# DiT with cross attention
import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils.accelerate_utils import apply_forward_hook
from einops import rearrange
from peft import get_peft_model_state_dict, set_peft_model_state_dict
from torch import nn
def timestep_embedding(t, dim, max_period=10000):
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)
return embedding
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6, trainable=False):
super().__init__()
self.eps = eps
if trainable:
self.weight = nn.Parameter(torch.ones(dim))
else:
self.weight = None
def forward(self, x):
x_dtype = x.dtype
x = x.float()
norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
if self.weight is not None:
return (x * norm * self.weight).to(dtype=x_dtype)
else:
return (x * norm).to(dtype=x_dtype)
class QKNorm(nn.Module):
"""Normalizing the query and the key independently, as Flux proposes"""
def __init__(self, dim, trainable=False):
super().__init__()
self.query_norm = RMSNorm(dim, trainable=trainable)
self.key_norm = RMSNorm(dim, trainable=trainable)
def forward(self, q, k):
q = self.query_norm(q)
k = self.key_norm(k)
return q, k
class Attention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
is_self_attn=True,
cross_attn_input_size=None,
residual_v=False,
dynamic_softmax_temperature=False,
):
super().__init__()
assert dim % num_heads == 0
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.is_self_attn = is_self_attn
self.residual_v = residual_v
self.dynamic_softmax_temperature = dynamic_softmax_temperature
if is_self_attn:
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
else:
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.context_kv = nn.Linear(cross_attn_input_size, dim * 2, bias=qkv_bias)
self.proj = nn.Linear(dim, dim, bias=False)
if residual_v:
self.lambda_param = nn.Parameter(torch.tensor(0.5).reshape(1))
self.qk_norm = QKNorm(self.head_dim)
def forward(self, x, context=None, v_0=None, rope=None):
if self.is_self_attn:
qkv = self.qkv(x)
qkv = rearrange(qkv, "b l (k h d) -> k b h l d", k=3, h=self.num_heads)
q, k, v = qkv.unbind(0)
if self.residual_v and v_0 is not None:
v = self.lambda_param * v + (1 - self.lambda_param) * v_0
if rope is not None:
# print(q.shape, rope[0].shape, rope[1].shape)
q = apply_rotary_emb(q, rope[0], rope[1])
k = apply_rotary_emb(k, rope[0], rope[1])
# https://arxiv.org/abs/2306.08645
# https://arxiv.org/abs/2410.01104
# ratioonale is that if tokens get larger, categorical distribution get more uniform
# so you want to enlargen entropy.
token_length = q.shape[2]
if self.dynamic_softmax_temperature:
ratio = math.sqrt(math.log(token_length) / math.log(1040.0)) # 1024 + 16
k = k * ratio
q, k = self.qk_norm(q, k)
else:
q = rearrange(self.q(x), "b l (h d) -> b h l d", h=self.num_heads)
kv = rearrange(
self.context_kv(context),
"b l (k h d) -> k b h l d",
k=2,
h=self.num_heads,
)
k, v = kv.unbind(0)
q, k = self.qk_norm(q, k)
x = F.scaled_dot_product_attention(q, k, v)
x = rearrange(x, "b h l d -> b l (h d)")
x = self.proj(x)
return x, v if self.is_self_attn else None
class DiTBlock(nn.Module):
def __init__(
self,
hidden_size,
cross_attn_input_size,
num_heads,
mlp_ratio=4.0,
qkv_bias=True,
residual_v=False,
dynamic_softmax_temperature=False,
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = RMSNorm(hidden_size, trainable=qkv_bias)
self.self_attn = Attention(
hidden_size,
num_heads=num_heads,
qkv_bias=qkv_bias,
is_self_attn=True,
residual_v=residual_v,
dynamic_softmax_temperature=dynamic_softmax_temperature,
)
if cross_attn_input_size is not None:
self.norm2 = RMSNorm(hidden_size, trainable=qkv_bias)
self.cross_attn = Attention(
