v), k.to(v)


class _FluxSelfAttention(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = False,
        supported_attention_backends: set[AttentionBackendEnum] | None = None,
    ):
        super().__init__()
        tp_size = get_tp_world_size()
        self.num_heads = num_heads
        self.local_num_heads = divide(num_heads, tp_size)
        self.head_dim = dim // num_heads

        self.qkv = MergedColumnParallelLinear(
            dim, [dim, dim, dim], bias=qkv_bias, gather_output=False
        )
        self.norm = _FluxQKNorm(self.head_dim)
        self.proj = RowParallelLinear(dim, dim, bias=True, input_is_parallel=True)

        if supported_attention_backends is None:
            supported_attention_backends = {
                AttentionBackendEnum.FA,
                AttentionBackendEnum.TORCH_SDPA,
            }
        self.local_attn = LocalAttention(
            num_heads=self.local_num_heads,
            head_size=self.head_dim,
            causal=False,
            supported_attention_backends=supported_attention_backends,
        )

    def forward(self, x: torch.Tensor, pe: torch.Tensor) -> torch.Tensor:
        qkv, _ = self.qkv(x)
        B, L, _ = qkv.shape
        qkv = qkv.view(B, L, 3, self.local_num_heads, self.head_dim)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v_for_norm = v.transpose(1, 2)
        q, k = self.norm(q, k, v_for_norm)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        x = self.local_attn(q, k, v)
        x = x.flatten(2)
        x, _ = self.proj(x)
        return x


@dataclass
class _FluxModulationOut:
    shift: torch.Tensor
    scale: torch.Tensor
    gate: torch.Tensor


class _FluxModulation(nn.Module):
    def __init__(self, dim: int, double: bool):
        super().__init__()
        self.is_double = double
        self.multiplier = 6 if double else 3
        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)

    def forward(
        self, vec: torch.Tensor
    ) -> Tuple[_FluxModulationOut, Optional[_FluxModulationOut]]:
        out = self.lin(F.silu(vec))[:, None, :]
        out = out.chunk(self.multiplier, dim=-1)

        return (
            _FluxModulationOut(*out[:3]),
            _FluxModulationOut(*out[3:]) if self.is_double else None,
        )


class _FluxDoubleStreamBlock(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        mlp_ratio: float,
        qkv_bias: bool = False,
        supported_attention_backends: set[AttentionBackendEnum] | None = None,
    ):
        super().__init__()
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        tp_size = get_tp_world_size()
        self.num_heads = num_heads
        self.local_num_heads = divide(num_heads, tp_size)
        self.hidden_size = hidden_size
        self.head_dim = hidden_size // num_heads
        self.img_mod = _FluxModulation(hidden_size, double=True)
        self.img_norm1 = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )
        self.img_attn = _FluxSelfAttention(
            dim=hidden_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            supported_attention_backends=supported_attention_backends,
        )

        self.img_norm2 = ScaleResidualLayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )
        self.img_mlp = MLP(hidden_size, mlp_hidden_dim, act_type="gelu_pytorch_tanh")

        self.txt_mod = _FluxModulation(hidden_size, double=True)
        self.txt_norm1 = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )
        self.txt_attn = _FluxSelfAttention(
            dim=hidden_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            supported_attention_backends=supported_attention_backends,
        )

        self.txt_norm2 = ScaleResidualLayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )
        self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, act_type="gelu_pytorch_tanh")

        if supported_attention_backends is None:
            supported_attention_backends = {
                AttentionBackendEnum.FA,
                AttentionBackendEnum.TORCH_SDPA,
            }
        self.local_attn_joint = LocalAttention(
            num_heads=self.local_num_heads,
            head_size=self.head_dim,
            causal=False,
            supported_attention_backends=supported_attention_backends,
        )

    def forward(
        self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        img_mod1, img_mod2 = self.img_mod(vec)
        txt_mod1, txt_mod2 = self.txt_mod(vec)

        img_modulated = self.img_norm1(img, shift=img_mod1.shift, scale=img_mod1.scale)

        B, img_L, _ = img_modulated.shape
        img_qkv, _ = self.img_attn.qkv(img_modulated)
        img_qkv = img_qkv.view(B, img_L, 3, self.local_num_heads, self.head_dim)
        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :, 2]
        img_q_t = img_q.transpose(1, 2)
        img_k_t = img_k.transpose(1, 2)
        img_v_t = img_v.transpose(1, 2)
        img_q_t, img_k_t = self.img_attn.norm(img_q_t, img_k_t, img_v_t)
        img_q = img_q_t.transpose(1, 2)
        img_k = img_k_t.transpose(1, 2)

