unet/mv_unet.py

import math
import numpy as np
from inspect import isfunction
from typing import Optional, Any, List

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

from diffusers.configuration_utils import ConfigMixin
from diffusers.models.modeling_utils import ModelMixin

# require xformers!
import xformers
import xformers.ops

from kiui.cam import orbit_camera

def get_camera(
    num_frames, elevation=15, azimuth_start=0, azimuth_span=360, blender_coord=True, extra_view=False,
):
    angle_gap = azimuth_span / num_frames
    cameras = []
    for azimuth in np.arange(azimuth_start, azimuth_span + azimuth_start, angle_gap):
        
        pose = orbit_camera(-elevation, azimuth, radius=1) # kiui's elevation is negated, [4, 4]

        # opengl to blender
        if blender_coord:
            pose[2] *= -1
            pose[[1, 2]] = pose[[2, 1]]

        cameras.append(pose.flatten())

    if extra_view:
        cameras.append(np.zeros_like(cameras[0]))

    return torch.from_numpy(np.stack(cameras, axis=0)).float() # [num_frames, 16]


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_tensors = list(args[:length])
        ctx.input_params = list(args[length:])

        with torch.no_grad():
            output_tensors = ctx.run_function(*ctx.input_tensors)
        return output_tensors

    @staticmethod
    def backward(ctx, *output_grads):
        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
        with torch.enable_grad():
            # Fixes a bug where the first op in run_function modifies the
            # Tensor storage in place, which is not allowed for detach()'d
            # Tensors.
            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
            output_tensors = ctx.run_function(*shallow_copies)
        input_grads = torch.autograd.grad(
            output_tensors,
            ctx.input_tensors + ctx.input_params,
            output_grads,
            allow_unused=True,
        )
        del ctx.input_tensors
        del ctx.input_params
        del output_tensors
        return (None, None) + input_grads


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: 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 x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period)
            * torch.arange(start=0, end=half, dtype=torch.float32)
            / half
        ).to(device=timesteps.device)
        args = timesteps[:, None] * 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
            )
    else:
        embedding = repeat(timesteps, "b -> b d", d=dim)
    # import pdb; pdb.set_trace()
    return embedding


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


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}")


def default(val, d):
    if val is not None:
        return val
    return d() if isfunction(d) else d


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)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        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 MemoryEfficientCrossAttention(nn.Module):
    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
    def __init__(
            self, 
            query_dim, 
            context_dim=None, 
            heads=8, 
            dim_head=64, 
            dropout=0.0,
            ip_dim=0,
            ip_weight=1,
        ):
        super().__init__()
        
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.heads = heads
        self.dim_head = dim_head

        self.ip_dim = ip_dim
        self.ip_weight = ip_weight

        if self.ip_dim > 0:
            self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
            self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)

        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.attention_op: Optional[Any] = None

    def forward(self, x, context=None):
        q = self.to_q(x)
        context = default(context, x)

        if self.ip_dim > 0:
            # context： [B, 77 + 16(ip), 1024]
            token_len = context.shape[1]
            context_ip = context[:, -self.ip_dim :, :]
            k_ip = self.to_k_ip(context_ip)
            v_ip = self.to_v_ip(context_ip)
            context = context[:, : (token_len - self.ip_dim), :]

        k = self.to_k(context)
        v = self.to_v(context)

        b, _, _ = q.shape
        q, k, v = map(
            lambda t: t.unsqueeze(3)
            .reshape(b, t.shape[1], self.heads, self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b * self.heads, t.shape[1], self.dim_head)
            .contiguous(),
            (q, k, v),
        )

