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import torch.nn as nn
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from pdb import set_trace as st
from timm.models.vision_transformer import Mlp
from ldm.modules.attention import MemoryEfficientCrossAttention
from .dit_models_xformers import DiT, get_2d_sincos_pos_embed, DiTBlock, FinalLayer, t2i_modulate, PixelArtTextCondDiTBlock, T2IFinalLayer, approx_gelu
from torch.nn import LayerNorm
from vit.vit_triplane import XYZPosEmbed
class DiT_TriLatent(DiT):
# DiT with 3D_aware operations
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
mixing_logit_init=-3,
mixed_prediction=True,
context_dim=False,
roll_out=False,
vit_blk=DiTBlock,
final_layer_blk=FinalLayer,
):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma, mixing_logit_init,
mixed_prediction, context_dim, roll_out, vit_blk,
final_layer_blk)
assert self.roll_out
def init_PE_3D_aware(self):
self.pos_embed = nn.Parameter(torch.zeros(
1, self.plane_n * self.x_embedder.num_patches, self.embed_dim),
requires_grad=False)
# Initialize (and freeze) pos_embed by sin-cos embedding:
p = int(self.x_embedder.num_patches**0.5)
D = self.pos_embed.shape[-1]
grid_size = (self.plane_n, p * p) # B n HW C
pos_embed = get_2d_sincos_pos_embed(D, grid_size).reshape(
self.plane_n * p * p, D) # H*W, D
self.pos_embed.data.copy_(
torch.from_numpy(pos_embed).float().unsqueeze(0))
def initialize_weights(self):
super().initialize_weights()
# ! add 3d-aware PE
self.init_PE_3D_aware()
def forward(self,
x,
timesteps=None,
context=None,
y=None,
get_attr='',
**kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# t = timesteps
assert context is not None
t = self.t_embedder(timesteps) # (N, D)
# if self.roll_out: # !
x = rearrange(x, 'b (c n) h w->(b n) c h w',
n=3) # downsample with same conv
x = self.x_embedder(x) # (b n) c h/f w/f
x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
x = x + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
# if self.roll_out: # ! roll-out in the L dim, not B dim. add condition to all tokens.
# x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
# assert context.ndim == 2
if isinstance(context, dict):
context = context['crossattn'] # sgm conditioner compat
context = self.clip_text_proj(context)
# c = t + context
# else:
# c = t # BS 1024
for blk_idx, block in enumerate(self.blocks):
# if self.roll_out:
if False:
if blk_idx % 2 == 0: # with-in plane self attention
x = rearrange(x, 'b (n l) c -> (b n) l c', n=3)
x = block(x, repeat(t, 'b c -> (b n) c ', n=3), # TODO, calculate once
repeat(context, 'b l c -> (b n) l c ', n=3)) # (N, T, D)
else: # global attention
x = rearrange(x, '(b n) l c -> b (n l) c ', n=self.plane_n)
x = block(x, t, context) # (N, T, D)
else:
x = block(x, t, context) # (N, T, D)
# todo later
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
if self.roll_out: # move n from L to B axis
x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
x = self.unpatchify(x) # (N, out_channels, H, W)
if self.roll_out: # move n from L to B axis
x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
# x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
# cast to float32 for better accuracy
x = x.to(torch.float32).contiguous()
# st()
return x
class DiT_TriLatent_PixelArt(DiT_TriLatent):
# DiT with 3D_aware operations
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
mixing_logit_init=-3,
mixed_prediction=True,
context_dim=False,
roll_out=False,
vit_blk=DiTBlock,
final_layer_blk=FinalLayer,
):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma, mixing_logit_init,
mixed_prediction, context_dim, roll_out, vit_blk,
final_layer_blk)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
del self.clip_text_proj
self.cap_embedder = nn.Sequential( # TODO, init with zero here.
