# Based on:
# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
import torch
import torch.nn as nn
from .blocks import (
t2i_modulate,
CaptionEmbedder,
AttentionKVCompress,
MultiHeadCrossAttention,
T2IFinalLayer,
SizeEmbedder,
)
from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
grid_h, grid_w = torch.meshgrid(
torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
indexing='ij'
)
emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D)
return emb
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, dtype=None, device=None, operations=None, **block_kwargs):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.attn = AttentionKVCompress(
hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
)
self.cross_attn = MultiHeadCrossAttention(
hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
)
self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
# 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,
dtype=dtype, device=device, operations=operations
)
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].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
x = x + (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 + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
return x
### Core PixArt Model ###
class PixArtMS(nn.Module):
"""
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,
dtype=None,
device=None,
operations=None,
**kwargs,
):
nn.Module.__init__(self)
self.dtype = dtype
self.pred_sigma = pred_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if pred_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.pe_interpolation = pe_interpolation
self.pe_precision = pe_precision
self.hidden_size = hidden_size
self.depth = depth
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(
nn.SiLU(),
operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
)
self.x_embedder = PatchEmbed(
patch_size=patch_size,
in_chans=in_channels,
embed_dim=hidden_size,
bias=True,
dtype=dtype,
device=device,
operations=operations
)
self.t_embedder = TimestepEmbedder(
hidden_size, dtype=dtype, device=device, operations=operations,
)
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,
dtype=dtype, device=device, operations=operations,
)
self.micro_conditioning = micro_condition
if self.micro_conditioning:
self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
# For fixed sin-cos embedding:
# num_patches = (input_size // patch_size) * (input_size // patch_size)
# self.base_size = input_size // self.patch_size
# self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
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],
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,
dtype=dtype,
device=device,
operations=operations,
)
for i in range(depth)
])
self.final_layer = T2IFinalLayer(
hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
)
def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=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) conditioning
ar: (N, 1): aspect ratio
cs: (N ,2) size conditioning for height/width
"""
B, C, H, W = x.shape
c_res = (H + W) // 2
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(c_res / (512/8.0), self.pe_precision or 0)
pos_embed = get_2d_sincos_pos_embed_torch(
self.hidden_size,
h=(H // self.patch_size),
w=(W // self.patch_size),
pe_interpolation=pe_interpolation,
base_size=((round(c_res / 64) * 64) // self.patch_size),
device=x.device,
dtype=x.dtype,
).unsqueeze(0)
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
t = self.t_embedder(timestep, x.dtype) # (N, D)
if self.micro_conditioning and (c_size is not None and c_ar is not None):
bs = x.shape[0]
c_size = self.csize_embedder(c_size, bs) # (N, D)
c_ar = self.ar_embedder(c_ar, bs) # (N, D)
t = t + torch.cat([c_size, c_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 = None
y = y.squeeze(1).view(1, -1, x.shape[-1])
for block in self.blocks:
x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D)
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x, H, W) # (N, out_channels, H, W)
return x
def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
B, C, H, W = x.shape
# Fallback for missing microconds
if self.micro_conditioning:
if c_size is None:
c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
if c_ar is None:
c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
## 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_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
## only return EPS
if self.pred_sigma:
return out[:, :self.in_channels]
return out
def unpatchify(self, x, h, w):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = h // self.patch_size
w = w // self.patch_size
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
return imgs