mirror of
https://github.com/ostris/ai-toolkit.git
synced 2026-02-24 22:33:56 +00:00
721 lines
26 KiB
Python
721 lines
26 KiB
Python
import math
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
from einops import rearrange
|
|
from torch import Tensor, nn
|
|
import torch.nn.functional as F
|
|
|
|
from .math import attention, rope
|
|
from functools import lru_cache
|
|
|
|
|
|
class EmbedND(nn.Module):
|
|
def __init__(self, dim: int, theta: int, axes_dim: list[int]):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.theta = theta
|
|
self.axes_dim = axes_dim
|
|
|
|
def forward(self, ids: Tensor) -> Tensor:
|
|
n_axes = ids.shape[-1]
|
|
emb = torch.cat(
|
|
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
|
|
dim=-3,
|
|
)
|
|
|
|
return emb.unsqueeze(1)
|
|
|
|
|
|
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
|
|
"""
|
|
Create sinusoidal timestep embeddings.
|
|
:param t: a 1-D Tensor of N indices, one per batch element.
|
|
These may 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.
|
|
"""
|
|
t = time_factor * t
|
|
half = dim // 2
|
|
freqs = torch.exp(
|
|
-math.log(max_period)
|
|
* torch.arange(start=0, end=half, dtype=torch.float32)
|
|
/ half
|
|
).to(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)
|
|
if torch.is_floating_point(t):
|
|
embedding = embedding.to(t)
|
|
return embedding
|
|
|
|
|
|
class MLPEmbedder(nn.Module):
|
|
def __init__(self, in_dim: int, hidden_dim: int):
|
|
super().__init__()
|
|
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
|
|
self.silu = nn.SiLU()
|
|
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return self.out_layer(self.silu(self.in_layer(x)))
|
|
|
|
|
|
class RMSNorm(torch.nn.Module):
|
|
def __init__(self, dim: int, use_compiled: bool = False):
|
|
super().__init__()
|
|
self.scale = nn.Parameter(torch.ones(dim))
|
|
self.use_compiled = use_compiled
|
|
|
|
def _forward(self, x: Tensor):
|
|
x_dtype = x.dtype
|
|
x = x.float()
|
|
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
|
|
return (x * rrms).to(dtype=x_dtype) * self.scale
|
|
|
|
def forward(self, x: Tensor):
|
|
return F.rms_norm(x, self.scale.shape, weight=self.scale, eps=1e-6)
|
|
# if self.use_compiled:
|
|
# return torch.compile(self._forward)(x)
|
|
# else:
|
|
# return self._forward(x)
|
|
|
|
|
|
def distribute_modulations(tensor: torch.Tensor, depth_single_blocks, depth_double_blocks):
|
|
"""
|
|
Distributes slices of the tensor into the block_dict as ModulationOut objects.
|
|
|
|
Args:
|
|
tensor (torch.Tensor): Input tensor with shape [batch_size, vectors, dim].
|
|
"""