        txt_modulated = self.txt_norm1(txt, shift=txt_mod1.shift, scale=txt_mod1.scale)
        txt_L = txt_modulated.shape[1]
        txt_qkv, _ = self.txt_attn.qkv(txt_modulated)
        txt_qkv = txt_qkv.view(B, txt_L, 3, self.local_num_heads, self.head_dim)
        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :, 2]
        txt_q_t = txt_q.transpose(1, 2)
        txt_k_t = txt_k.transpose(1, 2)
        txt_v_t = txt_v.transpose(1, 2)
        txt_q_t, txt_k_t = self.txt_attn.norm(txt_q_t, txt_k_t, txt_v_t)
        txt_q = txt_q_t.transpose(1, 2)
        txt_k = txt_k_t.transpose(1, 2)

        q = torch.cat((txt_q, img_q), dim=1)
        k = torch.cat((txt_k, img_k), dim=1)
        v = torch.cat((txt_v, img_v), dim=1)

        attn = self.local_attn_joint(q, k, v)
        attn = attn.flatten(2)

        txt_attn, img_attn = attn[:, :txt_L], attn[:, txt_L:]

        img_proj, _ = self.img_attn.proj(img_attn)
        img_modulated, img = self.img_norm2(
            residual=img,
            x=img_proj,
            gate=img_mod1.gate,
            shift=img_mod2.shift,
            scale=img_mod2.scale,
        )
        img = _fused_add_gate(img, self.img_mlp(img_modulated), img_mod2.gate)

        txt_proj, _ = self.txt_attn.proj(txt_attn)
        txt_modulated, txt = self.txt_norm2(
            residual=txt,
            x=txt_proj,
            gate=txt_mod1.gate,
            shift=txt_mod2.shift,
            scale=txt_mod2.scale,
        )
        txt = _fused_add_gate(txt, self.txt_mlp(txt_modulated), txt_mod2.gate)
        return img, txt


class _FluxSingleStreamBlock(nn.Module):
    """
    A DiT block with parallel linear layers as described in
    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
    """

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        qk_scale: Optional[float] = None,
        supported_attention_backends: set[AttentionBackendEnum] | None = None,
    ):
        super().__init__()

        tp_size = get_tp_world_size()
        self.hidden_dim = hidden_size
        self.num_heads = num_heads
        self.local_num_heads = divide(num_heads, tp_size)
        self.head_dim = hidden_size // num_heads
        self.tp_size = tp_size

        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.linear1 = MergedColumnParallelLinear(
            hidden_size,
            [hidden_size, hidden_size, hidden_size, self.mlp_hidden_dim],
            bias=True,
            gather_output=False,
        )
        self.linear2 = MixedRowParallelLinear(
            [hidden_size, self.mlp_hidden_dim],
            hidden_size,
            bias=True,
            input_is_parallel=True,
        )

        self.norm = _FluxQKNorm(self.head_dim)

        self.hidden_size = hidden_size
        self.pre_norm = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )

        self.mlp_act = _FluxGELU(approximate="tanh")
        self.modulation = _FluxModulation(hidden_size, double=False)

        if supported_attention_backends is None:
            supported_attention_backends = {
                AttentionBackendEnum.FA,
                AttentionBackendEnum.TORCH_SDPA,
            }
        self.local_attn = LocalAttention(
            num_heads=self.local_num_heads,
            head_size=self.head_dim,
            causal=False,
            supported_attention_backends=supported_attention_backends,
        )

    def forward(
        self, x: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
    ) -> torch.Tensor:
        mod, _ = self.modulation(vec)

        x_mod = self.pre_norm(x, shift=mod.shift, scale=mod.scale)
        linear1_out, _ = self.linear1(x_mod)
        local_qkv_dim = 3 * self.head_dim * self.local_num_heads
        local_mlp_dim = self.mlp_hidden_dim // self.tp_size
        qkv, mlp = torch.split(linear1_out, [local_qkv_dim, local_mlp_dim], dim=-1)

        B, L, _ = qkv.shape
        qkv = qkv.view(B, L, 3, self.local_num_heads, self.head_dim)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
        q_t = q.transpose(1, 2)
        k_t = k.transpose(1, 2)
        v_t = v.transpose(1, 2)
        q_t, k_t = self.norm(q_t, k_t, v_t)
        q = q_t.transpose(1, 2)
        k = k_t.transpose(1, 2)

        attn = self.local_attn(q, k, v)
        attn = attn.flatten(2)

        output, _ = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
        return _fused_add_gate(x, output, mod.gate)


class _FluxLastLayer(nn.Module):
    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
        super().__init__()
        self.norm_final = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, eps=1e-6
        )
        self.linear = nn.Linear(
            hidden_size, patch_size * patch_size * out_channels, bias=True
        )
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
        )

    def forward(self, x: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
        x = self.norm_final(x, shift=shift[:, None, :], scale=scale[:, None, :])
        x = self.linear(x)
        return x


class Hunyuan3D2DiT(CachableDiT, LayerwiseOffloadableModuleMixin):
    """Hunyuan3D DiT model (Flux-style architecture for Hunyuan3D-2.0)."""

