add imagedream

3916e55 about 2 months ago

No virus

34.4 kB

	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)