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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from copy import deepcopy
from typing import Optional, Tuple
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import torch
import torch.nn as nn
from timm.models.layers import DropPath
from torch.nn import Linear, Module, init
from diffusion.model.builder import MODELS
from diffusion.model.nets.basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
from diffusion.model.nets.fastlinear.modules import TritonLiteMLA, TritonMBConvPreGLU
from diffusion.model.nets.sana import Sana, get_2d_sincos_pos_embed
from diffusion.model.nets.sana_blocks import (
Attention,
CaptionEmbedder,
FlashAttention,
LiteLA,
MultiHeadCrossAttention,
PatchEmbedMS,
T2IFinalLayer,
t2i_modulate,
)
from diffusion.model.nets.sana_multi_scale import SanaMS, SanaMSBlock
from diffusion.model.utils import auto_grad_checkpoint, to_2tuple
#############################################################################
# Core Sana Model #
#################################################################################
class ControlSanaMSBlock(Module):
def __init__(self, base_block: SanaMSBlock, block_index: int) -> None:
super().__init__()
self.copied_block = deepcopy(base_block)
self.block_index = block_index
self.hidden_size = hidden_size = base_block.hidden_size
if self.block_index == 0:
self.before_proj = Linear(hidden_size, hidden_size)
self.after_proj = Linear(hidden_size, hidden_size)
def initialize_all_and_copy_from_base(self, base_block):
for name, param in self.named_parameters():
param.requires_grad_(True)
self.copied_block.load_state_dict(base_block.state_dict())
self.train()
if self.block_index == 0:
init.zeros_(self.before_proj.weight)
init.zeros_(self.before_proj.bias)
init.zeros_(self.after_proj.weight)
init.zeros_(self.after_proj.bias)
def forward(self, x, y, t, control_signal, mask=None, HW=None):
if self.block_index == 0:
# the first block
control_signal = self.before_proj(control_signal)
control_signal = self.copied_block(x + control_signal, y, t, mask, HW)
control_signal_skip = self.after_proj(control_signal)
else:
# load from previous control_signal and produce the control_signal for skip connection
control_signal = self.copied_block(control_signal, y, t, mask, HW)
control_signal_skip = self.after_proj(control_signal)
return control_signal, control_signal_skip
@MODELS.register_module()
class SanaMSControlNet(SanaMS):
"""
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,
class_dropout_prob=0.1,
learn_sigma=True,
pred_sigma=True,
drop_path: float = 0.0,
caption_channels=4096,
pe_interpolation=1.0,
config=None,
model_max_length=300,
qk_norm=False,
y_norm=False,
norm_eps=1e-5,
attn_type="flash",
ffn_type="mlp",
use_pe=True,
y_norm_scale_factor=1.0,
patch_embed_kernel=None,
mlp_acts=("silu", "gelu", None),
linear_head_dim=32,
copy_blocks_num=7,
**kwargs,
):
super().__init__(
input_size=input_size,
patch_size=patch_size,
in_channels=in_channels,
hidden_size=hidden_size,
depth=depth,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
class_dropout_prob=class_dropout_prob,
learn_sigma=learn_sigma,
pred_sigma=pred_sigma,
drop_path=drop_path,
caption_channels=caption_channels,
pe_interpolation=pe_interpolation,
config=config,
model_max_length=model_max_length,
qk_norm=qk_norm,
y_norm=y_norm,
norm_eps=norm_eps,
attn_type=attn_type,
ffn_type=ffn_type,
use_pe=use_pe,
y_norm_scale_factor=y_norm_scale_factor,
patch_embed_kernel=patch_embed_kernel,
mlp_acts=mlp_acts,
linear_head_dim=linear_head_dim,
**kwargs,
)
self.h = self.w = 0
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
self.pos_embed_ms = None
kernel_size = patch_embed_kernel or patch_size
self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True)
self.y_embedder = CaptionEmbedder(
in_channels=caption_channels,
hidden_size=hidden_size,
uncond_prob=class_dropout_prob,
act_layer=approx_gelu,
token_num=model_max_length,
)
drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList(
[
SanaMSBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path[i],
input_size=(input_size // patch_size, input_size // patch_size),
qk_norm=qk_norm,
attn_type=attn_type,
ffn_type=ffn_type,
mlp_acts=mlp_acts,
linear_head_dim=linear_head_dim,
)
for i in range(depth)
]
