guided_diffusion/dit.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.nn.functional as F
from rotary_embedding_torch import RotaryEmbedding
from torch.jit import Final
import numpy as np
import math
from timm.models.vision_transformer import Attention, Mlp
from timm.models.vision_transformer_relpos import RelPosAttention
from timm.layers import Format, nchw_to, to_2tuple, _assert, RelPosBias, use_fused_attn
from typing import Callable, List, Optional, Tuple, Union
from functools import partial

def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


#################################################################################
#               Embedding Layers for Timesteps and Class Labels                 #
#################################################################################

class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=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)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq)
        return t_emb


class LabelEmbedder(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """
    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
        else:
            drop_ids = force_drop_ids == 1
        labels = torch.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels, train, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings


#################################################################################
#               Embedding Layers for Patches that Support H != W                #
#################################################################################

class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding
    """
    output_fmt: Format

    def __init__(
            self,
            img_size: Optional[Union[int, tuple, list]] = 224,
            patch_size: Union[int, tuple, list] = 16,
            in_chans: int = 3,
            embed_dim: int = 768,
            norm_layer: Optional[Callable] = None,
            flatten: bool = True,
            output_fmt: Optional[str] = None,
            bias: bool = True,
            strict_img_size: bool = True,
    ):
        super().__init__()
        self.patch_size = to_2tuple(patch_size)
        if img_size is not None:
            if isinstance(img_size, int):
                self.img_size = to_2tuple(img_size)
            elif len(img_size) == 1:
                self.img_size = to_2tuple(img_size[0])
            else:
                self.img_size = img_size
            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
            self.num_patches = self.grid_size[0] * self.grid_size[1]
        else:
            self.img_size = None
            self.grid_size = None
            self.num_patches = None

        if output_fmt is not None:
            self.flatten = False
            self.output_fmt = Format(output_fmt)
        else:
            # flatten spatial dim and transpose to channels last, kept for bwd compat
            self.flatten = flatten
            self.output_fmt = Format.NCHW
        self.strict_img_size = strict_img_size

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        if self.img_size is not None:
            if self.strict_img_size:
                _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
                _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
            else:
                _assert(
                    H % self.patch_size[0] == 0,
                    f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
                )
                _assert(
                    W % self.patch_size[1] == 0,
                    f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
                )

        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
        elif self.output_fmt != Format.NCHW:
            x = nchw_to(x, self.output_fmt)
        x = self.norm(x)
        return x


class FlattenNorm(nn.Module):
    """ Flatten 2D Image to a vector
    """

    def __init__(
            self,
            img_size: Optional[Union[int, tuple, list]] = 224,
            embed_dim: int = 768,
            norm_layer: Optional[Callable] = None,
    ):
        super().__init__()
        self.num_patches = max(img_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
        # todo: hard code 64 and hidden_dim for now
        self.MLP = nn.Sequential(nn.Linear(64, 256), nn.SiLU(), nn.Linear(256, embed_dim))

    def forward(self, x):
        x = x.permute(0, 2, 1, 3).flatten(2)   # B x 4 x 128 x 16 -> B x 128 x 4 x 16 - > B x 128 x 64
        x = self.MLP(x)    # B x 128 x 768
        x = self.norm(x)
        return x

    
class FlattenPatchify1D(nn.Module):
    """ Flatten 2D Image to a vector with pitch per token
    """

    def __init__(
            self,
            in_channels: int = 4,
            img_size: Optional[Union[int, tuple, list]] = 224,
            embed_dim: int = 768,
            patch_size: int = 8,
            norm_layer: Optional[Callable] = None,
    ):
        super().__init__()
        # dummy, is not needed by the rotary model, but needed for REL and DiT
        self.num_patches = img_size[0] * img_size[1] // patch_size   # img_size: 128x16
        self.patch_size = patch_size
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
        self.MLP = nn.Sequential(nn.Linear(in_channels * patch_size, 256), nn.SiLU(), nn.Linear(256, embed_dim))

    def forward(self, x):
        x = x.permute(0, 2, 3, 1)      # B x c x 128 x 16 -> B x 128 x 16 x c
        b, n_time, n_pitch, c = x.shape
        num_patches = n_time * n_pitch // self.patch_size
        # B x 128 x 16 x 4 -> B x (128 x 16 / 8) x (4 * 8)
        x = x.reshape(b, num_patches, -1)
        x = self.MLP(x)    # B x 256 x 768
        x = self.norm(x)
        return x