|
|
batch_size, vectors, dim = tensor.shape
|
|
|
|
block_dict = {}
|
|
|
|
# HARD CODED VALUES! lookup table for the generated vectors
|
|
# TODO: move this into chroma config!
|
|
# Add 38 single mod blocks
|
|
for i in range(depth_single_blocks):
|
|
key = f"single_blocks.{i}.modulation.lin"
|
|
block_dict[key] = None
|
|
|
|
# Add 19 image double blocks
|
|
for i in range(depth_double_blocks):
|
|
key = f"double_blocks.{i}.img_mod.lin"
|
|
block_dict[key] = None
|
|
|
|
# Add 19 text double blocks
|
|
for i in range(depth_double_blocks):
|
|
key = f"double_blocks.{i}.txt_mod.lin"
|
|
block_dict[key] = None
|
|
|
|
# Add the final layer
|
|
block_dict["final_layer.adaLN_modulation.1"] = None
|
|
# 6.2b version
|
|
# block_dict["lite_double_blocks.4.img_mod.lin"] = None
|
|
# block_dict["lite_double_blocks.4.txt_mod.lin"] = None
|
|
|
|
idx = 0 # Index to keep track of the vector slices
|
|
|
|
for key in block_dict.keys():
|
|
if "single_blocks" in key:
|
|
# Single block: 1 ModulationOut
|
|
block_dict[key] = ModulationOut(
|
|
shift=tensor[:, idx : idx + 1, :],
|
|
scale=tensor[:, idx + 1 : idx + 2, :],
|
|
gate=tensor[:, idx + 2 : idx + 3, :],
|
|
)
|
|
idx += 3 # Advance by 3 vectors
|
|
|
|
elif "img_mod" in key:
|
|
# Double block: List of 2 ModulationOut
|
|
double_block = []
|
|
for _ in range(2): # Create 2 ModulationOut objects
|
|
double_block.append(
|
|
ModulationOut(
|
|
shift=tensor[:, idx : idx + 1, :],
|
|
scale=tensor[:, idx + 1 : idx + 2, :],
|
|
gate=tensor[:, idx + 2 : idx + 3, :],
|
|
)
|
|
)
|
|
idx += 3 # Advance by 3 vectors per ModulationOut
|
|
block_dict[key] = double_block
|
|
|
|
elif "txt_mod" in key:
|
|
# Double block: List of 2 ModulationOut
|
|
double_block = []
|
|
for _ in range(2): # Create 2 ModulationOut objects
|
|
double_block.append(
|
|
ModulationOut(
|
|
shift=tensor[:, idx : idx + 1, :],
|
|
scale=tensor[:, idx + 1 : idx + 2, :],
|
|
gate=tensor[:, idx + 2 : idx + 3, :],
|
|
)
|
|
)
|
|
idx += 3 # Advance by 3 vectors per ModulationOut
|
|
block_dict[key] = double_block
|
|
|
|
elif "final_layer" in key:
|
|
# Final layer: 1 ModulationOut
|
|
block_dict[key] = [
|
|
tensor[:, idx : idx + 1, :],
|
|
tensor[:, idx + 1 : idx + 2, :],
|
|
]
|
|
idx += 2 # Advance by 3 vectors
|
|
|
|
return block_dict
|
|
|
|
|
|
|
|
class NerfEmbedder(nn.Module):
|
|
"""
|
|
An embedder module that combines input features with a 2D positional
|
|
encoding that mimics the Discrete Cosine Transform (DCT).
|
|
|
|
This module takes an input tensor of shape (B, P^2, C), where P is the
|
|
patch size, and enriches it with positional information before projecting
|
|
it to a new hidden size.
|
|
"""
|
|
def __init__(self, in_channels, hidden_size_input, max_freqs):
|
|
"""
|
|
Initializes the NerfEmbedder.
|
|
|
|
Args:
|
|
in_channels (int): The number of channels in the input tensor.
|
|
hidden_size_input (int): The desired dimension of the output embedding.
|
|
max_freqs (int): The number of frequency components to use for both
|
|
the x and y dimensions of the positional encoding.
|
|
The total number of positional features will be max_freqs^2.
|
|
"""
|
|
super().__init__()
|
|
self.max_freqs = max_freqs
|
|
self.hidden_size_input = hidden_size_input
|
|
|
|
# A linear layer to project the concatenated input features and
|
|
# positional encodings to the final output dimension.
|
|
self.embedder = nn.Sequential(
|
|
nn.Linear(in_channels + max_freqs**2, hidden_size_input)
|
|
)
|
|
|
|
@lru_cache(maxsize=4)
|
|
def fetch_pos(self, patch_size, device, dtype):
|
|
"""
|
|
Generates and caches 2D DCT-like positional embeddings for a given patch size.
|
|
|
|
The LRU cache is a performance optimization that avoids recomputing the
|
|
same positional grid on every forward pass.
|
|
|
|
Args:
|
|
patch_size (int): The side length of the square input patch.
|
|
device: The torch device to create the tensors on.
|
|
dtype: The torch dtype for the tensors.
