unet_sd.py

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
import torch.utils.checkpoint


_xformers_imported = False
try:
    import xformers.ops
    _xformers_imported = True
except ImportError:
    _xformers_imported = False


def exists(x):
    return x is not None

def default(val, d):
    if exists(val):
        return val
    return d() if callable(d) else d


class UNetDiffusersCompatOutput:
    def __init__(self, sample: torch.Tensor) -> None:
        self.sample = sample


class UNetSD(nn.Module):

    def __init__(self,
            in_dim: int = 7,
            dim: int = 512,
            y_dim: int = 512,
            context_dim: int = 512,
            out_dim: int = 6,
            dim_mult: list[int] = [1, 2, 3, 4],
            num_heads: int | None = None,
            head_dim: int = 64,
            num_res_blocks: int = 3,
            attn_scales: list[float] = [1 / 2, 1 / 4, 1 / 8],
            use_scale_shift_norm: bool = True,
            dropout: float = 0.1,
            temporal_attn_times: int = 2,
            temporal_attention: bool = True,
            use_checkpoint: bool = False,
            use_image_dataset: bool = False,
            use_fps_condition: bool = False,
            use_sim_mask: bool = False,
            diffusers_compat: bool = False
    ) -> None:
        super().__init__()
        embed_dim = dim * 4
        num_heads = num_heads if num_heads else dim // 32
        self.diffusers_compat = diffusers_compat
        self.in_dim = in_dim
        self.dim = dim
        self.y_dim = y_dim
        self.context_dim = context_dim
        self.embed_dim = embed_dim
        self.out_dim = out_dim
        self.dim_mult = dim_mult
        self.num_heads = num_heads
        # parameters for spatial/temporal attention
        self.head_dim = head_dim
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.use_scale_shift_norm = use_scale_shift_norm
        self.temporal_attn_times = temporal_attn_times
        self.temporal_attention = temporal_attention
        self.use_checkpoint = use_checkpoint
        self.use_image_dataset = use_image_dataset
        self.use_fps_condition = use_fps_condition
        self.use_sim_mask = use_sim_mask
        use_linear_in_temporal = False
        transformer_depth = 1
        disabled_sa = False
        # params
        enc_dims = [dim * u for u in [1] + dim_mult]
        dec_dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
        shortcut_dims = []
        scale = 1.0

        # embeddings
        self.time_embed = nn.Sequential(
            nn.Linear(dim, embed_dim), nn.SiLU(),
            nn.Linear(embed_dim, embed_dim))

        if self.use_fps_condition:
            self.fps_embedding = nn.Sequential(
                nn.Linear(dim, embed_dim), nn.SiLU(),
                nn.Linear(embed_dim, embed_dim))
            nn.init.zeros_(self.fps_embedding[-1].weight)
            nn.init.zeros_(self.fps_embedding[-1].bias)

        # encoder
        self.input_blocks = nn.ModuleList()
        init_block = nn.ModuleList([nn.Conv2d(self.in_dim, dim, 3, padding=1)])

        if temporal_attention:
            init_block.append(
                TemporalTransformer(
                    dim,
                    num_heads,
                    head_dim,
                    depth=transformer_depth,
                    context_dim=context_dim,
                    disable_self_attn=disabled_sa,
                    use_linear=use_linear_in_temporal,
                    multiply_zero=use_image_dataset))

        self.input_blocks.append(init_block)
        shortcut_dims.append(dim)
        for i, (in_dim,
                out_dim) in enumerate(zip(enc_dims[:-1], enc_dims[1:])):
            for j in range(num_res_blocks):
                # residual (+attention) blocks
                block = nn.ModuleList([
                    ResBlock(
                        in_dim,
                        embed_dim,
                        dropout,
                        out_channels=out_dim,
                        use_scale_shift_norm=False,
                        use_image_dataset=use_image_dataset,
                    )
                ])
                if scale in attn_scales:
                    block.append(
                        SpatialTransformer(
                            out_dim,
                            out_dim // head_dim,
                            head_dim,
                            depth=1,
                            context_dim=self.context_dim,
                            disable_self_attn=False,
                            use_linear=True))
                    if self.temporal_attention:
                        block.append(
                            TemporalTransformer(
                                out_dim,
                                out_dim // head_dim,
                                head_dim,
                                depth=transformer_depth,
                                context_dim=context_dim,
                                disable_self_attn=disabled_sa,
                                use_linear=use_linear_in_temporal,
                                multiply_zero=use_image_dataset))

