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import math
from dataclasses import dataclass
import jax
import jax.numpy as jnp
import numpy as np
from flax import nnx
@dataclass
class DiTConfig:
input_dim: int
hidden_dim: int
num_blocks: int
num_heads: int
patch_size: int
patch_stride: int
time_freq_dim: int
time_max_period: int
mlp_ratio: int
use_bias: bool
padding: str
pos_embed_cls_token: bool
pos_embed_extra_tokens: int
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 = jnp.arange(grid_size, dtype=jnp.float32)
grid_w = jnp.arange(grid_size, dtype=jnp.float32)
grid = jnp.meshgrid(grid_w, grid_h) # here w goes first
grid = jnp.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 = jnp.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 = jnp.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.0
omega = 1.0 / 16**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = jnp.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = jnp.sin(out) # (M, D/2)
emb_cos = jnp.cos(out) # (M, D/2)
emb = jnp.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
class PatchEmbedding(nnx.Module):
"""Patch embedding module."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.cnn = nnx.Conv(
config.input_dim,
config.hidden_dim,
kernel_size=(config.patch_size, config.patch_size),
strides=(config.patch_stride, config.patch_stride),
padding=config.padding,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
)
def __call__(self, x):
return self.cnn(x)
class TimeEmbedding(nnx.Module):
"""Time embedding module."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.freq_dim = config.time_freq_dim
self.max_period = config.time_max_period
self.fc1 = nnx.Linear(
self.freq_dim, config.hidden_dim, use_bias=config.use_bias, rngs=rngs, dtype=jnp.bfloat16
)
self.fc2 = nnx.Linear(
config.hidden_dim, config.hidden_dim, use_bias=config.use_bias, rngs=rngs, dtype=jnp.bfloat16
)
@staticmethod
def cosine_embedding(t, dim, max_period):
assert dim % 2 == 0
half = dim // 2
freqs = jnp.exp(
-math.log(max_period)
* jnp.arange(start=0, stop=half, dtype=jnp.float32)
/ half
)
args = t[:, None] * freqs[None, :] * 1024
embedding = jnp.concatenate([jnp.cos(args), jnp.sin(args)], axis=-1)
return embedding
def __call__(self, t):
t_freq = self.cosine_embedding(t, self.freq_dim, self.max_period)
t_embed = self.fc1(t_freq)
t_embed = nnx.silu(t_embed)
t_embed = self.fc2(t_embed)
return t_embed
class MLP(nnx.Module):
"""MLP module."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.fc1 = nnx.Linear(
config.hidden_dim,
config.hidden_dim * config.mlp_ratio,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
)
self.fc2 = nnx.Linear(
config.hidden_dim * config.mlp_ratio,
config.hidden_dim,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
)
def __call__(self, x):
x = self.fc1(x)
x = nnx.silu(x)
x = self.fc2(x)
return x
class SelfAttention(nnx.Module):
"""Self attention module."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.fc = nnx.Linear(
config.hidden_dim,
3 * config.hidden_dim,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
)
self.heads = config.num_heads
self.head_dim = config.hidden_dim // config.num_heads
assert config.hidden_dim % config.num_heads == 0
self.q_norm = nnx.RMSNorm(num_features=self.head_dim, use_scale=True, rngs=rngs)
self.k_norm = nnx.RMSNorm(num_features=self.head_dim, use_scale=True, rngs=rngs)
def __call__(self, x):
q, k, v = jnp.split(self.fc(x), 3, axis=-1)
# reshape q, k v, to N, T, H, D
q = q.reshape(q.shape[0], q.shape[1], self.heads, self.head_dim)
k = k.reshape(k.shape[0], k.shape[1], self.heads, self.head_dim)
v = v.reshape(v.shape[0], v.shape[1], self.heads, self.head_dim)
q = self.q_norm(q)
k = self.k_norm(k)
o = jax.nn.dot_product_attention(q, k, v, is_causal=False)
o = o.reshape(o.shape[0], o.shape[1], self.heads * self.head_dim)
return o
def modulate(x, shift, scale):
return x * (1 + scale[:, None, :]) + shift[:, None, :]
class TransformerBlock(nnx.Module):
"""Transformer block."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.norm1 = nnx.RMSNorm(
num_features=config.hidden_dim, use_scale=False, rngs=rngs
)
self.attn = SelfAttention(config, rngs=rngs)
self.norm2 = nnx.RMSNorm(
num_features=config.hidden_dim, use_scale=False, rngs=rngs
)
self.mlp = MLP(config, rngs=rngs)
self.adalm_modulation = nnx.Sequential(
nnx.silu,
nnx.Linear(
config.hidden_dim,
6 * config.hidden_dim,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
),
)
def __call__(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = jnp.split(
self.adalm_modulation(c), 6, axis=-1
)
attn_x = self.norm1(x)
attn_x = modulate(attn_x, shift_msa, scale_msa)
x = x + gate_msa[:, None, :] * self.attn(attn_x)
mlp_x = self.norm2(x)
mlp_x = modulate(mlp_x, shift_mlp, scale_mlp)
x = x + gate_mlp[:, None, :] * self.mlp(mlp_x)
return x
class FinalLayer(nnx.Module):
"""Final layer."""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.norm = nnx.RMSNorm(
num_features=config.hidden_dim, use_scale=False, rngs=rngs
)
self.conv = nnx.ConvTranspose(
config.hidden_dim,
config.input_dim,
kernel_size=(config.patch_size, config.patch_size),
strides=(config.patch_stride, config.patch_stride),
padding=config.padding,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
)
self.adalm_modulation = nnx.Sequential(
nnx.silu,
nnx.Linear(
config.hidden_dim,
2 * config.hidden_dim,
use_bias=config.use_bias,
rngs=rngs,
dtype=jnp.bfloat16,
),
)
def __call__(self, x, c):
shift, scale = jnp.split(self.adalm_modulation(c), 2, axis=-1)
x = self.norm(x)
x = modulate(x, shift, scale)
# reshape to N, H, W, C
H = W = int(x.shape[1] ** 0.5)
x = x.reshape(x.shape[0], H, W, x.shape[-1])
x = self.conv(x)
return x
class DiT(nnx.Module):
"""Diffusion Transformer"""
def __init__(self, config: DiTConfig, *, rngs: nnx.Rngs):
super().__init__()
self.config = config
self.time_embedding = TimeEmbedding(config, rngs=rngs)
self.patch_embedding = PatchEmbedding(config, rngs=rngs)
self.blocks = [
TransformerBlock(config, rngs=rngs) for _ in range(config.num_blocks)
]
self.final_layer = FinalLayer(config, rngs=rngs)
def __call__(self, xt, t):
t = self.time_embedding(t)
x = self.patch_embedding(xt)
N, H, W, D = x.shape
x = x.reshape(N, H * W, D)
x = x + get_2d_sincos_pos_embed(
D,
H,
cls_token=self.config.pos_embed_cls_token,
extra_tokens=self.config.pos_embed_extra_tokens,
).reshape(1, H * W, D)
c = t
for block in self.blocks:
x = block(x, c)
x = self.final_layer(x, c)
return x
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