    _aliases = ["hy3dgen.shapegen.models.Hunyuan3DDiT"]

    param_names_mapping = Hunyuan3DDiTConfig().param_names_mapping

    @classmethod
    def build_config_from_params(cls, params: dict) -> Hunyuan3DDiTConfig:
        """Build a DiTConfig from YAML-style parameter dict."""
        field_mapping = {
            "num_heads": "num_attention_heads",
            "depth": "num_layers",
            "depth_single_blocks": "num_single_layers",
        }
        arch_kwargs = {}
        for k, v in params.items():
            if k in ("ckpt_path", "supported_attention_backends"):
                continue
            mapped = field_mapping.get(k, k)
            if k == "axes_dim" and isinstance(v, list):
                v = tuple(v)
            arch_kwargs[mapped] = v
        return Hunyuan3DDiTConfig(arch_config=Hunyuan3DDiTArchConfig(**arch_kwargs))

    def __init__(
        self,
        config: Hunyuan3DDiTConfig,
        hf_config: dict | None = None,
        **kwargs,
    ):
        super().__init__(config=config, hf_config=hf_config or {}, **kwargs)
        arch = config.arch_config

        in_channels = arch.in_channels
        context_in_dim = arch.context_in_dim
        hidden_size = arch.hidden_size
        mlp_ratio = arch.mlp_ratio
        num_heads = arch.num_attention_heads
        depth = arch.num_layers
        depth_single_blocks = arch.num_single_layers
        axes_dim = list(arch.axes_dim)
        theta = arch.theta
        qkv_bias = arch.qkv_bias
        time_factor = arch.time_factor
        guidance_embed = arch.guidance_embed
        supported_attention_backends = arch._supported_attention_backends

        self.in_channels = in_channels
        self.context_in_dim = context_in_dim
        self.hidden_size = hidden_size
        self.mlp_ratio = mlp_ratio
        self.num_heads = num_heads
        self.num_attention_heads = num_heads
        self.depth = depth
        self.depth_single_blocks = depth_single_blocks
        self.axes_dim = axes_dim
        self.theta = theta
        self.qkv_bias = qkv_bias
        self.time_factor = time_factor
        self.out_channels = self.in_channels
        self.num_channels_latents = self.in_channels
        self.guidance_embed = guidance_embed

        if hidden_size % num_heads != 0:
            raise ValueError(
                f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}"
            )
        pe_dim = hidden_size // num_heads
        if sum(axes_dim) != pe_dim:
            raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
        self.latent_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
        self.time_in = _FluxMLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
        self.cond_in = nn.Linear(context_in_dim, self.hidden_size)
        self.guidance_in = (
            _FluxMLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
            if guidance_embed
            else nn.Identity()
        )

        self.double_blocks = nn.ModuleList(
            [
                _FluxDoubleStreamBlock(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    supported_attention_backends=supported_attention_backends,
                )
                for _ in range(depth)
            ]
        )

        self.single_blocks = nn.ModuleList(
            [
                _FluxSingleStreamBlock(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=mlp_ratio,
                    supported_attention_backends=supported_attention_backends,
                )
                for _ in range(depth_single_blocks)
            ]
        )

        self.final_layer = _FluxLastLayer(self.hidden_size, 1, self.out_channels)

        # LayerwiseOffloadableModuleMixin
        self.layer_names = ["double_blocks", "single_blocks"]

    def forward(
        self,
        x,
        t,
        contexts,
        **kwargs,
    ) -> torch.Tensor:
        """Forward pass for denoising."""

        cond = contexts["main"]

        latent = self.latent_in(x)

        t_emb = _flux_timestep_embedding(t, 256, self.time_factor).to(
            dtype=latent.dtype
        )

        vec = self.time_in(t_emb)

        if self.guidance_embed:
            guidance = kwargs.get("guidance", None)
            if guidance is None:
                raise ValueError(
                    "Didn't get guidance strength for guidance distilled model."
                )
            vec = vec + self.guidance_in(
                _flux_timestep_embedding(guidance, 256, self.time_factor)
            )

        cond = self.cond_in(cond)

        pe = None

        # Double blocks
        for i, block in enumerate(self.double_blocks):
            latent, cond = block(img=latent, txt=cond, vec=vec, pe=pe)
        latent = torch.cat((cond, latent), 1)

        # Single blocks
        for i, block in enumerate(self.single_blocks):
            latent = block(latent, vec=vec, pe=pe)

        latent = latent[:, cond.shape[1] :, ...]
        latent = self.final_layer(latent, vec)
        return latent


import copy
import json
import os as _os

from diffusers.models import UNet2DConditionModel
from diffusers.models.attention_processor import Attention as DiffusersAttention
from diffusers.models.transformers.transformer_2d import BasicTransformerBlock


def _chunked_feed_forward(
    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int
):
    """Feed forward with chunking to save memory."""
    if hidden_states.shape[chunk_dim] % chunk_size != 0:
        raise ValueError(
            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}"
            f"has to be divisible by chunk size: {chunk_size}."
            f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
        )

    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
    ff_output = torch.cat(
        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
        dim=chunk_dim,
    )
    return ff_output


class SGLangAttentionWrapper(torch.nn.Module):
    """Drop-in replacement for DiffusersAttention that uses sglang's attention backend."""