LayerNorm(context_dim),
nn.Linear(
context_dim,
hidden_size,
),
)
nn.init.constant_(self.cap_embedder[-1].weight, 0)
nn.init.constant_(self.cap_embedder[-1].bias, 0)
def forward(self,
x,
timesteps=None,
context=None,
y=None,
get_attr='',
**kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# t = timesteps
assert context is not None
clip_cls_token = self.cap_embedder(context['vector']) # pooled
t = self.t_embedder(timesteps) + clip_cls_token # (N, D)
t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
# if self.roll_out: # !
x = rearrange(x, 'b (c n) h w->(b n) c h w',
n=3) # downsample with same conv
x = self.x_embedder(x) # (b n) c h/f w/f
x = rearrange(x, '(b n) l c -> b (n l) c', n=3)
x = x + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
# if self.roll_out: # ! roll-out in the L dim, not B dim. add condition to all tokens.
# x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
# assert context.ndim == 2
if isinstance(context, dict):
context = context['crossattn'] # sgm conditioner compat
# context = self.clip_text_proj(context) # ! with rmsnorm here for
# c = t + context
# else:
# c = t # BS 1024
for blk_idx, block in enumerate(self.blocks):
x = block(x, t0, context) # (N, T, D)
# todo later
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
if self.roll_out: # move n from L to B axis
x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
x = self.unpatchify(x) # (N, out_channels, H, W)
if self.roll_out: # move n from L to B axis
x = rearrange(x, '(b n) c h w -> b (c n) h w', n=3)
# x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
# cast to float32 for better accuracy
x = x.to(torch.float32).contiguous()
# st()
return x
# ! compat issue
def forward_with_cfg(self, x, t, context, cfg_scale):
"""
Forward pass of SiT, but also batches the unconSiTional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
# half = x[: len(x) // 2]
# combined = torch.cat([half, half], dim=0)
eps = self.forward(x, t, context)
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
return eps
# PCD, general single-stage model.
class DiT_PCD_PixelArt(DiT_TriLatent_PixelArt):
# DiT with 3D_aware operations
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
mixing_logit_init=-3,
mixed_prediction=True,
context_dim=False,
roll_out=False,
vit_blk=PixelArtTextCondDiTBlock,
final_layer_blk=FinalLayer,
):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma, mixing_logit_init,
mixed_prediction, context_dim, roll_out, vit_blk,
final_layer_blk)
# an MLP to transform the input 19-dim feature to high-dim.
self.x_embedder = Mlp(in_features=in_channels,
hidden_features=hidden_size,
out_features=hidden_size,
act_layer=approx_gelu,
drop=0)
del self.pos_embed
def forward(self,
x,
timesteps=None,
context=None,
y=None,
get_attr='',
**kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# t = timesteps
assert context is not None
clip_cls_token = self.cap_embedder(context['caption_vector']) # pooled
t = self.t_embedder(timesteps) + clip_cls_token # (N, D)
t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
x = self.x_embedder(x)
# assert context.ndim == 2
if isinstance(context, dict):
context = context['caption_crossattn'] # sgm conditioner compat
# loop dit block
for blk_idx, block in enumerate(self.blocks):
x = block(x, t0, context) # (N, T, D)
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
# cast to float32 for better accuracy
x = x.to(torch.float32).contiguous()
return x
# ! two-stage version, the second-stage here, for text pretraining.
class DiT_PCD_PixelArt_tofeat(DiT_PCD_PixelArt):
# DiT with 3D_aware operations
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
mixing_logit_init=-3,
mixed_prediction=True,
context_dim=False,
roll_out=False,
vit_blk=DiTBlock,
final_layer_blk=FinalLayer,
use_pe_cond=True,
):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma, mixing_logit_init,
mixed_prediction, context_dim, roll_out, PixelArtTextCondDiTBlock,
final_layer_blk)
self.use_pe_cond = use_pe_cond
if use_pe_cond:
self.xyz_pos_embed = XYZPosEmbed(hidden_size)
else:
self.x_embedder = Mlp(in_features=in_channels+3,
hidden_features=hidden_size,
out_features=hidden_size,
act_layer=approx_gelu,
drop=0)
def forward(self,
x,
timesteps=None,
context=None,
y=None,
get_attr='',
**kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# t = timesteps
assert isinstance(context, dict)