                in_dim = out_dim
                self.input_blocks.append(block)
                shortcut_dims.append(out_dim)

                # downsample
                if i != len(dim_mult) - 1 and j == num_res_blocks - 1:
                    downsample = Downsample(
                        out_dim, True, dims=2, out_channels=out_dim)
                    shortcut_dims.append(out_dim)
                    scale /= 2.0
                    self.input_blocks.append(downsample)

        # middle
        self.middle_block = nn.ModuleList([
            ResBlock(
                out_dim,
                embed_dim,
                dropout,
                use_scale_shift_norm=False,
                use_image_dataset=use_image_dataset,
            ),
            SpatialTransformer(
                out_dim,
                out_dim // head_dim,
                head_dim,
                depth=1,
                context_dim=self.context_dim,
                disable_self_attn=False,
                use_linear=True)
        ])

        if self.temporal_attention:
            self.middle_block.append(
                TemporalTransformer(
                    out_dim,
                    out_dim // head_dim,
                    head_dim,
                    depth=transformer_depth,
                    context_dim=context_dim,
                    disable_self_attn=disabled_sa,
                    use_linear=use_linear_in_temporal,
                    multiply_zero=use_image_dataset,
                ))

        self.middle_block.append(
            ResBlock(
                out_dim,
                embed_dim,
                dropout,
                use_scale_shift_norm=False,
                use_image_dataset=use_image_dataset,
            ))

        # decoder
        self.output_blocks = nn.ModuleList()
        for i, (in_dim,
                out_dim) in enumerate(zip(dec_dims[:-1], dec_dims[1:])):
            for j in range(num_res_blocks + 1):
                # residual (+attention) blocks
                block = nn.ModuleList([
                    ResBlock(
                        in_dim + shortcut_dims.pop(),
                        embed_dim,
                        dropout,
                        out_dim,
                        use_scale_shift_norm=False,
                        use_image_dataset=use_image_dataset,
                    )
                ])
                if scale in attn_scales:
                    block.append(
                        SpatialTransformer(
                            out_dim,
                            out_dim // head_dim,
                            head_dim,
                            depth=1,
                            context_dim=1024,
                            disable_self_attn=False,
                            use_linear=True))

                    if self.temporal_attention:
                        block.append(
                            TemporalTransformer(
                                out_dim,
                                out_dim // head_dim,
                                head_dim,
                                depth=transformer_depth,
                                context_dim=context_dim,
                                disable_self_attn=disabled_sa,
                                use_linear=use_linear_in_temporal,
                                multiply_zero=use_image_dataset))
                in_dim = out_dim

                # upsample
                if i != len(dim_mult) - 1 and j == num_res_blocks:
                    upsample = Upsample(
                        out_dim, True, dims=2.0, out_channels=out_dim)
                    scale *= 2.0
                    block.append(upsample)
                self.output_blocks.append(block)

        # head
        self.out = nn.Sequential(
            nn.GroupNorm(32, out_dim), nn.SiLU(),
            nn.Conv2d(out_dim, self.out_dim, 3, padding=1))

