mvdream / unet /mv_unet.py

Update unet/mv_unet.py

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	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 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,
	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)

	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,
	):
	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,
	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 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,
	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_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):
	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 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,
	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.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_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,
	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_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_scale_shift_norm=use_scale_shift_norm,
	),
	SpatialTransformer3D(
	ch,
	num_heads,
	dim_head,
	context_dim=context_dim,
	depth=transformer_depth,
	ip_dim=self.ip_dim,
	ip_weight=self.ip_weight,
	),
	ResBlock(
	ch,
	time_embed_dim,
	dropout,
	dims=dims,
	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_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,
	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_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)