|
|
|
|
Returns:
|
|
A tensor of shape (1, patch_size^2, max_freqs^2) containing the
|
|
positional embeddings.
|
|
"""
|
|
# Create normalized 1D coordinate grids from 0 to 1.
|
|
pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
|
|
pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
|
|
|
|
# Create a 2D meshgrid of coordinates.
|
|
pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij")
|
|
|
|
# Reshape positions to be broadcastable with frequencies.
|
|
# Shape becomes (patch_size^2, 1, 1).
|
|
pos_x = pos_x.reshape(-1, 1, 1)
|
|
pos_y = pos_y.reshape(-1, 1, 1)
|
|
|
|
# Create a 1D tensor of frequency values from 0 to max_freqs-1.
|
|
freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device)
|
|
|
|
# Reshape frequencies to be broadcastable for creating 2D basis functions.
|
|
# freqs_x shape: (1, max_freqs, 1)
|
|
# freqs_y shape: (1, 1, max_freqs)
|
|
freqs_x = freqs[None, :, None]
|
|
freqs_y = freqs[None, None, :]
|
|
|
|
# A custom weighting coefficient, not part of standard DCT.
|
|
# This seems to down-weight the contribution of higher-frequency interactions.
|
|
coeffs = (1 + freqs_x * freqs_y) ** -1
|
|
|
|
# Calculate the 1D cosine basis functions for x and y coordinates.
|
|
# This is the core of the DCT formulation.
|
|
dct_x = torch.cos(pos_x * freqs_x * torch.pi)
|
|
dct_y = torch.cos(pos_y * freqs_y * torch.pi)
|
|
|
|
# Combine the 1D basis functions to create 2D basis functions by element-wise
|
|
# multiplication, and apply the custom coefficients. Broadcasting handles the
|
|
# combination of all (pos_x, freqs_x) with all (pos_y, freqs_y).
|
|
# The result is flattened into a feature vector for each position.
|
|
dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2)
|
|
|
|
return dct
|
|
|
|
def forward(self, inputs):
|
|
"""
|
|
Forward pass for the embedder.
|
|
|
|
Args:
|
|
inputs (Tensor): The input tensor of shape (B, P^2, C).
|
|
|
|
Returns:
|
|
Tensor: The output tensor of shape (B, P^2, hidden_size_input).
|
|
"""
|
|
# Get the batch size, number of pixels, and number of channels.
|
|
B, P2, C = inputs.shape
|
|
# Store the original dtype to cast back to at the end.
|
|
original_dtype = inputs.dtype
|
|
# Force all operations within this module to run in fp32.
|
|
with torch.autocast("cuda", enabled=False):
|
|
# Infer the patch side length from the number of pixels (P^2).
|
|
patch_size = int(P2 ** 0.5)
|
|
|
|
inputs = inputs.float()
|
|
# Fetch the pre-computed or cached positional embeddings.
|
|
dct = self.fetch_pos(patch_size, inputs.device, torch.float32)
|
|
|
|
# Repeat the positional embeddings for each item in the batch.
|
|
dct = dct.repeat(B, 1, 1)
|
|
|
|
# Concatenate the original input features with the positional embeddings
|
|
# along the feature dimension.
|
|
inputs = torch.cat([inputs, dct], dim=-1)
|
|
|
|
# Project the combined tensor to the target hidden size.
|
|
inputs = self.embedder.float()(inputs)
|
|
|
|
return inputs.to(original_dtype)
|
|
|
|
|
|
|
|
class NerfGLUBlock(nn.Module):
|
|
"""
|
|
A NerfBlock using a Gated Linear Unit (GLU) like MLP.