#################################################################################
#                                 Core DiT Model                                #
#################################################################################

class RotaryAttention(nn.Module):
    fused_attn: Final[bool]

    def __init__(
            self,
            dim,
            num_heads=8,
            qkv_bias=False,
            qk_norm=False,
            attn_drop=0.,
            proj_drop=0.,
            norm_layer=nn.LayerNorm,
            rotary_emb=None,
    ):
        super().__init__()
        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.fused_attn = use_fused_attn()
        self.rotary_emb = rotary_emb

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        q, k = self.q_norm(q), self.k_norm(k)

        if self.rotary_emb is not None:
            q = self.rotary_emb.rotate_queries_or_keys(q)
            k = self.rotary_emb.rotate_queries_or_keys(k)

        if self.fused_attn:
            x = F.scaled_dot_product_attention(
                q, k, v,
                dropout_p=self.attn_drop.p,
            )
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class DiTBlock(nn.Module):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
    """
    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate="tanh")
        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x


class DiTBlockRotary(nn.Module):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning & rotary attention.
    """
    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, rotary_emb=None, **block_kwargs):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = RotaryAttention(hidden_size, num_heads=num_heads, qkv_bias=True, rotary_emb=rotary_emb, **block_kwargs)
        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate="tanh")
        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x


class FinalLayer(nn.Module):
    """
    The final layer of DiT.
    """
    def __init__(self, hidden_size, patch_size, out_channels):
        super().__init__()
        self.norm_final = nn.LayerNorm(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, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


class FinalLayerPatch1D(nn.Module):
    """
    The final layer of DiT with 1d Patchify.
    """
    def __init__(self, hidden_size, out_channels, patch_size_1d=16):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, patch_size_1d*out_channels, bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


class DiT(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """
    def __init__(
        self,
        input_size=32,
        patch_size=2,
        in_channels=3,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        class_dropout_prob=0.1,
        num_classes=9,    # cluster composers into 9 groups
        learn_sigma=True,
        patchify=True,
    ):
        super().__init__()
        self.learn_sigma = learn_sigma
        self.in_channels = in_channels
        self.out_channels = in_channels * 2 if learn_sigma else in_channels
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.input_size = input_size
        self.patchify = patchify

        if patchify:
            self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
        else:
            self.x_embedder = FlattenNorm(input_size, hidden_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.num_classes = num_classes
        if self.num_classes:
            self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
        num_patches = self.x_embedder.num_patches
        # Will use fixed sin-cos embedding:
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)

        self.blocks = nn.ModuleList([
            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
        ])
        if patchify:
            self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
        else:
            self.final_layer = FinalLayerPatch1D(hidden_size, self.out_channels, patch_size)
        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)

        # Initialize (and freeze) pos_embed by sin-cos embedding:
        if self.patchify:
            if isinstance(self.input_size, int) or len(self.input_size) == 1:
                pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5), int(self.x_embedder.num_patches ** 0.5))
            else:
                pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], self.x_embedder.grid_size[0], self.x_embedder.grid_size[1])
        else:
            # 1D position encoding
            pos_embed = get_1d_sincos_pos_embed_from_grid(self.pos_embed.shape[-1],
                                              np.arange(self.x_embedder.num_patches, dtype=np.float32))
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
        if self.patchify:
            w = self.x_embedder.proj.weight.data
            nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
            nn.init.constant_(self.x_embedder.proj.bias, 0)

        # Initialize label embedding table:
        if self.num_classes:
            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def unpatchify(self, x):
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        c = self.out_channels
        p = self.x_embedder.patch_size[0]
        if isinstance(self.input_size, int) or len(self.input_size) == 1:
            h = w = int(x.shape[1] ** 0.5)
            assert h * w == x.shape[1]
        else:
            h = self.input_size[0] // self.patch_size
            w = self.input_size[1] // self.patch_size