hidden_size,
num_heads=num_heads,
qkv_bias=qkv_bias,
is_self_attn=False,
cross_attn_input_size=cross_attn_input_size,
dynamic_softmax_temperature=dynamic_softmax_temperature,
)
else:
self.norm2 = None
self.cross_attn = None
self.norm3 = RMSNorm(hidden_size, trainable=qkv_bias)
mlp_hidden = int(hidden_size * mlp_ratio)
self.mlp = nn.Sequential(
nn.Linear(hidden_size, mlp_hidden),
nn.GELU(),
nn.Linear(mlp_hidden, hidden_size),
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 9 * hidden_size, bias=True))
self.adaLN_modulation[-1].weight.data.zero_()
self.adaLN_modulation[-1].bias.data.zero_()
# @torch.compile(mode='reduce-overhead')
def forward(self, x, context, c, v_0=None, rope=None):
(
shift_sa,
scale_sa,
gate_sa,
shift_ca,
scale_ca,
gate_ca,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.adaLN_modulation(c).chunk(9, dim=1)
scale_sa = scale_sa[:, None, :]
scale_ca = scale_ca[:, None, :]
scale_mlp = scale_mlp[:, None, :]
shift_sa = shift_sa[:, None, :]
shift_ca = shift_ca[:, None, :]
shift_mlp = shift_mlp[:, None, :]
gate_sa = gate_sa[:, None, :]
gate_ca = gate_ca[:, None, :]
gate_mlp = gate_mlp[:, None, :]
norm_x = self.norm1(x.clone())
norm_x = norm_x * (1 + scale_sa) + shift_sa
attn_out, v = self.self_attn(norm_x, v_0=v_0, rope=rope)
x = x + attn_out * gate_sa
if self.norm2 is not None:
norm_x = self.norm2(x)
norm_x = norm_x * (1 + scale_ca) + shift_ca
x = x + self.cross_attn(norm_x, context)[0] * gate_ca
norm_x = self.norm3(x)
norm_x = norm_x * (1 + scale_mlp) + shift_mlp
x = x + self.mlp(norm_x) * gate_mlp
return x, v
class PatchEmbed(nn.Module):
def __init__(self, patch_size=16, in_channels=3, embed_dim=768):
super().__init__()
self.patch_proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
self.patch_size = patch_size
def forward(self, x):
B, C, H, W = x.shape
x = self.patch_proj(x)
x = rearrange(x, "b c h w -> b (h w) c")
return x
class TwoDimRotary(torch.nn.Module):
def __init__(self, dim, base=10000, h=256, w=256):
super().__init__()
self.inv_freq = torch.FloatTensor([1.0 / (base ** (i / dim)) for i in range(0, dim, 2)])
self.h = h
self.w = w
t_h = torch.arange(h, dtype=torch.float32)
t_w = torch.arange(w, dtype=torch.float32)
freqs_h = torch.outer(t_h, self.inv_freq).unsqueeze(1) # h, 1, d / 2
freqs_w = torch.outer(t_w, self.inv_freq).unsqueeze(0) # 1, w, d / 2
freqs_h = freqs_h.repeat(1, w, 1) # h, w, d / 2
freqs_w = freqs_w.repeat(h, 1, 1) # h, w, d / 2
freqs_hw = torch.cat([freqs_h, freqs_w], 2) # h, w, d
self.register_buffer("freqs_hw_cos", freqs_hw.cos())
self.register_buffer("freqs_hw_sin", freqs_hw.sin())
def forward(self, x, height_width=None, extend_with_register_tokens=0):
if height_width is not None:
this_h, this_w = height_width
else:
this_hw = x.shape[1]
this_h, this_w = int(this_hw**0.5), int(this_hw**0.5)
cos = self.freqs_hw_cos[0 : this_h, 0 : this_w]
sin = self.freqs_hw_sin[0 : this_h, 0 : this_w]
cos = cos.clone().reshape(this_h * this_w, -1)
sin = sin.clone().reshape(this_h * this_w, -1)
# append N of zero-attn tokens
if extend_with_register_tokens > 0:
cos = torch.cat(
[
torch.ones(extend_with_register_tokens, cos.shape[1]).to(cos.device),
cos,
],
0,
)
sin = torch.cat(
[
torch.zeros(extend_with_register_tokens, sin.shape[1]).to(sin.device),
sin,
],
0,
)
return cos[None, None, :, :], sin[None, None, :, :] # [1, 1, T + N, Attn-dim]
def apply_rotary_emb(x, cos, sin):
orig_dtype = x.dtype
x = x.to(dtype=torch.float32)
assert x.ndim == 4 # multihead attention
d = x.shape[3] // 2
x1 = x[..., :d]
x2 = x[..., d:]
y1 = x1 * cos + x2 * sin
y2 = x1 * (-sin) + x2 * cos
return torch.cat([y1, y2], 3).to(dtype=orig_dtype)