    _SUPPORTED_BACKENDS = {AttentionBackendEnum.FA, AttentionBackendEnum.TORCH_SDPA}

    def __init__(
        self,
        query_dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        cross_attention_dim: int | None = None,
        out_bias: bool = True,
    ) -> None:
        super().__init__()
        self.inner_dim = dim_head * heads
        self.heads = heads
        self.dim_head = dim_head
        self.query_dim = query_dim
        cross_attention_dim = cross_attention_dim or query_dim

        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
        self.to_k = nn.Linear(cross_attention_dim, self.inner_dim, bias=bias)
        self.to_v = nn.Linear(cross_attention_dim, self.inner_dim, bias=bias)
        self.to_out = nn.ModuleList(
            [nn.Linear(self.inner_dim, query_dim, bias=out_bias), nn.Dropout(dropout)]
        )

        from sglang.multimodal_gen.runtime.layers.attention.backends.attention_backend import (
            wrap_attention_impl_forward,
        )
        from sglang.multimodal_gen.runtime.layers.attention.selector import (
            get_attn_backend,
        )

        attn_backend = get_attn_backend(
            dim_head, torch.float16, self._SUPPORTED_BACKENDS
        )
        impl_cls = attn_backend.get_impl_cls()
        self.attn_impl = impl_cls(
            num_heads=heads,
            head_size=dim_head,
            softmax_scale=dim_head**-0.5,
            num_kv_heads=heads,
            causal=False,
        )
        wrap_attention_impl_forward(self.attn_impl)
        self._attn_backend_name = attn_backend.get_enum().name

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor:
        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states

        B, N_q, _ = hidden_states.shape
        _, N_kv, _ = encoder_hidden_states.shape

        q = self.to_q(hidden_states).view(B, N_q, self.heads, self.dim_head)
        k = self.to_k(encoder_hidden_states).view(B, N_kv, self.heads, self.dim_head)
        v = self.to_v(encoder_hidden_states).view(B, N_kv, self.heads, self.dim_head)

        from sglang.multimodal_gen.runtime.managers.forward_context import (
            get_forward_context,
        )

        ctx = get_forward_context()
        out = self.attn_impl.forward(q, k, v, attn_metadata=ctx.attn_metadata)
        out = out.reshape(B, N_q, self.inner_dim)

        out = self.to_out[0](out)
        out = self.to_out[1](out)
        return out


class Basic2p5DTransformerBlock(torch.nn.Module):
    """2.5D Transformer block with Multiview Attention (MVA) and Reference View Attention (RVA)."""

    def __init__(
        self,
        transformer: BasicTransformerBlock,
        layer_name: str,
        use_ma: bool = True,
        use_ra: bool = True,
        is_turbo: bool = False,
        use_sglang_attn: bool = True,
    ) -> None:
        super().__init__()
        self.transformer = transformer
        self.layer_name = layer_name
        self.use_ma = use_ma
        self.use_ra = use_ra
        self.is_turbo = is_turbo
        self.use_sglang_attn = use_sglang_attn and not is_turbo

        attn_cls = (
            SGLangAttentionWrapper if self.use_sglang_attn else DiffusersAttention
        )
        attn_kwargs = dict(
            query_dim=self.dim,
            heads=self.num_attention_heads,
            dim_head=self.attention_head_dim,
            dropout=self.dropout,
            bias=self.attention_bias,
            cross_attention_dim=None,
            upcast_attention=self.attn1.upcast_attention,
            out_bias=True,
        )
        if self.use_sglang_attn:
            attn_kwargs.pop("upcast_attention")

        if self.use_ma:
            self.attn_multiview = attn_cls(**attn_kwargs)

        if self.use_ra:
            self.attn_refview = attn_cls(**attn_kwargs)

        if self.is_turbo:
            self._initialize_attn_weights()

    def _initialize_attn_weights(self):
        """Initialize attention weights for turbo mode."""
        if self.use_ma:
            self.attn_multiview.load_state_dict(self.attn1.state_dict())
            with torch.no_grad():
                for layer in self.attn_multiview.to_out:
                    for param in layer.parameters():
                        param.zero_()
        if self.use_ra:
            self.attn_refview.load_state_dict(self.attn1.state_dict())
            with torch.no_grad():
                for layer in self.attn_refview.to_out:
                    for param in layer.parameters():
                        param.zero_()

    def __getattr__(self, name: str):
        try:
            return super().__getattr__(name)
        except AttributeError:
            return getattr(self.transformer, name)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: dict = None,
        class_labels: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Optional[dict] = None,
    ) -> torch.Tensor:
        """Forward pass with MVA and RVA support."""
        batch_size = hidden_states.shape[0]

        cross_attention_kwargs = (
            cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
        )
        num_in_batch = cross_attention_kwargs.pop("num_in_batch", 1)
        mode = cross_attention_kwargs.pop("mode", None)

        if not self.is_turbo:
            mva_scale = cross_attention_kwargs.pop("mva_scale", 1.0)
            ref_scale = cross_attention_kwargs.pop("ref_scale", 1.0)
        else:
            position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None)
            position_voxel_indices = cross_attention_kwargs.pop(
                "position_voxel_indices", None
            )
            mva_scale = 1.0
            ref_scale = 1.0