        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
        return imgs

    def unflatten(self, x):
        c = self.out_channels
        x = x.reshape(shape=(x.shape[0], x.shape[1], c, -1))
        imgs = x.permute(0, 2, 1, 3)
        return imgs

    def forward(self, x, t, y=None):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
        c = self.t_embedder(t)                   # (N, D)
        if self.num_classes and y is not None:
            y = self.y_embedder(y, self.training)    # (N, D)
            c = c + y                                # (N, D)
        for block in self.blocks:
            x = block(x, c)                      # (N, T, D)
        x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)
        if self.patchify:
            x = self.unpatchify(x)                   # (N, out_channels, H, W)
        else:
            x = self.unflatten(x)
        return x

    def forward_with_cfg(self, x, t, y, cfg_scale):
        """
        Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
        """
        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
        half = x[: len(x) // 2]
        combined = torch.cat([half, half], dim=0)
        model_out = self.forward(combined, t, y)
        # For exact reproducibility reasons, we apply classifier-free guidance on only
        # three channels by default. The standard approach to cfg applies it to all channels.
        # This can be done by uncommenting the following line and commenting-out the line following that.
        # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
        eps, rest = model_out[:, :3], model_out[:, 3:]
        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
        eps = torch.cat([half_eps, half_eps], dim=0)
        return torch.cat([eps, rest], dim=1)


class DiTRotary(nn.Module):
    """
    Diffusion model with a Transformer backbone, with rotary position embedding.
    Use 1D position encoding, patchify is set to False
    """

    def __init__(
        self,
        input_size=32,
        patch_size=8,   # patch size for 1D patchify
        in_channels=3,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        class_dropout_prob=0.1,
        num_classes=9,  # cluster composers into 9 groups
        learn_sigma=True,
    ):
        super().__init__()
        self.learn_sigma = learn_sigma
        self.in_channels = in_channels
        self.out_channels = in_channels * 2 if learn_sigma else in_channels
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.input_size = input_size

        self.x_embedder = FlattenPatchify1D(in_channels, input_size, hidden_size, patch_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.num_classes = num_classes
        if self.num_classes:
            self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)

        rotary_dim = int(hidden_size // num_heads * 0.5)   # 0.5 is rotary percentage in multihead rope, by default 0.5
        self.rotary_emb = RotaryEmbedding(rotary_dim)
        self.blocks = nn.ModuleList([
            DiTBlockRotary(hidden_size, num_heads, mlp_ratio=mlp_ratio, rotary_emb=self.rotary_emb) for _ in range(depth)
        ])
        self.final_layer = FinalLayerPatch1D(hidden_size, self.out_channels, patch_size_1d=self.patch_size)
        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)

        self.apply(_basic_init)

        # Initialize label embedding table:
        if self.num_classes:
            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def unpatchify(self, x):
        """
        x: (N, T, img_size[1] / patch_size * C)
        imgs: (N, H, W, C)
        """
        # input_size[1] is the pitch dimension, should always be the same
        x = x.reshape(shape=(x.shape[0], -1, self.input_size[1], self.out_channels))
        imgs = x.permute(0, 3, 1, 2)
        return imgs

    def forward(self, x, t, y=None):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        x = self.x_embedder(x)  # (N, T, D), where T = H * W / patch_size
        c = self.t_embedder(t)  # (N, D)
        if self.num_classes and y is not None:
            y = self.y_embedder(y, self.training)  # (N, D)
            c = c + y  # (N, D)
        for block in self.blocks:
            x = block(x, c)  # (N, T, D)
        x = self.final_layer(x, c)  # (N, T, patch_size * out_channels)
        x = self.unpatchify(x)
        return x


class DiT_classifier(nn.Module):
    """
    Classifier used in classifier guidance.
    """
    def __init__(
        self,
        input_size=32,
        patch_size=2,
        in_channels=3,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        num_classes=9,
        patchify=True,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.input_size = input_size
        self.patchify = patchify