        # actually compute the attention, what we cannot get enough of
        out = xformers.ops.memory_efficient_attention(
            q, k, v, attn_bias=None, op=self.attention_op
        )

        if self.ip_dim > 0:
            k_ip, v_ip = map(
                lambda t: t.unsqueeze(3)
                .reshape(b, t.shape[1], self.heads, self.dim_head)
                .permute(0, 2, 1, 3)
                .reshape(b * self.heads, t.shape[1], self.dim_head)
                .contiguous(),
                (k_ip, v_ip),
            )
            # actually compute the attention, what we cannot get enough of
            out_ip = xformers.ops.memory_efficient_attention(
                q, k_ip, v_ip, attn_bias=None, op=self.attention_op
            )
            out = out + self.ip_weight * out_ip

        out = (
            out.unsqueeze(0)
            .reshape(b, self.heads, out.shape[1], self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b, out.shape[1], self.heads * self.dim_head)
        )
        return self.to_out(out)


class BasicTransformerBlock3D(nn.Module):
    
    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        context_dim,
        dropout=0.0,
        gated_ff=True,
        checkpoint=True,
        ip_dim=0,
        ip_weight=1,
    ):
        super().__init__()

        self.attn1 = MemoryEfficientCrossAttention(
            query_dim=dim,
            context_dim=None, # self-attention
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
        )
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = MemoryEfficientCrossAttention(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            # ip only applies to cross-attention
            ip_dim=ip_dim,
            ip_weight=ip_weight,
        ) 
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None, num_frames=1):
        return checkpoint(
            self._forward, (x, context, num_frames), self.parameters(), self.checkpoint
        )

    def _forward(self, x, context=None, num_frames=1):
        x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous()
        x = self.attn1(self.norm1(x), context=None) + x
        x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous()
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x


class SpatialTransformer3D(nn.Module):

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        context_dim, # cross attention input dim
        depth=1,
        dropout=0.0,
        ip_dim=0,
        ip_weight=1,
        use_checkpoint=True,
    ):
        super().__init__()

        if not isinstance(context_dim, list):
            context_dim = [context_dim]

        self.in_channels = in_channels

        inner_dim = n_heads * d_head
        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.proj_in = nn.Linear(in_channels, inner_dim)

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock3D(
                    inner_dim,
                    n_heads,
                    d_head,
                    context_dim=context_dim[d],
                    dropout=dropout,
                    checkpoint=use_checkpoint,
                    ip_dim=ip_dim,
                    ip_weight=ip_weight,
                )
                for d in range(depth)
            ]
        )
        
        self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
        

    def forward(self, x, context=None, num_frames=1):
        # 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)
        x = rearrange(x, "b c h w -> b (h w) c").contiguous()
        x = self.proj_in(x)
        for i, block in enumerate(self.transformer_blocks):
            x = block(x, context=context[i], num_frames=num_frames)
        x = self.proj_out(x)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
        
        return x + x_in


class PerceiverAttention(nn.Module):
    def __init__(self, *, dim, dim_head=64, heads=8):
        super().__init__()
        self.scale = dim_head ** -0.5
        self.dim_head = dim_head
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

    def forward(self, x, latents):
        """
        Args:
            x (torch.Tensor): image features
                shape (b, n1, D)
            latent (torch.Tensor): latent features
                shape (b, n2, D)
        """
        x = self.norm1(x)
        latents = self.norm2(latents)

        b, l, _ = latents.shape

        q = self.to_q(latents)
        kv_input = torch.cat((x, latents), dim=-2)
        k, v = self.to_kv(kv_input).chunk(2, dim=-1)

        q, k, v = map(
            lambda t: t.reshape(b, t.shape[1], self.heads, -1)
            .transpose(1, 2)
            .reshape(b, self.heads, t.shape[1], -1)
            .contiguous(),
            (q, k, v),
        )

        # attention
        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        out = weight @ v

        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)

        return self.to_out(out)


class Resampler(nn.Module):
    def __init__(
        self,
        dim=1024,
        depth=8,
        dim_head=64,
        heads=16,
        num_queries=8,
        embedding_dim=768,
        output_dim=1024,
        ff_mult=4,
    ):
        super().__init__()
        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
        self.proj_in = nn.Linear(embedding_dim, dim)
        self.proj_out = nn.Linear(dim, output_dim)
        self.norm_out = nn.LayerNorm(output_dim)