        # zero out the last layer params
        nn.init.zeros_(self.out[-1].weight)

        self._use_xformers: bool = False

    def set_use_memory_efficient_attention_xformers(self, valid: bool = True, attention_op = None) -> None:
        if valid:
            if not _xformers_imported:
                self._use_xformers = False
                print('xformers library not found, please install xformers first')
            else:
                self._use_xformers = True
        else:
            self._use_xformers = False
        def recurse_enable_xformers(m):
            if hasattr(m, 'set_use_memory_efficient_attention_xformers'):
                m.set_use_memory_efficient_attention_xformers(valid, attention_op)
        for c in self.children():
            c.apply(recurse_enable_xformers)

    def enable_gradient_checkpointing(self) -> None:
        def recurse_enable_gradient_checkpointing(m):
            if hasattr(m, 'enable_gradient_checkpointing'):
                m.enable_gradient_checkpointing()
        for c in self.children():
            c.apply(recurse_enable_gradient_checkpointing)

    def disable_gradient_checkpointing(self) -> None:
        def recurse_disable_gradient_checkpointing(m):
            if hasattr(m, 'disable_gradient_checkpointing'):
                m.disable_gradient_checkpointing()
        for c in self.children():
            c.apply(recurse_disable_gradient_checkpointing)


    def forward(self,
            x: torch.Tensor,
            t: torch.Tensor,
            y: torch.Tensor,
            fps: float | None = None,
            video_mask: torch.Tensor | None = None,
            focus_present_mask: torch.Tensor | None = None,
            prob_focus_present: float = 0.,
            mask_last_frame_num: int = 0  # mask last frame num
    ) -> torch.Tensor:
        """
        prob_focus_present: probability at which a given batch sample will focus on the present
                            (0. is all off, 1. is completely arrested attention across time)
        """
        batch, device = x.shape[0], x.device
        self.batch = batch

        # image and video joint training, if mask_last_frame_num is set, prob_focus_present will be ignored
        if mask_last_frame_num > 0:
            focus_present_mask = None
            video_mask[-mask_last_frame_num:] = False
        else:
            focus_present_mask = default(
                focus_present_mask, lambda: prob_mask_like(
                    (batch, ), prob_focus_present, device=device))

        time_rel_pos_bias = None
        # embeddings
        if self.use_fps_condition and fps is not None:
            e = (
                    self.time_embed(sinusoidal_embedding(t, self.dim)) +
                    self.fps_embedding(sinusoidal_embedding(fps, self.dim))
            )
        else:
            e = self.time_embed(sinusoidal_embedding(t, self.dim))
        context = y

        # repeat f times for spatial e and context
        f = x.shape[2]
        e = e.repeat_interleave(repeats = f, dim = 0)
        context = context.repeat_interleave(repeats = f, dim = 0)

        # always in shape (b f) c h w, except for temporal layer
        x = rearrange(x, 'b c f h w -> (b f) c h w')
        # encoder
        xs = []
        for block in self.input_blocks:
            x = self._forward_single(block, x, e, context, time_rel_pos_bias,
                                     focus_present_mask, video_mask)
            xs.append(x)

        # middle
        for block in self.middle_block:
            x = self._forward_single(block, x, e, context, time_rel_pos_bias,
                                     focus_present_mask, video_mask)

        # decoder
        for block in self.output_blocks:
            x = torch.cat([x, xs.pop()], dim=1)
            x = self._forward_single(
                block,
                x,
                e,
                context,
                time_rel_pos_bias,
                focus_present_mask,
                video_mask,
                reference = xs[-1] if len(xs) > 0 else None)

        # head
        x = self.out(x)
        # reshape back to (b c f h w)
        x = rearrange(x, '(b f) c h w -> b c f h w', b = batch)
        if self.diffusers_compat:
            return UNetDiffusersCompatOutput(x)
        else:
            return x

