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Running
on
Zero
SunderAli17
commited on
Commit
•
2b6b420
1
Parent(s):
75891cd
Create flux/modules/layers.py
Browse files- flux/modules/layers.py +253 -0
flux/modules/layers.py
ADDED
@@ -0,0 +1,253 @@
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1 |
+
import math
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2 |
+
from dataclasses import dataclass
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+
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+
import torch
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+
from einops import rearrange
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+
from torch import Tensor, nn
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+
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+
from flux.math import attention, rope
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+
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+
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+
class EmbedND(nn.Module):
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+
def __init__(self, dim: int, theta: int, axes_dim: list[int]):
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+
super().__init__()
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+
self.dim = dim
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+
self.theta = theta
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+
self.axes_dim = axes_dim
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+
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+
def forward(self, ids: Tensor) -> Tensor:
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+
n_axes = ids.shape[-1]
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+
emb = torch.cat(
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+
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
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+
dim=-3,
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+
)
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+
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+
return emb.unsqueeze(1)
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+
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+
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+
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
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+
"""
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+
Create sinusoidal timestep embeddings.
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+
:param t: a 1-D Tensor of N indices, one per batch element.
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+
These may be fractional.
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+
:param dim: the dimension of the output.
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+
:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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+
"""
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+
t = time_factor * t
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+
half = dim // 2
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+
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
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t.device
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+
)
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+
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args = t[:, None].float() * freqs[None]
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+
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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+
if dim % 2:
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+
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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if torch.is_floating_point(t):
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embedding = embedding.to(t)
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return embedding
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+
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+
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class MLPEmbedder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int):
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super().__init__()
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self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
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self.silu = nn.SiLU()
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self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
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+
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def forward(self, x: Tensor) -> Tensor:
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return self.out_layer(self.silu(self.in_layer(x)))
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+
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+
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.scale = nn.Parameter(torch.ones(dim))
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def forward(self, x: Tensor):
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x_dtype = x.dtype
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x = x.float()
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rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
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return (x * rrms).to(dtype=x_dtype) * self.scale
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+
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class QKNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.query_norm = RMSNorm(dim)
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self.key_norm = RMSNorm(dim)
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+
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
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q = self.query_norm(q)
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k = self.key_norm(k)
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return q.to(v), k.to(v)
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+
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+
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class SelfAttention(nn.Module):
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def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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+
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.norm = QKNorm(head_dim)
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self.proj = nn.Linear(dim, dim)
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+
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def forward(self, x: Tensor, pe: Tensor) -> Tensor:
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qkv = self.qkv(x)
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q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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q, k = self.norm(q, k, v)
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x = attention(q, k, v, pe=pe)
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x = self.proj(x)
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return x
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+
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@dataclass
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class ModulationOut:
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shift: Tensor
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scale: Tensor
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gate: Tensor
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class Modulation(nn.Module):
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def __init__(self, dim: int, double: bool):
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super().__init__()
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self.is_double = double
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self.multiplier = 6 if double else 3
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+
self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
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+
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+
def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut]:
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+
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
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122 |
+
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+
return (
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+
ModulationOut(*out[:3]),
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+
ModulationOut(*out[3:]) if self.is_double else None,
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+
)
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+
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+
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+
class DoubleStreamBlock(nn.Module):
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+
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
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131 |
+
super().__init__()
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132 |
+
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133 |
+
mlp_hidden_dim = int(hidden_size * mlp_ratio)
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+
self.num_heads = num_heads
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135 |
+
self.hidden_size = hidden_size
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136 |
+
self.img_mod = Modulation(hidden_size, double=True)
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137 |
+
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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138 |
+
self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
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139 |
+
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140 |
+
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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141 |
+
self.img_mlp = nn.Sequential(
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142 |
+
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
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143 |
+
nn.GELU(approximate="tanh"),
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+
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
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+
)
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146 |
+
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+
self.txt_mod = Modulation(hidden_size, double=True)
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148 |
+
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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149 |
+
self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
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150 |
+
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151 |
+
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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152 |
+
self.txt_mlp = nn.Sequential(
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153 |
+
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
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+
nn.GELU(approximate="tanh"),
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+
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
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+
)
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157 |
+
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+
def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
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159 |
+
img_mod1, img_mod2 = self.img_mod(vec)
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160 |
+
txt_mod1, txt_mod2 = self.txt_mod(vec)
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161 |
+
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162 |
+
# prepare image for attention
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163 |
+
img_modulated = self.img_norm1(img)
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164 |
+
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
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165 |
+
img_qkv = self.img_attn.qkv(img_modulated)
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166 |
+
img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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167 |
+
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
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168 |
+
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169 |
+
# prepare txt for attention
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170 |
+
txt_modulated = self.txt_norm1(txt)
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171 |
+
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
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172 |
+
txt_qkv = self.txt_attn.qkv(txt_modulated)
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173 |
+
txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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174 |
+
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
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175 |
+
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176 |
+
# run actual attention
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177 |
+
q = torch.cat((txt_q, img_q), dim=2)
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178 |
+
k = torch.cat((txt_k, img_k), dim=2)
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179 |
+
v = torch.cat((txt_v, img_v), dim=2)
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180 |
+
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181 |
+
attn = attention(q, k, v, pe=pe)
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182 |
+
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
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183 |
+
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184 |
+
# calculate the img bloks
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185 |
+
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
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186 |
+
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
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187 |
+
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188 |
+
# calculate the txt bloks
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189 |
+
txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
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190 |
+
txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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191 |
+
return img, txt
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+
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193 |
+
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194 |
+
class SingleStreamBlock(nn.Module):
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+
"""
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196 |
+
A DiT block with parallel linear layers as described in
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197 |
+
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
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198 |
+
"""
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199 |
+
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200 |
+
def __init__(
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201 |
+
self,
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+
hidden_size: int,
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+
num_heads: int,
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204 |
+
mlp_ratio: float = 4.0,
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+
qk_scale: float = None,
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206 |
+
):
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+
super().__init__()
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+
self.hidden_dim = hidden_size
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+
self.num_heads = num_heads
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210 |
+
head_dim = hidden_size // num_heads
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211 |
+
self.scale = qk_scale or head_dim**-0.5
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212 |
+
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213 |
+
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
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214 |
+
# qkv and mlp_in
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215 |
+
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
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216 |
+
# proj and mlp_out
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217 |
+
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
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218 |
+
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219 |
+
self.norm = QKNorm(head_dim)
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220 |
+
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221 |
+
self.hidden_size = hidden_size
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222 |
+
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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223 |
+
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+
self.mlp_act = nn.GELU(approximate="tanh")
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225 |
+
self.modulation = Modulation(hidden_size, double=False)
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+
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+
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
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228 |
+
mod, _ = self.modulation(vec)
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229 |
+
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
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230 |
+
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
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231 |
+
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232 |
+
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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233 |
+
q, k = self.norm(q, k, v)
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234 |
+
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235 |
+
# compute attention
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236 |
+
attn = attention(q, k, v, pe=pe)
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+
# compute activation in mlp stream, cat again and run second linear layer
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238 |
+
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
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239 |
+
return x + mod.gate * output
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240 |
+
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241 |
+
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242 |
+
class LastLayer(nn.Module):
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243 |
+
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
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244 |
+
super().__init__()
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245 |
+
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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246 |
+
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
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247 |
+
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
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248 |
+
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249 |
+
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
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250 |
+
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
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251 |
+
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
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252 |
+
x = self.linear(x)
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+
return x
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