|
|
"""
|
|
def __init__(self, hidden_size_s, hidden_size_x, mlp_ratio, use_compiled):
|
|
super().__init__()
|
|
# The total number of parameters for the MLP is increased to accommodate
|
|
# the gate, value, and output projection matrices.
|
|
# We now need to generate parameters for 3 matrices.
|
|
total_params = 3 * hidden_size_x**2 * mlp_ratio
|
|
self.param_generator = nn.Linear(hidden_size_s, total_params)
|
|
self.norm = RMSNorm(hidden_size_x, use_compiled)
|
|
self.mlp_ratio = mlp_ratio
|
|
# nn.init.zeros_(self.param_generator.weight)
|
|
# nn.init.zeros_(self.param_generator.bias)
|
|
|
|
|
|
def forward(self, x, s):
|
|
batch_size, num_x, hidden_size_x = x.shape
|
|
mlp_params = self.param_generator(s)
|
|
|
|
# Split the generated parameters into three parts for the gate, value, and output projection.
|
|
fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1)
|
|
|
|
# Reshape the parameters into matrices for batch matrix multiplication.
|
|
fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
|
|
fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
|
|
fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x)
|
|
|
|
# Normalize the generated weight matrices as in the original implementation.
|
|
fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2)
|
|
fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2)
|
|
fc2 = torch.nn.functional.normalize(fc2, dim=-2)
|
|
|
|
res_x = x
|
|
x = self.norm(x)
|
|
|
|
# Apply the final output projection.
|
|
x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2)
|
|
|
|
x = x + res_x
|
|
return x
|
|
|
|
|
|
class NerfFinalLayer(nn.Module):
|
|
def __init__(self, hidden_size, out_channels, use_compiled):
|
|
super().__init__()
|
|
self.norm = RMSNorm(hidden_size, use_compiled=use_compiled)
|
|
self.linear = nn.Linear(hidden_size, out_channels)
|
|
nn.init.zeros_(self.linear.weight)
|
|
nn.init.zeros_(self.linear.bias)
|
|
|
|
def forward(self, x):
|
|
x = self.norm(x)
|
|
x = self.linear(x)
|
|
return x
|
|
|
|
|
|
class NerfFinalLayerConv(nn.Module):
|
|
def __init__(self, hidden_size, out_channels, use_compiled):
|
|
super().__init__()
|
|
self.norm = RMSNorm(hidden_size, use_compiled=use_compiled)
|
|
|
|
# replace nn.Linear with nn.Conv2d since linear is just pointwise conv
|
|
self.conv = nn.Conv2d(
|
|
in_channels=hidden_size,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
padding=1
|
|
)
|
|
nn.init.zeros_(self.conv.weight)
|
|
nn.init.zeros_(self.conv.bias)
|
|
|
|
def forward(self, x):
|
|
# shape: [N, C, H, W] !
|
|
# RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
|
|
# So, we permute the dimensions to make the channel dimension the last one.
|
|
x_permuted = x.permute(0, 2, 3, 1) # Shape becomes [N, H, W, C]
|
|
|
|
# Apply normalization on the feature/channel dimension
|
|
x_norm = self.norm(x_permuted)
|
|
|
|
# Permute back to the original dimension order for the convolution
|
|
x_norm_permuted = x_norm.permute(0, 3, 1, 2) # Shape becomes [N, C, H, W]
|
|
|
|
# Apply the 3x3 convolution
|
|
x = self.conv(x_norm_permuted)
|
|
return x
|
|
|
|
|
|
class Approximator(nn.Module):
|
|
def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers=4):
|
|
super().__init__()
|
|
self.in_proj = nn.Linear(in_dim, hidden_dim, bias=True)
|
|
self.layers = nn.ModuleList(
|
|
[MLPEmbedder(hidden_dim, hidden_dim) for x in range(n_layers)]
|
|
)
|
|
self.norms = nn.ModuleList([RMSNorm(hidden_dim) for x in range(n_layers)])
|
|
self.out_proj = nn.Linear(hidden_dim, out_dim)
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
x = x.to(self.in_proj.weight.dtype)
|
|
x = self.in_proj(x)
|
|
|
|
for layer, norms in zip(self.layers, self.norms):
|
|
x = x + layer(norms(x))
|
|
|
|
x = self.out_proj(x)
|
|
|
|
return x
|
|
|
|
|
|
class QKNorm(torch.nn.Module):
|
|
def __init__(self, dim: int, use_compiled: bool = False):
|