    def _forward_single(self,
                        module,
                        x,
                        e,
                        context,
                        time_rel_pos_bias,
                        focus_present_mask,
                        video_mask,
                        reference=None):
        if isinstance(module, ResidualBlock):
            x = x.contiguous()
            x = module(x, e, reference)
        elif isinstance(module, ResBlock):
            x = x.contiguous()
            x = module(x, e, self.batch)
        elif isinstance(module, SpatialTransformer):
            x = module(x, context)
        elif isinstance(module, TemporalTransformer):
            x = rearrange(x, '(b f) c h w -> b c f h w', b=self.batch)
            x = module(x, context)
            x = rearrange(x, 'b c f h w -> (b f) c h w')
        elif isinstance(module, CrossAttention):
            x = module(x, context)
        elif isinstance(module, BasicTransformerBlock):
            x = module(x, context)
        elif isinstance(module, FeedForward):
            x = module(x, context)
        elif isinstance(module, Upsample):
            x = module(x)
        elif isinstance(module, Downsample):
            x = module(x)
        elif isinstance(module, Resample):
            x = module(x, reference)
        elif isinstance(module, nn.ModuleList):
            for block in module:
                x = self._forward_single(block, x, e, context,
                                         time_rel_pos_bias, focus_present_mask,
                                         video_mask, reference)
        else:
            x = module(x)
        return x


def sinusoidal_embedding(timesteps, dim):
    # check input
    half = dim // 2
    timesteps = timesteps.float()
    # compute sinusoidal embedding
    sinusoid = torch.outer(
        timesteps, torch.pow(10000,
                             -torch.arange(half, dtype=timesteps.dtype, device=timesteps.device).div(half)))
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    if dim % 2 != 0:
        x = torch.cat([x, torch.zeros_like(x[:, :1])], dim=1)
    return x


class CrossAttention(nn.Module):

    def __init__(self,
                 query_dim,
                 context_dim=None,
                 heads=8,
                 dim_head=64,
                 dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head**-0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))

        self._use_xformers = False

    def set_use_memory_efficient_attention_xformers(self, valid: bool = True, attention_op = None) -> None:
        self._use_xformers = valid and _xformers_imported

    def forward(self, x, context=None, mask=None):
        if self._use_xformers:
            return self._forward_xformers(x, context, mask)
        else:
            return self._forward(x, context, mask)

    def _forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h),
                      (q, k, v))
        sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
        del q, k

        if exists(mask):
            mask = rearrange(mask, 'b ... -> b (...)')
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, 'b j -> (b h) () j', h=h)
            sim.masked_fill_(~mask, max_neg_value)

        # attention, what we cannot get enough of
        sim = sim.softmax(dim=-1)

        out = torch.einsum('b i j, b j d -> b i d', sim, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        return self.to_out(out)

    def _forward_xformers(self, x, context=None, mask=None):
        if mask is not None:
            raise NotImplementedError("Masking with xformers is not yet supported")
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h),
                      (q, k, v))
        out = xformers.ops.memory_efficient_attention(q, k, v, scale=self.scale)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        return self.to_out(out)



class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data in spatial axis.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    NEW: use_linear for more efficiency instead of the 1x1 convs
    """

    def __init__(self,
                 in_channels,
                 n_heads,
                 d_head,
                 depth=1,
                 dropout=0.,
                 context_dim=None,
                 disable_self_attn=False,
                 use_linear=False,
                 use_checkpoint=True):
        super().__init__()
        if exists(context_dim) and not isinstance(context_dim, list):
            context_dim = [context_dim]
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv2d(
                in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        self.transformer_blocks = nn.ModuleList([
            BasicTransformerBlock(
                inner_dim,
                n_heads,
                d_head,
                dropout=dropout,
                context_dim=context_dim[d],
                disable_self_attn=disable_self_attn,
                checkpoint=use_checkpoint) for d in range(depth)
        ])
        if not use_linear:
            self.proj_out = zero_module(
                nn.Conv2d(
                    inner_dim, in_channels, kernel_size=1, stride=1,
                    padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if not isinstance(context, list):
            context = [context]
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        if not self.use_linear:
            x = self.proj_in(x)
        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
        if self.use_linear:
            x = self.proj_in(x)
        for i, block in enumerate(self.transformer_blocks):
            x = block(x, context=context[i])
        if self.use_linear:
            x = self.proj_out(x)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            x = self.proj_out(x)
        return x + x_in