# dino_spatial_token = rearrange(context['concat'], 'b v l c -> b (v l) c') # flatten MV dino features.
# t = self.t_embedder(timesteps)
clip_cls_token = self.cap_embedder(context['caption_vector']) # pooled
caption_crossattn, fps_xyz = context['caption_crossattn'], context['fps-xyz']
t = self.t_embedder(timesteps) + clip_cls_token # (N, D)
t0 = self.adaLN_modulation(t) # single-adaLN, B 6144
if self.use_pe_cond:
x = self.x_embedder(x) + self.xyz_pos_embed(fps_xyz) # point-wise addition
else: # use concat to add info
x = torch.cat([fps_xyz, x], dim=-1)
x = self.x_embedder(x)
# add a norm layer here, as in point-e
# x = self.ln_pre(x)
for blk_idx, block in enumerate(self.blocks):
x = block(x, t0, caption_crossattn)
# add a norm layer here, as in point-e
# x = self.ln_post(x)
x = self.final_layer(x, t) # no loss on the xyz side
x = x.to(torch.float32).contiguous()
return x
#################################################################################
# DiT_TriLatent Configs #
#################################################################################
def DiT_XL_2(**kwargs):
return DiT_TriLatent(depth=28,
hidden_size=1152,
patch_size=2,
num_heads=16,
**kwargs)
def DiT_L_2(**kwargs):
return DiT_TriLatent(depth=24,
hidden_size=1024,
patch_size=2,
num_heads=16,
**kwargs)
def DiT_B_2(**kwargs):
return DiT_TriLatent(depth=12,
hidden_size=768,
patch_size=2,
num_heads=12,
**kwargs)
def DiT_B_1(**kwargs):
return DiT_TriLatent(depth=12,
hidden_size=768,
patch_size=1,
num_heads=12,
**kwargs)
def DiT_B_Pixelart_2(**kwargs):
return DiT_TriLatent_PixelArt(depth=12,
hidden_size=768,
patch_size=2,
num_heads=12,
# vit_blk=PixelArtTextCondDiTBlock,
final_layer_blk=T2IFinalLayer,
**kwargs)
def DiT_L_Pixelart_2(**kwargs):
return DiT_TriLatent_PixelArt(depth=24,
hidden_size=1024,
patch_size=2,
num_heads=16,
# vit_blk=PixelArtTextCondDiTBlock,
final_layer_blk=T2IFinalLayer,
**kwargs)
# PCD-DiT
def DiT_PCD_B(**kwargs):
return DiT_PCD_PixelArt(depth=12,
hidden_size=768,
patch_size=1,
num_heads=12,
**kwargs)
def DiT_PCD_L(**kwargs):
return DiT_PCD_PixelArt(depth=24,
hidden_size=1024,
patch_size=1,
num_heads=16,
**kwargs)
def DiT_PCD_B_tofeat(**kwargs):
return DiT_PCD_PixelArt_tofeat(depth=12,
hidden_size=768,
patch_size=1,
num_heads=12,
**kwargs)
def DiT_PCD_L_tofeat(**kwargs):
return DiT_PCD_PixelArt_tofeat(depth=24,
hidden_size=1024,
patch_size=1,
num_heads=16,
**kwargs)
def DiT_PCD_XL_tofeat(**kwargs):
return DiT_PCD_PixelArt_tofeat(depth=28,
hidden_size=1152,
patch_size=1,
num_heads=16,
**kwargs)
DiT_models = {
'DiT-XL/2': DiT_XL_2,
'DiT-L/2': DiT_L_2,
'DiT-PixelArt-L/2': DiT_L_Pixelart_2,
'DiT-PixelArt-B/2': DiT_B_Pixelart_2,
'DiT-B/2': DiT_B_2,
'DiT-B/1': DiT_B_1,
'DiT-PCD-B': DiT_PCD_B,
'DiT-PCD-L': DiT_PCD_L,
'DiT-PCD-B-stage2-xyz2feat': DiT_PCD_B_tofeat,
'DiT-PCD-L-stage2-xyz2feat': DiT_PCD_L_tofeat,
'DiT-PCD-XL-stage2-xyz2feat': DiT_PCD_XL_tofeat,
# 'DiT-PCD-L-stage1-text': DiT_PCD_L_tofeat,
}
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