        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)

        # Normalization
        if self.norm_type == "ada_norm":
            norm_hidden_states = self.norm1(hidden_states, timestep)
        elif self.norm_type == "ada_norm_zero":
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
            )
        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
            norm_hidden_states = self.norm1(hidden_states)
        elif self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm1(
                hidden_states, added_cond_kwargs["pooled_text_emb"]
            )
        elif self.norm_type == "ada_norm_single":
            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
            ).chunk(6, dim=1)
            norm_hidden_states = self.norm1(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
        else:
            raise ValueError("Incorrect norm used")

        if self.pos_embed is not None:
            norm_hidden_states = self.pos_embed(norm_hidden_states)

        # Prepare GLIGEN inputs
        cross_attention_kwargs = (
            cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
        )
        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)

        # Self-attention
        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=(
                encoder_hidden_states if self.only_cross_attention else None
            ),
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        if self.norm_type == "ada_norm_zero":
            attn_output = gate_msa.unsqueeze(1) * attn_output
        elif self.norm_type == "ada_norm_single":
            attn_output = gate_msa * attn_output

        hidden_states = attn_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        # Reference Attention - Write mode
        if mode is not None and "w" in mode:
            condition_embed_dict[self.layer_name] = rearrange(
                norm_hidden_states, "(b n) l c -> b (n l) c", n=num_in_batch
            )

        # Reference Attention - Read mode
        if mode is not None and "r" in mode and self.use_ra:
            condition_embed = (
                condition_embed_dict[self.layer_name]
                .unsqueeze(1)
                .repeat(1, num_in_batch, 1, 1)
            )
            condition_embed = rearrange(condition_embed, "b n l c -> (b n) l c")

            attn_output = self.attn_refview(
                norm_hidden_states,
                encoder_hidden_states=condition_embed,
                attention_mask=None,
                **cross_attention_kwargs,
            )

            if not self.is_turbo:
                ref_scale_timing = ref_scale
                if isinstance(ref_scale, torch.Tensor):
                    ref_scale_timing = (
                        ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
                    )
                    for _ in range(attn_output.ndim - 1):
                        ref_scale_timing = ref_scale_timing.unsqueeze(-1)

            hidden_states = ref_scale_timing * attn_output + hidden_states

            if hidden_states.ndim == 4:
                hidden_states = hidden_states.squeeze(1)

        # Multiview Attention
        if num_in_batch > 1 and self.use_ma:
            multivew_hidden_states = rearrange(
                norm_hidden_states, "(b n) l c -> b (n l) c", n=num_in_batch
            )

            if self.is_turbo:
                position_mask = None
                if position_attn_mask is not None:
                    if multivew_hidden_states.shape[1] in position_attn_mask:
                        position_mask = position_attn_mask[
                            multivew_hidden_states.shape[1]
                        ]
                position_indices = None
                if position_voxel_indices is not None:
                    if multivew_hidden_states.shape[1] in position_voxel_indices:
                        position_indices = position_voxel_indices[
                            multivew_hidden_states.shape[1]
                        ]
                attn_output = self.attn_multiview(
                    multivew_hidden_states,
                    encoder_hidden_states=multivew_hidden_states,
                    attention_mask=position_mask,
                    position_indices=position_indices,
                    **cross_attention_kwargs,
                )
            else:
                attn_output = self.attn_multiview(
                    multivew_hidden_states,
                    encoder_hidden_states=multivew_hidden_states,
                    **cross_attention_kwargs,
                )

            attn_output = rearrange(
                attn_output, "b (n l) c -> (b n) l c", n=num_in_batch
            )

            hidden_states = mva_scale * attn_output + hidden_states
            if hidden_states.ndim == 4:
                hidden_states = hidden_states.squeeze(1)

        # GLIGEN Control
        if gligen_kwargs is not None:
            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])

        # Cross-Attention
        if self.attn2 is not None:
            if self.norm_type == "ada_norm":
                norm_hidden_states = self.norm2(hidden_states, timestep)
            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
                norm_hidden_states = self.norm2(hidden_states)
            elif self.norm_type == "ada_norm_single":
                norm_hidden_states = hidden_states
            elif self.norm_type == "ada_norm_continuous":
                norm_hidden_states = self.norm2(
                    hidden_states, added_cond_kwargs["pooled_text_emb"]
                )
            else:
                raise ValueError("Incorrect norm")