|
super().__init__()
|
|
self.query_norm = RMSNorm(dim, use_compiled=use_compiled)
|
|
self.key_norm = RMSNorm(dim, use_compiled=use_compiled)
|
|
self.use_compiled = use_compiled
|
|
|
|
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
|
|
q = self.query_norm(q)
|
|
k = self.key_norm(k)
|
|
return q.to(v), k.to(v)
|
|
|
|
|
|
class SelfAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_heads: int = 8,
|
|
qkv_bias: bool = False,
|
|
use_compiled: bool = False,
|
|
):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.norm = QKNorm(head_dim, use_compiled=use_compiled)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.use_compiled = use_compiled
|
|
|
|
def forward(self, x: Tensor, pe: Tensor) -> Tensor:
|
|
qkv = self.qkv(x)
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
x = attention(q, k, v, pe=pe)
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
|
|
@dataclass
|
|
class ModulationOut:
|
|
shift: Tensor
|
|
scale: Tensor
|
|
gate: Tensor
|
|
|
|
|
|
def _modulation_shift_scale_fn(x, scale, shift):
|
|
return (1 + scale) * x + shift
|
|
|
|
|
|
def _modulation_gate_fn(x, gate, gate_params):
|
|
return x + gate * gate_params
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float,
|
|
qkv_bias: bool = False,
|
|
use_compiled: bool = False,
|
|
):
|
|
super().__init__()
|
|
|
|
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.num_heads = num_heads
|
|
self.hidden_size = hidden_size
|
|
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_attn = SelfAttention(
|
|
dim=hidden_size,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
use_compiled=use_compiled,
|
|
)
|
|
|
|
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_attn = SelfAttention(
|
|
dim=hidden_size,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
use_compiled=use_compiled,
|
|
)
|
|
|
|
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
self.use_compiled = use_compiled
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def modulation_shift_scale_fn(self, x, scale, shift):
|
|
if self.use_compiled:
|
|
return torch.compile(_modulation_shift_scale_fn)(x, scale, shift)
|
|
else:
|
|
return _modulation_shift_scale_fn(x, scale, shift)
|
|
|
|
def modulation_gate_fn(self, x, gate, gate_params):
|
|
if self.use_compiled:
|
|
return torch.compile(_modulation_gate_fn)(x, gate, gate_params)
|
|
else:
|
|
return _modulation_gate_fn(x, gate, gate_params)
|
|
|
|
def forward(
|
|
self,
|
|
img: Tensor,
|
|
txt: Tensor,
|
|
pe: Tensor,
|
|
distill_vec: list[ModulationOut],
|
|
mask: Tensor,
|
|
) -> tuple[Tensor, Tensor]:
|
|
(img_mod1, img_mod2), (txt_mod1, txt_mod2) = distill_vec
|
|
|
|
# prepare image for attention
|
|
img_modulated = self.img_norm1(img)
|
|
# replaced with compiled fn
|
|
# img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
|
|
img_modulated = self.modulation_shift_scale_fn(
|
|
img_modulated, img_mod1.scale, img_mod1.shift
|
|
)
|
|
img_qkv = self.img_attn.qkv(img_modulated)
|
|
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
|
|
)
|
|
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
|
|
|
|
# prepare txt for attention
|
|
txt_modulated = self.txt_norm1(txt)
|
|
# replaced with compiled fn
|
|
# txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
|
|
txt_modulated = self.modulation_shift_scale_fn(
|
|
txt_modulated, txt_mod1.scale, txt_mod1.shift
|
|
)
|
|
txt_qkv = self.txt_attn.qkv(txt_modulated)
|
|
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
|
|
)
|
|
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
|
|
|
|
# run actual attention
|
|
q = torch.cat((txt_q, img_q), dim=2)
|
|
k = torch.cat((txt_k, img_k), dim=2)
|
|
v = torch.cat((txt_v, img_v), dim=2)
|
|
|
|
attn = attention(q, k, v, pe=pe, mask=mask)
|
|
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
|
|
|
|
# calculate the img bloks
|
|
# replaced with compiled fn
|
|