class TemporalTransformer(nn.Module):
    """
    Transformer block for image-like data in temporal axis.
    First, reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """

    def __init__(self,
                 in_channels,
                 n_heads,
                 d_head,
                 depth=1,
                 dropout=0.,
                 context_dim=None,
                 disable_self_attn=False,
                 use_linear=False,
                 use_checkpoint=True,
                 only_self_att=True,
                 multiply_zero=False):
        super().__init__()
        self.multiply_zero = multiply_zero
        self.only_self_att = only_self_att
        if self.only_self_att:
            context_dim = None
        if not isinstance(context_dim, list):
            context_dim = [context_dim]
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv1d(
                in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        self.transformer_blocks = nn.ModuleList([
            BasicTransformerBlock(
                inner_dim,
                n_heads,
                d_head,
                dropout=dropout,
                context_dim=context_dim[d],
                checkpoint=use_checkpoint) for d in range(depth)
        ])
        if not use_linear:
            self.proj_out = zero_module(
                nn.Conv1d(
                    inner_dim, in_channels, kernel_size=1, stride=1,
                    padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if self.only_self_att:
            context = None
        if not isinstance(context, list):
            context = [context]
        b, c, f, h, w = x.shape
        x_in = x
        x = self.norm(x)

        if not self.use_linear:
            x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous()
            x = self.proj_in(x)
        if self.use_linear:
            x = rearrange(
                x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous()
            x = self.proj_in(x)

        if self.only_self_att:
            x = rearrange(x, 'bhw c f -> bhw f c').contiguous()
            for i, block in enumerate(self.transformer_blocks):
                x = block(x)
            x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous()
        else:
            x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous()
            for i, block in enumerate(self.transformer_blocks):
                context[i] = rearrange(
                    context[i], '(b f) l con -> b f l con',
                    f=self.frames).contiguous()
                # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
                for j in range(b):
                    context_i_j = repeat(
                        context[i][j],
                        'f l con -> (f r) l con',
                        r=(h * w) // self.frames,
                        f=self.frames).contiguous()
                    x[j] = block(x[j], context=context_i_j)

        if self.use_linear:
            x = self.proj_out(x)
            x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous()
            x = self.proj_out(x)
            x = rearrange(
                x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous()

        if self.multiply_zero:
            x = 0.0 * x + x_in
        else:
            x = x + x_in
        return x


class BasicTransformerBlock(nn.Module):

    def __init__(self,
            dim,
            n_heads,
            d_head,
            dropout = 0.,
            context_dim = None,
            gated_ff = True,
            checkpoint = False,
            disable_self_attn = False
    ):
        super().__init__()
        self.disable_self_attn = disable_self_attn
        self.attn1 = CrossAttention(
                query_dim = dim,
                heads = n_heads,
                dim_head = d_head,
                dropout = dropout,
                context_dim = context_dim if self.disable_self_attn else
                None
        )  # is a self-attention if not self.disable_self_attn
        self.ff = FeedForward(dim, dropout = dropout, glu = gated_ff)
        self.attn2 = CrossAttention(
                query_dim = dim,
                context_dim = context_dim,
                heads = n_heads,
                dim_head = d_head,
                dropout = dropout
        )  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def enable_gradient_checkpointing(self) -> None:
        self.checkpoint = True

    def disable_gradient_checkpointing(self) -> None:
        self.checkpoint = False

    def forward(self, x, context = None):
        if self.checkpoint:
            return torch.utils.checkpoint.checkpoint(self._forward, x, context)
        else:
            return self._forward(x, context = context)

    def _forward(self, x, context = None):
        x = self.attn1(
            self.norm1(x),
            context=context if self.disable_self_attn else None) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x