            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
                norm_hidden_states = self.pos_embed(norm_hidden_states)

            attn_output = self.attn2(
                norm_hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                **cross_attention_kwargs,
            )

            hidden_states = attn_output + hidden_states

        # Feed-forward
        if self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm3(
                hidden_states, added_cond_kwargs["pooled_text_emb"]
            )
        elif not self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm3(hidden_states)

        if self.norm_type == "ada_norm_zero":
            norm_hidden_states = (
                norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
            )

        if self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm2(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp

        if self._chunk_size is not None:
            ff_output = _chunked_feed_forward(
                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size
            )
        else:
            ff_output = self.ff(norm_hidden_states)

        if self.norm_type == "ada_norm_zero":
            ff_output = gate_mlp.unsqueeze(1) * ff_output
        elif self.norm_type == "ada_norm_single":
            ff_output = gate_mlp * ff_output

        hidden_states = ff_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        return hidden_states


@torch.no_grad()
def compute_voxel_grid_mask(position: torch.Tensor, grid_resolution: int = 8):
    """Compute voxel grid mask for position-aware attention."""
    position = position.half()
    B, N, _, H, W = position.shape
    assert H % grid_resolution == 0 and W % grid_resolution == 0

    valid_mask = (position != 1).all(dim=2, keepdim=True)
    valid_mask = valid_mask.expand_as(position)
    position[valid_mask == False] = 0

    position = rearrange(
        position,
        "b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w",
        num_h=grid_resolution,
        num_w=grid_resolution,
    )
    valid_mask = rearrange(
        valid_mask,
        "b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w",
        num_h=grid_resolution,
        num_w=grid_resolution,
    )

    grid_position = position.sum(dim=(-2, -1))
    count_masked = valid_mask.sum(dim=(-2, -1))

    grid_position = grid_position / count_masked.clamp(min=1)
    grid_position[count_masked < 5] = 0

    grid_position = grid_position.permute(0, 1, 4, 2, 3)
    grid_position = rearrange(grid_position, "b n c h w -> b n (h w) c")

    grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4)
    grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3)

    distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1)

    weights = distances
    grid_distance = 1.73 / grid_resolution

    weights = weights < grid_distance

    return weights


def compute_multi_resolution_mask(
    position_maps: torch.Tensor, grid_resolutions: List[int] = [32, 16, 8]
) -> dict:
    """Compute multi-resolution position attention masks."""
    position_attn_mask = {}
    with torch.no_grad():
        for grid_resolution in grid_resolutions:
            position_mask = compute_voxel_grid_mask(position_maps, grid_resolution)
            position_mask = rearrange(
                position_mask, "b ni nj li lj -> b (ni li) (nj lj)"
            )
            position_attn_mask[position_mask.shape[1]] = position_mask
    return position_attn_mask


@torch.no_grad()
def compute_discrete_voxel_indice(
    position: torch.Tensor, grid_resolution: int = 8, voxel_resolution: int = 128
):
    """Compute discrete voxel indices for position encoding."""
    position = position.half()
    B, N, _, H, W = position.shape
    assert H % grid_resolution == 0 and W % grid_resolution == 0

    valid_mask = (position != 1).all(dim=2, keepdim=True)
    valid_mask = valid_mask.expand_as(position)
    position[valid_mask == False] = 0

    position = rearrange(
        position,
        "b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w",
        num_h=grid_resolution,
        num_w=grid_resolution,
    )
    valid_mask = rearrange(
        valid_mask,
        "b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w",
        num_h=grid_resolution,
        num_w=grid_resolution,
    )

    grid_position = position.sum(dim=(-2, -1))
    count_masked = valid_mask.sum(dim=(-2, -1))

    grid_position = grid_position / count_masked.clamp(min=1)
    grid_position[count_masked < 5] = 0

    grid_position = grid_position.permute(0, 1, 4, 2, 3).clamp(0, 1)
    voxel_indices = grid_position * (voxel_resolution - 1)
    voxel_indices = torch.round(voxel_indices).long()
    return voxel_indices


def compute_multi_resolution_discrete_voxel_indice(
    position_maps: torch.Tensor,
    grid_resolutions: List[int] = [64, 32, 16, 8],
    voxel_resolutions: List[int] = [512, 256, 128, 64],
) -> dict:
    """Compute multi-resolution discrete voxel indices."""
    voxel_indices = {}
    with torch.no_grad():
        for grid_resolution, voxel_resolution in zip(
            grid_resolutions, voxel_resolutions
        ):
            voxel_indice = compute_discrete_voxel_indice(
                position_maps, grid_resolution, voxel_resolution
            )
            voxel_indice = rearrange(voxel_indice, "b n c h w -> b (n h w) c")
            voxel_indices[voxel_indice.shape[1]] = {
                "voxel_indices": voxel_indice,
                "voxel_resolution": voxel_resolution,
            }
    return voxel_indices


class UNet2p5DConditionModel(torch.nn.Module):
    """2.5D UNet for multi-view texture generation."""

    def __init__(self, unet: UNet2DConditionModel) -> None:
        super().__init__()
        self.unet = unet

        self.use_ma = True
        self.use_ra = True
        self.use_camera_embedding = True
        self.use_dual_stream = True
        self.is_turbo = False

        if self.use_dual_stream:
            self.unet_dual = copy.deepcopy(unet)
            self.init_attention(self.unet_dual)
        self.init_attention(
            self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo
        )
        self.init_condition()
        self.init_camera_embedding()