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        nn.Sequential(
                            nn.LayerNorm(dim),
                            nn.Linear(dim, dim * ff_mult, bias=False),
                            nn.GELU(),
                            nn.Linear(dim * ff_mult, dim, bias=False),
                        )
                    ]
                )
            )

    def forward(self, x):
        latents = self.latents.repeat(x.size(0), 1, 1)
        x = self.proj_in(x)
        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents

        latents = self.proj_out(latents)
        return self.norm_out(latents)


class CondSequential(nn.Sequential):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb, context=None, num_frames=1):
        for layer in self:
            if isinstance(layer, ResBlock):
                x = layer(x, emb)
            elif isinstance(layer, SpatialTransformer3D):
                x = layer(x, context, num_frames=num_frames)
            else:
                x = layer(x)
        return 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 = conv_nd(
                dims, 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 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 use_conv:
            self.op = conv_nd(
                dims,
                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 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 use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
        up=False,
        down=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_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            nn.GroupNorm(32, channels),
            nn.SiLU(),
            conv_nd(dims, 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(
                conv_nd(dims, 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 = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        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 checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )

    def _forward(self, x, emb):
        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)
        return self.skip_connection(x) + h


class MultiViewUNetModel(ModelMixin, ConfigMixin):
    """
    The full multi-view UNet model with attention, timestep embedding and camera embedding.
    :param in_channels: channels in the input Tensor.
    :param model_channels: base channel count for the model.
    :param out_channels: channels in the output Tensor.
    :param num_res_blocks: number of residual blocks per downsample.
    :param attention_resolutions: a collection of downsample rates at which
        attention will take place. May be a set, list, or tuple.
        For example, if this contains 4, then at 4x downsampling, attention
        will be used.
    :param dropout: the dropout probability.
    :param channel_mult: channel multiplier for each level of the UNet.
    :param conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param num_classes: if specified (as an int), then this model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
    :param num_heads: the number of attention heads in each attention layer.
    :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
    :param resblock_updown: use residual blocks for up/downsampling.
    :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    :param camera_dim: dimensionality of camera input.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        transformer_depth=1,
        context_dim=None,
        n_embed=None,
        num_attention_blocks=None,
        adm_in_channels=None,
        camera_dim=None,
        ip_dim=0, # imagedream uses ip_dim > 0
        ip_weight=1.0,
        **kwargs,
    ):
        super().__init__()
        assert context_dim is not None
        
        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert (
                num_head_channels != -1
            ), "Either num_heads or num_head_channels has to be set"

        if num_head_channels == -1:
            assert (
                num_heads != -1
            ), "Either num_heads or num_head_channels has to be set"

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        if isinstance(num_res_blocks, int):
            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
        else:
            if len(num_res_blocks) != len(channel_mult):
                raise ValueError(
                    "provide num_res_blocks either as an int (globally constant) or "
                    "as a list/tuple (per-level) with the same length as channel_mult"
                )
            self.num_res_blocks = num_res_blocks
        
        if num_attention_blocks is not None:
            assert len(num_attention_blocks) == len(self.num_res_blocks)
            assert all(
                map(
                    lambda i: self.num_res_blocks[i] >= num_attention_blocks[i],
                    range(len(num_attention_blocks)),
                )
            )
            print(
                f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
                f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
                f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
                f"attention will still not be set."
            )

        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        self.ip_dim = ip_dim
        self.ip_weight = ip_weight

        if self.ip_dim > 0:
            self.image_embed = Resampler(
                dim=context_dim,
                depth=4,
                dim_head=64,
                heads=12,
                num_queries=ip_dim,  # num token
                embedding_dim=1280,
                output_dim=context_dim,
                ff_mult=4,
            )

