# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange, repeat
from timm.models.vision_transformer import Mlp, PatchEmbed
import os
import sys
sys.path.append(os.path.split(sys.path[0])[0])
# the xformers lib allows less memory, faster training and inference
try:
import xformers
import xformers.ops
except:
XFORMERS_IS_AVAILBLE = False
# from timm.models.layers.helpers import to_2tuple
# from timm.models.layers.trace_utils import _assert
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Attention Layers from TIMM #
#################################################################################
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., use_lora=False, attention_mode='math'):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.attention_mode = attention_mode
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if self.attention_mode == 'xformers': # cause loss nan while using with amp
x = xformers.ops.memory_efficient_attention(q, k, v).reshape(B, N, C)
elif self.attention_mode == 'flash':
# cause loss nan while using with amp
# Optionally use the context manager to ensure one of the fused kerenels is run
with torch.backends.cuda.sdp_kernel(enable_math=False):
x = torch.nn.functional.scaled_dot_product_attention(q, k, v).reshape(B, N, C) # require pytorch 2.0
elif self.attention_mode == 'math':
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
else:
raise NotImplemented
x = self.proj(x)
x = self.proj_drop(x)
return x
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * 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)
return embedding
def forward(self, t, use_fp16=False):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
if use_fp16:
t_freq = t_freq.to(dtype=torch.float16)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
#################################################################################
# Core Latte Model #
#################################################################################
class TransformerBlock(nn.Module):
"""
A Latte block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of Latte.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class Latte(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
num_frames=16,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
extras=2,
attention_mode='math',
):
super().__init__()
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.extras = extras
self.num_frames = num_frames
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
if self.extras == 2:
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
if self.extras == 78: # timestep + text_embedding
self.text_embedding_projection = nn.Sequential(
nn.SiLU(),
nn.Linear(1024, hidden_size, bias=True)
)
num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
self.temp_embed = nn.Parameter(torch.zeros(1, num_frames, hidden_size), requires_grad=False)
self.blocks = nn.ModuleList([
TransformerBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode) for _ in range(depth)
])
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
temp_embed = get_1d_sincos_temp_embed(self.temp_embed.shape[-1], self.temp_embed.shape[-2])
self.temp_embed.data.copy_(torch.from_numpy(temp_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
if self.extras == 2:
# Initialize label embedding table:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in Latte blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
# @torch.cuda.amp.autocast()
# @torch.compile
def forward(self, x, t, y=None, use_fp16=False, y_image=None, use_image_num=0):
"""
Forward pass of Latte.
x: (N, F, C, H, W) tensor of video inputs
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
y_image: tensor of video frames
use_image_num: how many video frames are used
"""
if use_fp16:
x = x.to(dtype=torch.float16)
batches, frames, channels, high, weight = x.shape
x = rearrange(x, 'b f c h w -> (b f) c h w')
x = self.x_embedder(x) + self.pos_embed
t = self.t_embedder(t, use_fp16=use_fp16)
timestep_spatial = repeat(t, 'n d -> (n c) d', c=self.temp_embed.shape[1] + use_image_num)
timestep_temp = repeat(t, 'n d -> (n c) d', c=self.pos_embed.shape[1])
if self.extras == 2:
y = self.y_embedder(y, self.training)
if self.training:
y_image_emb = []
# print(y_image)
for y_image_single in y_image:
# print(y_image_single)
y_image_single = y_image_single.reshape(1, -1)
y_image_emb.append(self.y_embedder(y_image_single, self.training))
y_image_emb = torch.cat(y_image_emb, dim=0)
y_spatial = repeat(y, 'n d -> n c d', c=self.temp_embed.shape[1])
y_spatial = torch.cat([y_spatial, y_image_emb], dim=1)
y_spatial = rearrange(y_spatial, 'n c d -> (n c) d')
else:
y_spatial = repeat(y, 'n d -> (n c) d', c=self.temp_embed.shape[1])
y_temp = repeat(y, 'n d -> (n c) d', c=self.pos_embed.shape[1])
elif self.extras == 78:
text_embedding = self.text_embedding_projection(text_embedding)
text_embedding_video = text_embedding[:, :1, :]
text_embedding_image = text_embedding[:, 1:, :]
text_embedding_video = repeat(text_embedding, 'n t d -> n (t c) d', c=self.temp_embed.shape[1])
text_embedding_spatial = torch.cat([text_embedding_video, text_embedding_image], dim=1)
text_embedding_spatial = rearrange(text_embedding_spatial, 'n t d -> (n t) d')
text_embedding_temp = repeat(text_embedding_video, 'n t d -> n (t c) d', c=self.pos_embed.shape[1])
text_embedding_temp = rearrange(text_embedding_temp, 'n t d -> (n t) d')
for i in range(0, len(self.blocks), 2):
spatial_block, temp_block = self.blocks[i:i+2]
if self.extras == 2:
c = timestep_spatial + y_spatial
elif self.extras == 78:
c = timestep_spatial + text_embedding_spatial
else:
c = timestep_spatial
x = spatial_block(x, c)
x = rearrange(x, '(b f) t d -> (b t) f d', b=batches)
x_video = x[:, :(frames-use_image_num), :]
x_image = x[:, (frames-use_image_num):, :]
# Add Time Embedding
if i == 0:
x_video = x_video + self.temp_embed
if self.extras == 2:
c = timestep_temp + y_temp
elif self.extras == 78:
c = timestep_temp + text_embedding_temp
else:
c = timestep_temp
x_video = temp_block(x_video, c)
x = torch.cat([x_video, x_image], dim=1)
x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
if self.extras == 2:
c = timestep_spatial + y_spatial
else:
c = timestep_spatial
x = self.final_layer(x, c)
x = self.unpatchify(x)
x = rearrange(x, '(b f) c h w -> b f c h w', b=batches)
# print(x.shape)
return x
def forward_with_cfg(self, x, t, y, cfg_scale, use_fp16=False):
"""
Forward pass of Latte, but also batches the unconditional 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)
if use_fp16:
combined = combined.to(dtype=torch.float16)
model_out = self.forward(combined, t, y, use_fp16=use_fp16)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
eps, rest = model_out[:, :, :4, ...], model_out[:, :, 4:, ...] # 2 16 4 32 32
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 torch.cat([eps, rest], dim=2)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
def get_1d_sincos_temp_embed(embed_dim, length):
pos = torch.arange(0, length).unsqueeze(1)
return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
#################################################################################
# Latte Configs #
#################################################################################
def Latte_XL_2(**kwargs):
return Latte(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
def Latte_XL_4(**kwargs):
return Latte(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
def Latte_XL_8(**kwargs):
return Latte(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)
def Latte_L_2(**kwargs):
return Latte(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
def Latte_L_4(**kwargs):
return Latte(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)
def Latte_L_8(**kwargs):
return Latte(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)
def Latte_B_2(**kwargs):
return Latte(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
def Latte_B_4(**kwargs):
return Latte(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
def Latte_B_8(**kwargs):
return Latte(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
def Latte_S_2(**kwargs):
return Latte(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
def Latte_S_4(**kwargs):
return Latte(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
def Latte_S_8(**kwargs):
return Latte(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
LatteIMG_models = {
'LatteIMG-XL/2': Latte_XL_2, 'LatteIMG-XL/4': Latte_XL_4, 'LatteIMG-XL/8': Latte_XL_8,
'LatteIMG-L/2': Latte_L_2, 'LatteIMG-L/4': Latte_L_4, 'LatteIMG-L/8': Latte_L_8,
'LatteIMG-B/2': Latte_B_2, 'LatteIMG-B/4': Latte_B_4, 'LatteIMG-B/8': Latte_B_8,
'LatteIMG-S/2': Latte_S_2, 'LatteIMG-S/4': Latte_S_4, 'LatteIMG-S/8': Latte_S_8,
}
if __name__ == '__main__':
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
use_image_num = 8
img = torch.randn(3, 16+use_image_num, 4, 32, 32).to(device)
t = torch.tensor([1, 2, 3]).to(device)
y = torch.tensor([1, 2, 3]).to(device)
y_image = [torch.tensor([48, 37, 72, 63, 74, 6, 7, 8]).to(device),
torch.tensor([37, 72, 63, 74, 70, 1, 2, 3]).to(device),
torch.tensor([72, 63, 74, 70, 71, 5, 8, 7]).to(device),
]
network = Latte_XL_2().to(device)
network.train()
out = network(img, t, y=y, y_image=y_image, use_image_num=use_image_num)
print(out.shape)