    @staticmethod
    def from_pretrained(pretrained_model_name_or_path: str, **kwargs):
        """Load a pretrained UNet2p5DConditionModel."""
        torch_dtype = kwargs.pop("dtype", kwargs.pop("torch_dtype", torch.float32))
        config_path = _os.path.join(pretrained_model_name_or_path, "config.json")
        unet_ckpt_path = _os.path.join(
            pretrained_model_name_or_path, "diffusion_pytorch_model.bin"
        )

        with open(config_path, "r", encoding="utf-8") as file:
            config = json.load(file)

        unet = UNet2DConditionModel(**config)
        unet = UNet2p5DConditionModel(unet)
        unet_ckpt = torch.load(unet_ckpt_path, map_location="cpu", weights_only=True)
        unet.load_state_dict(unet_ckpt, strict=True)
        unet = unet.to(torch_dtype)
        return unet

    def init_condition(self):
        """Initialize condition-related modules."""
        self.unet.conv_in = torch.nn.Conv2d(
            12,  # 4 (latent) + 4 (normal) + 4 (position)
            self.unet.conv_in.out_channels,
            kernel_size=self.unet.conv_in.kernel_size,
            stride=self.unet.conv_in.stride,
            padding=self.unet.conv_in.padding,
            dilation=self.unet.conv_in.dilation,
            groups=self.unet.conv_in.groups,
            bias=self.unet.conv_in.bias is not None,
        )

        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024))
        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024))

    def init_camera_embedding(self):
        """Initialize camera embedding module."""
        if self.use_camera_embedding:
            time_embed_dim = 1280
            self.max_num_ref_image = 5
            self.max_num_gen_image = 12 * 3 + 4 * 2
            self.unet.class_embedding = nn.Embedding(
                self.max_num_ref_image + self.max_num_gen_image, time_embed_dim
            )

    def init_attention(
        self,
        unet: UNet2DConditionModel,
        use_ma: bool = False,
        use_ra: bool = False,
        is_turbo: bool = False,
        use_sglang_attn: bool = True,
    ):
        """Initialize attention blocks with MVA and RVA support."""
        block_kwargs = dict(
            use_ma=use_ma,
            use_ra=use_ra,
            is_turbo=is_turbo,
            use_sglang_attn=use_sglang_attn,
        )

        # Down blocks
        for down_block_i, down_block in enumerate(unet.down_blocks):
            if (
                hasattr(down_block, "has_cross_attention")
                and down_block.has_cross_attention
            ):
                for attn_i, attn in enumerate(down_block.attentions):
                    for transformer_i, transformer in enumerate(
                        attn.transformer_blocks
                    ):
                        if isinstance(transformer, BasicTransformerBlock):
                            attn.transformer_blocks[transformer_i] = (
                                Basic2p5DTransformerBlock(
                                    transformer,
                                    f"down_{down_block_i}_{attn_i}_{transformer_i}",
                                    **block_kwargs,
                                )
                            )

        # Mid block
        if (
            hasattr(unet.mid_block, "has_cross_attention")
            and unet.mid_block.has_cross_attention
        ):
            for attn_i, attn in enumerate(unet.mid_block.attentions):
                for transformer_i, transformer in enumerate(attn.transformer_blocks):
                    if isinstance(transformer, BasicTransformerBlock):
                        attn.transformer_blocks[transformer_i] = (
                            Basic2p5DTransformerBlock(
                                transformer,
                                f"mid_{attn_i}_{transformer_i}",
                                **block_kwargs,
                            )
                        )

        # Up blocks
        for up_block_i, up_block in enumerate(unet.up_blocks):
            if (
                hasattr(up_block, "has_cross_attention")
                and up_block.has_cross_attention
            ):
                for attn_i, attn in enumerate(up_block.attentions):
                    for transformer_i, transformer in enumerate(
                        attn.transformer_blocks
                    ):
                        if isinstance(transformer, BasicTransformerBlock):
                            attn.transformer_blocks[transformer_i] = (
                                Basic2p5DTransformerBlock(
                                    transformer,
                                    f"up_{up_block_i}_{attn_i}_{transformer_i}",
                                    **block_kwargs,
                                )
                            )

        if use_sglang_attn and (use_ma or use_ra):
            backend = "unknown"
            for block in self._iter_2p5d_blocks(unet):
                for attr in ("attn_multiview", "attn_refview"):
                    wrapper = getattr(block, attr, None)
                    if isinstance(wrapper, SGLangAttentionWrapper):
                        backend = wrapper._attn_backend_name
                        break
                if backend != "unknown":
                    break
            count = sum(1 for _ in self._iter_2p5d_blocks(unet))
            logger.info(
                "Initialized %d Basic2p5DTransformerBlocks with sglang %s attention",
                count,
                backend,
            )