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            nn.Linear(model_channels, time_embed_dim),
            nn.SiLU(),
            nn.Linear(time_embed_dim, time_embed_dim),
        )

        if camera_dim is not None:
            time_embed_dim = model_channels * 4
            self.camera_embed = nn.Sequential(
                nn.Linear(camera_dim, time_embed_dim),
                nn.SiLU(),
                nn.Linear(time_embed_dim, time_embed_dim),
            )

        if self.num_classes is not None:
            if isinstance(self.num_classes, int):
                self.label_emb = nn.Embedding(self.num_classes, time_embed_dim)
            elif self.num_classes == "continuous":
                # print("setting up linear c_adm embedding layer")
                self.label_emb = nn.Linear(1, time_embed_dim)
            elif self.num_classes == "sequential":
                assert adm_in_channels is not None
                self.label_emb = nn.Sequential(
                    nn.Sequential(
                        nn.Linear(adm_in_channels, time_embed_dim),
                        nn.SiLU(),
                        nn.Linear(time_embed_dim, time_embed_dim),
                    )
                )
            else:
                raise ValueError()

        self.input_blocks = nn.ModuleList(
            [
                CondSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for nr in range(self.num_res_blocks[level]):
                layers: List[Any] = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels

                    if num_attention_blocks is None or nr < num_attention_blocks[level]:
                        layers.append(
                            SpatialTransformer3D(
                                ch,
                                num_heads,
                                dim_head,
                                context_dim=context_dim,
                                depth=transformer_depth,
                                use_checkpoint=use_checkpoint,
                                ip_dim=self.ip_dim,
                                ip_weight=self.ip_weight,
                            )
                        )
                self.input_blocks.append(CondSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    CondSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        
        self.middle_block = CondSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            SpatialTransformer3D(
                ch,
                num_heads,
                dim_head,
                context_dim=context_dim,
                depth=transformer_depth,
                use_checkpoint=use_checkpoint,
                ip_dim=self.ip_dim,
                ip_weight=self.ip_weight,
            ), 
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(self.num_res_blocks[level] + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = model_channels * mult
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels

                    if num_attention_blocks is None or i < num_attention_blocks[level]:
                        layers.append(
                            SpatialTransformer3D(
                                ch,
                                num_heads,
                                dim_head,
                                context_dim=context_dim,
                                depth=transformer_depth,
                                use_checkpoint=use_checkpoint,
                                ip_dim=self.ip_dim,
                                ip_weight=self.ip_weight,
                            )
                        )
                if level and i == self.num_res_blocks[level]:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                    )
                    ds //= 2
                self.output_blocks.append(CondSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            nn.GroupNorm(32, ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
                nn.GroupNorm(32, ch),
                conv_nd(dims, model_channels, n_embed, 1),
                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
            )

    def forward(
        self,
        x,
        timesteps=None,
        context=None,
        y=None,
        camera=None,
        num_frames=1,
        ip=None,
        ip_img=None,
        **kwargs,
    ):
        """
        Apply the model to an input batch.
        :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views).
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :param num_frames: a integer indicating number of frames for tensor reshaping.
        :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views).
        """
        assert (
            x.shape[0] % num_frames == 0
        ), "input batch size must be dividable by num_frames!"
        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"

        hs = []

        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)

        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            assert y is not None
            assert y.shape[0] == x.shape[0]
            emb = emb + self.label_emb(y)

        # Add camera embeddings
        if camera is not None:
            emb = emb + self.camera_embed(camera)
        
        # imagedream variant
        if self.ip_dim > 0:
            x[(num_frames - 1) :: num_frames, :, :, :] = ip_img # place at [4, 9]
            ip_emb = self.image_embed(ip)
            context = torch.cat((context, ip_emb), 1)

        h = x
        for module in self.input_blocks:
            h = module(h, emb, context, num_frames=num_frames)
            hs.append(h)
        h = self.middle_block(h, emb, context, num_frames=num_frames)
        for module in self.output_blocks:
            h = torch.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context, num_frames=num_frames)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)