# feedforward
class GEGLU(nn.Module):

    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


class FeedForward(nn.Module):

    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = nn.Sequential(nn.Linear(
            dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim)

        self.net = nn.Sequential(project_in, nn.Dropout(dropout),
                                 nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.net(x)


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self,
                 channels,
                 use_conv,
                 dims=2,
                 out_channels=None,
                 padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = nn.Conv2d(
                self.channels, self.out_channels, 3, padding=padding)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2),
                mode='nearest')
        else:
            x = F.interpolate(x, scale_factor=2, mode='nearest')
        if self.use_conv:
            x = self.conv(x)
        return x


class ResBlock(nn.Module):
    """
    A residual block that can optionally change the number of channels.
    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    :param use_temporal_conv: if True, use the temporal convolution.
    :param use_image_dataset: if True, the temporal parameters will not be optimized.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        up=False,
        down=False,
        use_temporal_conv=True,
        use_image_dataset=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_scale_shift_norm = use_scale_shift_norm
        self.use_temporal_conv = use_temporal_conv

        self.in_layers = nn.Sequential(
            nn.GroupNorm(32, channels),
            nn.SiLU(),
            nn.Conv2d(channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            nn.Linear(
                emb_channels,
                2 * self.out_channels
                if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            nn.GroupNorm(32, self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1)
        else:
            self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)

        if self.use_temporal_conv:
            self.temopral_conv = TemporalConvBlock_v2(
                self.out_channels,
                self.out_channels,
                dropout=0.1,
                use_image_dataset=use_image_dataset)

    def forward(self, x, emb, batch_size):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.
        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return self._forward(x, emb, batch_size)

    def _forward(self, x, emb, batch_size):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = torch.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        h = self.skip_connection(x) + h

        if self.use_temporal_conv:
            h = rearrange(h, '(b f) c h w -> b c f h w', b=batch_size)
            h = self.temopral_conv(h)
            h = rearrange(h, 'b c f h w -> (b f) c h w')
        return h


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self,
                 channels,
                 use_conv,
                 dims=2,
                 out_channels=None,
                 padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if self.use_conv:
            self.op = nn.Conv2d(
                self.channels,
                self.out_channels,
                3,
                stride=stride,
                padding=padding)
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class Resample(nn.Module):

    def __init__(self, in_dim, out_dim, mode):
        assert mode in ['none', 'upsample', 'downsample']
        super(Resample, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.mode = mode

    def forward(self, x, reference=None):
        if self.mode == 'upsample':
            assert reference is not None
            x = F.interpolate(x, size=reference.shape[-2:], mode='nearest')
        elif self.mode == 'downsample':
            x = F.adaptive_avg_pool2d(
                x, output_size=tuple(u // 2 for u in x.shape[-2:]))
        return x


class ResidualBlock(nn.Module):

    def __init__(self,
                 in_dim,
                 embed_dim,
                 out_dim,
                 use_scale_shift_norm=True,
                 mode='none',
                 dropout=0.0):
        super(ResidualBlock, self).__init__()
        self.in_dim = in_dim
        self.embed_dim = embed_dim
        self.out_dim = out_dim
        self.use_scale_shift_norm = use_scale_shift_norm
        self.mode = mode

        # layers
        self.layer1 = nn.Sequential(
            nn.GroupNorm(32, in_dim), nn.SiLU(),
            nn.Conv2d(in_dim, out_dim, 3, padding=1))
        self.resample = Resample(in_dim, in_dim, mode)
        self.embedding = nn.Sequential(
            nn.SiLU(),
            nn.Linear(embed_dim,
                      out_dim * 2 if use_scale_shift_norm else out_dim))
        self.layer2 = nn.Sequential(
            nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout),
            nn.Conv2d(out_dim, out_dim, 3, padding=1))
        self.shortcut = nn.Identity() if in_dim == out_dim else nn.Conv2d(
            in_dim, out_dim, 1)
        # zero out the last layer params
        nn.init.zeros_(self.layer2[-1].weight)