        if patchify:
            self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
        else:
            self.x_embedder = FlattenNorm(input_size, hidden_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.num_classes = num_classes
        num_patches = self.x_embedder.num_patches
        # Will use fixed sin-cos embedding:
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)

        self.blocks = nn.ModuleList([
            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
        ])
        self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size), requires_grad=True)
        self.norm = nn.LayerNorm(hidden_size)
        self.classifier_head = nn.Sequential(nn.Linear(hidden_size, hidden_size//4),
                                             nn.SiLU(), nn.Linear(hidden_size//4, self.num_classes))
        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)

        if self.patchify:
            if isinstance(self.input_size, int) or len(self.input_size) == 1:
                pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5), int(self.x_embedder.num_patches ** 0.5))
            else:
                pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], self.x_embedder.grid_size[0], self.x_embedder.grid_size[1])
        else:
            # 1D position encoding
            pos_embed = get_1d_sincos_pos_embed_from_grid(self.pos_embed.shape[-1],
                                              np.arange(self.x_embedder.num_patches, dtype=np.float32))
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        # Initialize class token
        nn.init.normal_(self.cls_token, std=1e-6)

        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
        if self.patchify:
            w = self.x_embedder.proj.weight.data
            nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
            nn.init.constant_(self.x_embedder.proj.bias, 0)

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

    def forward(self, x, t):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
        c = self.t_embedder(t)                   # (N, D)
        for block in self.blocks:
            x = block(x, c)                      # (N, T, D)
        x = x[:, 0, :]                           # (N, D)
        x = self.norm(x)
        x = self.classifier_head(x)                # (N, num_classes)
        return x


class DiTRotaryClassifier(nn.Module):
    """
    Diffusion model with a Transformer backbone, with rotary position embedding.
    Use 1D position encoding, patchify is set to False
    """

    def __init__(
        self,
        input_size=32,
        patch_size=8,   # patch size for 1D patchify
        in_channels=3,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        num_classes=9,  # cluster composers into 9 groups
        chord=False,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.input_size = input_size
        self.chord = chord
        self.hidden_size = hidden_size

        self.x_embedder = FlattenPatchify1D(in_channels, input_size, hidden_size, patch_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.num_classes = num_classes

        rotary_dim = int(hidden_size // num_heads * 0.5)   # 0.5 is rotary percentage in multihead rope, by default 0.5
        self.rotary_emb = RotaryEmbedding(rotary_dim)
        self.blocks = nn.ModuleList([
            DiTBlockRotary(hidden_size, num_heads, mlp_ratio=mlp_ratio, rotary_emb=self.rotary_emb) for _ in range(depth)
        ])
        self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size), requires_grad=True)
        self.norm = nn.LayerNorm(hidden_size)
        self.classifier_head = nn.Sequential(nn.Linear(hidden_size, hidden_size//4),
                                             nn.SiLU(), nn.Linear(hidden_size//4, self.num_classes))
        if self.chord:
            self.norm_key = nn.LayerNorm(hidden_size)
            # predict key also: 24 major and minor keys + null
            self.classifier_head_key = nn.Sequential(nn.Linear(hidden_size, hidden_size//4),
                                             nn.SiLU(), nn.Linear(hidden_size//4, 25))
        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)

        self.apply(_basic_init)

        # Initialize class token
        nn.init.normal_(self.cls_token, std=1e-6)

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

    def forward(self, x, t, y=None):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        if self.chord:
            n_token = x.shape[2] // x.shape[3]
        x = self.x_embedder(x)  # (N, T, D), where T = H * W / patch_size
        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
        c = self.t_embedder(t)  # (N, D)
        for block in self.blocks:
            x = block(x, c)  # (N, T, D)
        if self.chord:
            x_key = x[:, 0, :]
            x_key = self.norm_key(x_key)
            key = self.classifier_head_key(x_key)
            x_chord = x[:, 1:, :]
            x_chord = x_chord.reshape(shape=[x.shape[0], n_token, -1, self.hidden_size])
            x_chord = x_chord.mean(dim=-2)
            x_chord = self.norm(x_chord)
            chord = self.classifier_head(x_chord)
            return key, chord
        else:
            x = x[:, 0, :]  # (N, D)
            x = self.norm(x)
            x = self.classifier_head(x)    # (N, num_classes)
            return x


