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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import torch
import torch.nn as nn
from tqdm import tqdm
from timm.models.layers import DropPath
from timm.models.vision_transformer import Mlp
from .utils import auto_grad_checkpoint, to_2tuple
from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
from .PixArt import PixArt, get_2d_sincos_pos_embed
class PatchEmbed(nn.Module):
"""
2D Image to Patch Embedding
"""
def __init__(
self,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
bias=True,
):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.flatten = flatten
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class PixArtMSBlock(nn.Module):
"""
A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = AttentionKVCompress(
hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
qk_norm=qk_norm, **block_kwargs
)
self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
# to be compatible with lower version pytorch
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
def forward(self, x, y, t, mask=None, HW=None, **kwargs):
B, N, C = x.shape
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
return x
### Core PixArt Model ###
class PixArtMS(PixArt):
"""
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.,
caption_channels=4096,
pe_interpolation=None,
pe_precision=None,
config=None,
model_max_length=120,
micro_condition=True,
qk_norm=False,
kv_compress_config=None,
**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,
pe_interpolation=pe_interpolation,
config=config,
model_max_length=model_max_length,
qk_norm=qk_norm,
kv_compress_config=kv_compress_config,
**kwargs,
)
self.dtype = torch.get_default_dtype()
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.x_embedder = PatchEmbed(patch_size, in_channels, hidden_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)
self.micro_conditioning = micro_condition
if self.micro_conditioning:
self.csize_embedder = SizeEmbedder(hidden_size//3) # c_size embed
self.ar_embedder = SizeEmbedder(hidden_size//3) # aspect ratio embed
drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
if kv_compress_config is None:
kv_compress_config = {
'sampling': None,
'scale_factor': 1,
'kv_compress_layer': [],
}
self.blocks = nn.ModuleList([
PixArtMSBlock(
hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
input_size=(input_size // patch_size, input_size // patch_size),
sampling=kv_compress_config['sampling'],
sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
qk_norm=qk_norm,
)
for i in range(depth)
])
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
"""
Original forward pass of PixArt.
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 = t.to(self.dtype)
y = y.to(self.dtype)
pe_interpolation = self.pe_interpolation
if pe_interpolation is None or self.pe_precision is not None:
# calculate pe_interpolation on-the-fly
pe_interpolation = round((x.shape[-1]+x.shape[-2])/2.0 / (512/8.0), self.pe_precision or 0)
self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
pos_embed = torch.from_numpy(
get_2d_sincos_pos_embed(
self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation,
base_size=self.base_size
)
).unsqueeze(0).to(device=x.device, dtype=self.dtype)
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
t = self.t_embedder(timestep) # (N, D)
if self.micro_conditioning:
c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
csize = self.csize_embedder(c_size, bs) # (N, D)
ar = self.ar_embedder(ar, bs) # (N, D)
t = t + torch.cat([csize, ar], dim=1)
t0 = self.t_block(t)
y = self.y_embedder(y, self.training) # (N, D)
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])
for block in self.blocks:
x = auto_grad_checkpoint(block, x, y, t0, y_lens, (self.h, self.w), **kwargs) # (N, T, D) #support grad checkpoint
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 forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
"""
Forward pass that adapts comfy input to original forward function
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
timesteps: (N,) tensor of diffusion timesteps
context: (N, 1, 120, C) conditioning
img_hw: height|width conditioning
aspect_ratio: aspect ratio conditioning
"""
## size/ar from cond with fallback based on the latent image shape.
bs = x.shape[0]
data_info = {}
if img_hw is None:
data_info["img_hw"] = torch.tensor(
[[x.shape[2]*8, x.shape[3]*8]],
dtype=self.dtype,
device=x.device
).repeat(bs, 1)
else:
data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device)
if aspect_ratio is None or True:
data_info["aspect_ratio"] = torch.tensor(
[[x.shape[2]/x.shape[3]]],
dtype=self.dtype,
device=x.device
).repeat(bs, 1)
else:
data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device)
## Still accepts the input w/o that dim but returns garbage
if len(context.shape) == 3:
context = context.unsqueeze(1)
## run original forward pass
out = self.forward_raw(
x = x.to(self.dtype),
t = timesteps.to(self.dtype),
y = context.to(self.dtype),
data_info=data_info,
)
## only return EPS
out = out.to(torch.float)
eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
return eps
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
assert self.h * self.w == x.shape[1]
x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
return imgs
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