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
# define controlnet
self.copy_blocks_num = copy_blocks_num
self.controlnet = nn.ModuleList([ControlSanaMSBlock(self.blocks[i], i) for i in range(copy_blocks_num)])
def load_pretrain_and_initialize(self, model_path):
missing, unexpected = self.load_state_dict(torch.load(model_path)["state_dict"], strict=False)
self.initialize_all()
return missing, unexpected
def initialize_all(self):
# freeze all the parameters
for p in self.parameters():
p.requires_grad_(False)
# self.eval()
for i, block in enumerate(self.controlnet):
block.initialize_all_and_copy_from_base(self.blocks[i])
def forward_controlnet(self, control_signal):
if self.use_pe:
control_signal = self.x_embedder(control_signal) + self.pos_embed_ms
else:
control_signal = self.x_embedder(control_signal)
return control_signal
def forward(self, x, timestep, control_signal, y, mask=None, data_info=None, **kwargs):
"""
Forward pass of Sana.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N, 1, 120, C) tensor of class labels
"""
bs = x.shape[0]
x = x.to(self.dtype)
timestep = timestep.to(self.dtype)
y = y.to(self.dtype)
self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
if self.use_pe:
x = self.x_embedder(x)
if self.pos_embed_ms is None or self.pos_embed_ms.shape[1:] != x.shape[1:]:
self.pos_embed_ms = (
torch.from_numpy(
get_2d_sincos_pos_embed(
self.pos_embed.shape[-1],
(self.h, self.w),
pe_interpolation=self.pe_interpolation,
base_size=self.base_size,
)
)
.unsqueeze(0)
.to(x.device)
.to(self.dtype)
)
x += self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2
else:
x = self.x_embedder(x)
# control signal branch
control_signal = control_signal.to(self.dtype)
control_signal = self.forward_controlnet(control_signal)
t = self.t_embedder(timestep) # (N, D)
t0 = self.t_block(t)
y = self.y_embedder(y, self.training, mask=mask) # (N, D)
if self.y_norm:
y = self.attention_y_norm(y)
if mask is not None:
if mask.shape[0] != y.shape[0]:
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
mask = mask.squeeze(1).squeeze(1)
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
else:
y_lens = [y.shape[2]] * y.shape[0]
y = y.squeeze(1).view(1, -1, x.shape[-1])
x = auto_grad_checkpoint(self.blocks[0], x, y, t0, y_lens, (self.h, self.w), **kwargs)
for i in range(1, self.copy_blocks_num + 1):
control_signal, control_signal_skip = auto_grad_checkpoint(
self.controlnet[i - 1], x, y, t0, control_signal, y_lens, (self.h, self.w), **kwargs
)
x = auto_grad_checkpoint(self.blocks[i], x + control_signal_skip, y, t0, y_lens, (self.h, self.w), **kwargs)
for i in range(self.copy_blocks_num + 1, len(self.blocks)):
x = auto_grad_checkpoint(self.blocks[i], x, y, t0, y_lens, (self.h, self.w), **kwargs)
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
return x
def __call__(self, *args, **kwargs):
"""
This method allows the object to be called like a function.
It simply calls the forward method.
"""
return self.forward(*args, **kwargs)
def forward_with_dpmsolver(self, x, timestep, y, data_info, **kwargs):
"""
dpm solver donnot need variance prediction
"""
control_signal = kwargs.pop("control_signal", None)
assert control_signal is not None, "control_signal is required for dpm solver"
assert control_signal.dim() == 4, "control_signal should be a 4D tensor"
if x.shape[0] != control_signal.shape[0]:
control_signal = control_signal.repeat(x.shape[0] // control_signal.shape[0], 1, 1, 1)
assert control_signal.shape[0] == x.shape[0], "control_signal and x should have the same batch size"
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
model_out = self.forward(x, timestep, control_signal, y, data_info=data_info, **kwargs)
return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out
#################################################################################
# Sana Multi-scale Configs #
#################################################################################
@MODELS.register_module()
def SanaMSControlNet_600M_P1_D28(**kwargs):
return SanaMSControlNet(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)
@MODELS.register_module()
def SanaMSControlNet_1600M_P1_D20(**kwargs):
return SanaMSControlNet(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
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