class DiT(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin): # type: ignore[misc]
@register_to_config
def __init__(
self,
in_channels=4,
patch_size=2,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
cross_attn_input_size=128,
residual_v=False,
train_bias_and_rms=True,
use_rope=True,
gradient_checkpoint=False,
dynamic_softmax_temperature=False,
rope_base=10000,
):
super().__init__()
self.patch_embed = PatchEmbed(patch_size, in_channels, hidden_size)
if use_rope:
self.rope = TwoDimRotary(hidden_size // (2 * num_heads), base=rope_base, h=512, w=512)
else:
self.positional_embedding = nn.Parameter(torch.zeros(1, 2048, hidden_size))
self.register_tokens = nn.Parameter(torch.randn(1, 16, hidden_size))
self.time_embed = nn.Sequential(
nn.Linear(hidden_size, 4 * hidden_size),
nn.SiLU(),
nn.Linear(4 * hidden_size, hidden_size),
)
self.blocks = nn.ModuleList(
[
DiTBlock(
hidden_size=hidden_size,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
cross_attn_input_size=cross_attn_input_size,
residual_v=residual_v,
qkv_bias=train_bias_and_rms,
dynamic_softmax_temperature=dynamic_softmax_temperature,
)
for _ in range(depth)
]
)
self.final_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
self.final_norm = RMSNorm(hidden_size, trainable=train_bias_and_rms)
self.final_proj = nn.Linear(hidden_size, patch_size * patch_size * in_channels)
nn.init.zeros_(self.final_modulation[-1].weight)
nn.init.zeros_(self.final_modulation[-1].bias)
nn.init.zeros_(self.final_proj.weight)
nn.init.zeros_(self.final_proj.bias)
self.paramstatus = {}
for n, p in self.named_parameters():
self.paramstatus[n] = {
"shape": p.shape,
"requires_grad": p.requires_grad,
}
def save_lora_weights(self, save_directory):
"""Save LoRA weights to a file"""
lora_state_dict = get_peft_model_state_dict(self)
torch.save(lora_state_dict, f"{save_directory}/lora_weights.pt")
def load_lora_weights(self, load_directory):
"""Load LoRA weights from a file"""
lora_state_dict = torch.load(f"{load_directory}/lora_weights.pt")
set_peft_model_state_dict(self, lora_state_dict)
@apply_forward_hook
def forward(self, x, context, timesteps):
b, c, h, w = x.shape
x = self.patch_embed(x) # b, T, d
x = torch.cat([self.register_tokens.repeat(b, 1, 1), x], 1) # b, T + N, d
if self.config.use_rope:
cos, sin = self.rope(
x,
extend_with_register_tokens=16,
height_width=(h // self.config.patch_size, w // self.config.patch_size),
)
else:
x = x + self.positional_embedding.repeat(b, 1, 1)[:, : x.shape[1], :]
cos, sin = None, None
t_emb = timestep_embedding(timesteps * 1000, self.config.hidden_size).to(x.device, dtype=x.dtype)
t_emb = self.time_embed(t_emb)
v_0 = None
for _idx, block in enumerate(self.blocks):
if self.config.gradient_checkpoint:
x, v = torch.utils.checkpoint.checkpoint(
block,
x,
context,
t_emb,
v_0,
(cos, sin),
use_reentrant=True,
)
else:
x, v = block(x, context, t_emb, v_0, (cos, sin))
if v_0 is None:
v_0 = v
x = x[:, 16:, :]
final_shift, final_scale = self.final_modulation(t_emb).chunk(2, dim=1)
x = self.final_norm(x)
x = x * (1 + final_scale[:, None, :]) + final_shift[:, None, :]
x = self.final_proj(x)
x = rearrange(
x,
"b (h w) (p1 p2 c) -> b c (h p1) (w p2)",
h=h // self.config.patch_size,
w=w // self.config.patch_size,
p1=self.config.patch_size,
p2=self.config.patch_size,
)
return x
if __name__ == "__main__":
model = DiT(
in_channels=4,
patch_size=2,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
cross_attn_input_size=128,
residual_v=False,
train_bias_and_rms=True,
use_rope=True,
).cuda()
print(
model(
torch.randn(1, 4, 64, 64).cuda(),
torch.randn(1, 37, 128).cuda(),
torch.tensor([1.0]).cuda(),
)
)