    def forward(self, x, e, reference=None):
        identity = self.resample(x, reference)
        x = self.layer1[-1](self.resample(self.layer1[:-1](x), reference))
        e = self.embedding(e).unsqueeze(-1).unsqueeze(-1).type(x.dtype)
        if self.use_scale_shift_norm:
            scale, shift = e.chunk(2, dim=1)
            x = self.layer2[0](x) * (1 + scale) + shift
            x = self.layer2[1:](x)
        else:
            x = x + e
            x = self.layer2(x)
        x = x + self.shortcut(identity)
        return x


class AttentionBlock(nn.Module):

    def __init__(self, dim, context_dim=None, num_heads=None, head_dim=None):
        # consider head_dim first, then num_heads
        num_heads = dim // head_dim if head_dim else num_heads
        head_dim = dim // num_heads
        assert num_heads * head_dim == dim
        super(AttentionBlock, self).__init__()
        self.dim = dim
        self.context_dim = context_dim
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.scale = math.pow(head_dim, -0.25)

        # layers
        self.norm = nn.GroupNorm(32, dim)
        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
        if context_dim is not None:
            self.context_kv = nn.Linear(context_dim, dim * 2)
        self.proj = nn.Conv2d(dim, dim, 1)

        # zero out the last layer params
        nn.init.zeros_(self.proj.weight)

    def forward(self, x, context=None):
        r"""x:       [B, C, H, W].
            context: [B, L, C] or None.
        """
        identity = x
        b, c, h, w, n, d = *x.size(), self.num_heads, self.head_dim

        # compute query, key, value
        x = self.norm(x)
        q, k, v = self.to_qkv(x).view(b, n * 3, d, h * w).chunk(3, dim=1)
        if context is not None:
            ck, cv = self.context_kv(context).reshape(b, -1, n * 2,
                                                      d).permute(0, 2, 3,
                                                                 1).chunk(
                                                                     2, dim=1)
            k = torch.cat([ck, k], dim=-1)
            v = torch.cat([cv, v], dim=-1)

        # compute attention
        attn = torch.matmul(q.transpose(-1, -2) * self.scale, k * self.scale)
        attn = F.softmax(attn, dim=-1)

        # gather context
        x = torch.matmul(v, attn.transpose(-1, -2))
        x = x.reshape(b, c, h, w)
        # output
        x = self.proj(x)
        return x + identity


class TemporalConvBlock_v2(nn.Module):

    def __init__(self,
                 in_dim,
                 out_dim=None,
                 dropout=0.0,
                 use_image_dataset=False):
        super(TemporalConvBlock_v2, self).__init__()
        if out_dim is None:
            out_dim = in_dim  # int(1.5*in_dim)
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.use_image_dataset = use_image_dataset

        # conv layers
        self.conv1 = nn.Sequential(
            nn.GroupNorm(32, in_dim), nn.SiLU(),
            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)))
        self.conv2 = nn.Sequential(
            nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)))
        self.conv3 = nn.Sequential(
            nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)))
        self.conv4 = nn.Sequential(
            nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)))

        # zero out the last layer params,so the conv block is identity
        nn.init.zeros_(self.conv4[-1].weight)
        nn.init.zeros_(self.conv4[-1].bias)

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)

        if self.use_image_dataset:
            x = identity + 0.0 * x
        else:
            x = identity + x
        return x


def prob_mask_like(shape, prob, device):
    if prob == 1:
        return torch.ones(shape, device=device, dtype=torch.bool)
    elif prob == 0:
        return torch.zeros(shape, device=device, dtype=torch.bool)
    else:
        mask = torch.zeros(shape, device=device).float().uniform_(0, 1) < prob
        # aviod mask all, which will cause find_unused_parameters error
        if mask.all():
            mask[0] = False
        return mask


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f'unsupported dimensions: {dims}')


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f'unsupported dimensions: {dims}')