# img = img + img_mod1.gate * self.img_attn.proj(img_attn)
|
|
# img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
|
|
img = self.modulation_gate_fn(img, img_mod1.gate, self.img_attn.proj(img_attn))
|
|
img = self.modulation_gate_fn(
|
|
img,
|
|
img_mod2.gate,
|
|
self.img_mlp(
|
|
self.modulation_shift_scale_fn(
|
|
self.img_norm2(img), img_mod2.scale, img_mod2.shift
|
|
)
|
|
),
|
|
)
|
|
|
|
# calculate the txt bloks
|
|
# replaced with compiled fn
|
|
# txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
|
|
# txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
|
|
txt = self.modulation_gate_fn(txt, txt_mod1.gate, self.txt_attn.proj(txt_attn))
|
|
txt = self.modulation_gate_fn(
|
|
txt,
|
|
txt_mod2.gate,
|
|
self.txt_mlp(
|
|
self.modulation_shift_scale_fn(
|
|
self.txt_norm2(txt), txt_mod2.scale, txt_mod2.shift
|
|
)
|
|
),
|
|
)
|
|
|
|
return img, txt
|
|
|
|
|
|
class SingleStreamBlock(nn.Module):
|
|
"""
|
|
A DiT block with parallel linear layers as described in
|
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qk_scale: float | None = None,
|
|
use_compiled: bool = False,
|
|
):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_size
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_size // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
# qkv and mlp_in
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
|
# proj and mlp_out
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
|
|
|
self.norm = QKNorm(head_dim, use_compiled=use_compiled)
|
|
|
|
self.hidden_size = hidden_size
|
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
|
|
self.mlp_act = nn.GELU(approximate="tanh")
|
|
self.use_compiled = use_compiled
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def modulation_shift_scale_fn(self, x, scale, shift):
|
|
if self.use_compiled:
|
|
return torch.compile(_modulation_shift_scale_fn)(x, scale, shift)
|
|
else:
|
|
return _modulation_shift_scale_fn(x, scale, shift)
|
|
|
|
def modulation_gate_fn(self, x, gate, gate_params):
|
|
if self.use_compiled:
|
|
return torch.compile(_modulation_gate_fn)(x, gate, gate_params)
|
|
else:
|
|
return _modulation_gate_fn(x, gate, gate_params)
|
|
|
|
def forward(
|
|
self, x: Tensor, pe: Tensor, distill_vec: list[ModulationOut], mask: Tensor
|
|
) -> Tensor:
|
|
mod = distill_vec
|
|
# replaced with compiled fn
|
|
# x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
|
|
x_mod = self.modulation_shift_scale_fn(self.pre_norm(x), mod.scale, mod.shift)
|
|
qkv, mlp = torch.split(
|
|
self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
|
|
)
|
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
|
|
# compute attention
|
|
attn = attention(q, k, v, pe=pe, mask=mask)
|
|
# compute activation in mlp stream, cat again and run second linear layer
|
|
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
|
|
# replaced with compiled fn
|
|
# return x + mod.gate * output
|
|
return self.modulation_gate_fn(x, mod.gate, output)
|
|
|
|
|
|
class LastLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
patch_size: int,
|
|
out_channels: int,
|
|
use_compiled: bool = False,
|
|
):
|
|
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.use_compiled = use_compiled
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def modulation_shift_scale_fn(self, x, scale, shift):
|
|
if self.use_compiled:
|
|
return torch.compile(_modulation_shift_scale_fn)(x, scale, shift)
|
|
else:
|
|
return _modulation_shift_scale_fn(x, scale, shift)
|
|
|
|
def forward(self, x: Tensor, distill_vec: list[Tensor]) -> Tensor:
|
|
shift, scale = distill_vec
|
|
shift = shift.squeeze(1)
|
|
scale = scale.squeeze(1)
|
|
# replaced with compiled fn
|
|
# x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.modulation_shift_scale_fn(
|
|
self.norm_final(x), scale[:, None, :], shift[:, None, :]
|
|
)
|
|
x = self.linear(x)
|
|
return x
|