    @staticmethod
    def _iter_2p5d_blocks(unet):
        """Yield all Basic2p5DTransformerBlock instances in a UNet."""
        for block_group in (unet.down_blocks, [unet.mid_block], unet.up_blocks):
            for block in block_group:
                if not hasattr(block, "attentions"):
                    continue
                for attn in block.attentions:
                    for tb in attn.transformer_blocks:
                        if isinstance(tb, Basic2p5DTransformerBlock):
                            yield tb

    def __getattr__(self, name: str):
        try:
            return super().__getattr__(name)
        except AttributeError:
            return getattr(self.unet, name)

    def forward(
        self,
        sample: torch.Tensor,
        timestep: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        *args,
        down_intrablock_additional_residuals=None,
        down_block_res_samples=None,
        mid_block_res_sample=None,
        **cached_condition,
    ):
        """Forward pass for multi-view texture generation."""
        B, N_gen, _, H, W = sample.shape
        assert H == W

        if self.use_camera_embedding:
            camera_info_gen = (
                cached_condition["camera_info_gen"] + self.max_num_ref_image
            )
            camera_info_gen = rearrange(camera_info_gen, "b n -> (b n)")
        else:
            camera_info_gen = None

        # Concatenate latents with normal and position maps
        sample = [sample]
        if "normal_imgs" in cached_condition:
            sample.append(cached_condition["normal_imgs"])
        if "position_imgs" in cached_condition:
            sample.append(cached_condition["position_imgs"])
        sample = torch.cat(sample, dim=2)

        sample = rearrange(sample, "b n c h w -> (b n) c h w")

        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(
            1, N_gen, 1, 1
        )
        encoder_hidden_states_gen = rearrange(
            encoder_hidden_states_gen, "b n l c -> (b n) l c"
        )

        # Process reference images for RVA
        if self.use_ra:
            if "condition_embed_dict" in cached_condition:
                condition_embed_dict = cached_condition["condition_embed_dict"]
            else:
                condition_embed_dict = {}
                ref_latents = cached_condition["ref_latents"]
                N_ref = ref_latents.shape[1]

                if self.use_camera_embedding:
                    camera_info_ref = cached_condition["camera_info_ref"]
                    camera_info_ref = rearrange(camera_info_ref, "b n -> (b n)")
                else:
                    camera_info_ref = None

                ref_latents = rearrange(ref_latents, "b n c h w -> (b n) c h w")

                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(
                    1
                ).repeat(B, N_ref, 1, 1)
                encoder_hidden_states_ref = rearrange(
                    encoder_hidden_states_ref, "b n l c -> (b n) l c"
                )

                noisy_ref_latents = ref_latents
                timestep_ref = 0

                if self.use_dual_stream:
                    unet_ref = self.unet_dual
                else:
                    unet_ref = self.unet

                unet_ref(
                    noisy_ref_latents,
                    timestep_ref,
                    encoder_hidden_states=encoder_hidden_states_ref,
                    class_labels=camera_info_ref,
                    return_dict=False,
                    cross_attention_kwargs={
                        "mode": "w",
                        "num_in_batch": N_ref,
                        "condition_embed_dict": condition_embed_dict,
                    },
                )
                cached_condition["condition_embed_dict"] = condition_embed_dict
        else:
            condition_embed_dict = None

        mva_scale = cached_condition.get("mva_scale", 1.0)
        ref_scale = cached_condition.get("ref_scale", 1.0)

        if self.is_turbo:
            position_attn_mask = cached_condition.get("position_attn_mask", None)
            position_voxel_indices = cached_condition.get(
                "position_voxel_indices", None
            )
            cross_attention_kwargs_ = {
                "mode": "r",
                "num_in_batch": N_gen,
                "condition_embed_dict": condition_embed_dict,
                "position_attn_mask": position_attn_mask,
                "position_voxel_indices": position_voxel_indices,
                "mva_scale": mva_scale,
                "ref_scale": ref_scale,
            }
        else:
            cross_attention_kwargs_ = {
                "mode": "r",
                "num_in_batch": N_gen,
                "condition_embed_dict": condition_embed_dict,
                "mva_scale": mva_scale,
                "ref_scale": ref_scale,
            }

        return self.unet(
            sample,
            timestep,
            encoder_hidden_states_gen,
            *args,
            class_labels=camera_info_gen,
            down_intrablock_additional_residuals=(
                [
                    s.to(dtype=self.unet.dtype)
                    for s in down_intrablock_additional_residuals
                ]
                if down_intrablock_additional_residuals is not None
                else None
            ),
            down_block_additional_residuals=(
                [s.to(dtype=self.unet.dtype) for s in down_block_res_samples]
                if down_block_res_samples is not None
                else None
            ),
            mid_block_additional_residual=(
                mid_block_res_sample.to(dtype=self.unet.dtype)
                if mid_block_res_sample is not None
                else None
            ),
            return_dict=False,
            cross_attention_kwargs=cross_attention_kwargs_,
        )


# Entry class for model registry
EntryClass = [Hunyuan3D2DiT, UNet2p5DConditionModel]