#################################################################################
#                   Sine/Cosine Positional Embedding Functions                  #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py

def get_2d_sincos_pos_embed(embed_dim, grid_size_h, grid_size_w, cls_token=False, extra_tokens=0):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size_h, dtype=np.float32)
    grid_w = np.arange(grid_size_w, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.
    omega = 1. / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out) # (M, D/2)
    emb_cos = np.cos(out) # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


#################################################################################
#                                   DiT Configs                                  #
#################################################################################

def DiT_XL_2(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)

def DiT_XL_4(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)

def DiTRotary_XL_16(**kwargs):
    return DiTRotary(depth=28, hidden_size=1152, patch_size=16, num_heads=16, **kwargs)

def DiTRotary_XL_8(**kwargs):
    return DiTRotary(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)

def DiT_XL_8(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)

def DiT_L_2(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)

def DiT_L_4(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)

def DiT_L_8(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)

def DiT_B_2(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)

def DiT_B_4(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)

def DiTRotary_B_16(**kwargs):   # seq_len = 128 = 128 * 16/16
    return DiTRotary(depth=12, hidden_size=768, patch_size=16, num_heads=12, **kwargs)

def DiTRotary_B_8(**kwargs):   # seq_len = 256 = 128 * 16/8
    return DiTRotary(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)

def DiT_B_8(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)

def DiT_B_4_classifier(**kwargs):
    return DiT_classifier(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)

def DiT_B_8_classifier(**kwargs):
    return DiT_classifier(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)

def DiTRotary_B_8_classifier(**kwargs):
    return DiTRotaryClassifier(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)

def DiT_S_2(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)

def DiT_S_2_classifier(**kwargs):
    return DiT_classifier(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)

def DiTRotary_S_8_classifier(**kwargs):
    return DiTRotaryClassifier(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)

def DiTRotary_S_8_chord_classifier(**kwargs):
    return DiTRotaryClassifier(depth=12, hidden_size=384, patch_size=8, num_heads=6, chord=True, **kwargs)

def DiT_XS_2_classifier(**kwargs):
    return DiT_classifier(depth=4, hidden_size=384, patch_size=2, num_heads=6, **kwargs)

def DiTRotary_XS_8_classifier(**kwargs):
    return DiTRotaryClassifier(depth=4, hidden_size=384, patch_size=8, num_heads=6, **kwargs)

def DiT_S_4(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)

def DiT_S_4_classifier(**kwargs):
    return DiT_classifier(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)

def DiT_S_8(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)


DiT_models = {
    'DiT-XL/2': DiT_XL_2,  'DiT-XL/4': DiT_XL_4,  'DiT-XL/8': DiT_XL_8,
    'DiT-L/2':  DiT_L_2,   'DiT-L/4':  DiT_L_4,   'DiT-L/8':  DiT_L_8,
    'DiT-B/2':  DiT_B_2,   'DiT-B/4':  DiT_B_4,   'DiT-B/8':  DiT_B_8,
    'DiT-S/2':  DiT_S_2,   'DiT-S/4':  DiT_S_4,   'DiT-S/8':  DiT_S_8,
    'DiTRotary_B_16': DiTRotary_B_16,  'DiTRotary_B_8': DiTRotary_B_8,
    'DiTRotary_XL_16': DiTRotary_XL_16, 'DiTRotary_XL_8': DiTRotary_XL_8,
    'DiT-B/4-cls':  DiT_B_4_classifier,   'DiT-B/8-cls':  DiT_B_8_classifier,
    'DiT-S/4-cls': DiT_S_4_classifier, 'DiT-S/2-cls': DiT_S_2_classifier,
    'DiT-XS/2-cls': DiT_XS_2_classifier,
    'DiTRotary-XS/8-cls': DiTRotary_XS_8_classifier,
    'DiTRotary-S/8-cls': DiTRotary_S_8_classifier,
    'DiTRotary-S/8-chord-cls': DiTRotary_S_8_chord_classifier,
    'DiTRotary-B/8-cls': DiTRotary_B_8_classifier,
 }