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import math
from functools import partial
from torch import nn
import torch
class AuraFlowPatchEmbed(nn.Module):
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
pos_embed_max_size=None,
):
super().__init__()
self.num_patches = (height // patch_size) * (width // patch_size)
self.pos_embed_max_size = pos_embed_max_size
self.proj = nn.Linear(patch_size * patch_size * in_channels, embed_dim)
self.pos_embed = nn.Parameter(torch.randn(1, pos_embed_max_size, embed_dim) * 0.1)
self.patch_size = patch_size
self.height, self.width = height // patch_size, width // patch_size
self.base_size = height // patch_size
def forward(self, latent):
batch_size, num_channels, height, width = latent.size()
latent = latent.view(
batch_size,
num_channels,
height // self.patch_size,
self.patch_size,
width // self.patch_size,
self.patch_size,
)
latent = latent.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
latent = self.proj(latent)
try:
return latent + self.pos_embed
except RuntimeError:
raise RuntimeError(
f"Positional embeddings are too small for the number of patches. "
f"Please increase `pos_embed_max_size` to at least {self.num_patches}."
)
# comfy
# def apply_pos_embeds(self, x, h, w):
# h = (h + 1) // self.patch_size
# w = (w + 1) // self.patch_size
# max_dim = max(h, w)
#
# cur_dim = self.h_max
# pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
#
# if max_dim > cur_dim:
# pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1,
# -1)
# cur_dim = max_dim
#
# from_h = (cur_dim - h) // 2
# from_w = (cur_dim - w) // 2
# pos_encoding = pos_encoding[:, from_h:from_h + h, from_w:from_w + w]
# return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
# def patchify(self, x):
# B, C, H, W = x.size()
# pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
# pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
#
# x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
# x = x.view(
# B,
# C,
# (H + 1) // self.patch_size,
# self.patch_size,
# (W + 1) // self.patch_size,
# self.patch_size,
# )
# x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
# return x
def patch_auraflow_pos_embed(pos_embed):
# we need to hijack the forward and replace with a custom one. Self is the model
def new_forward(self, latent):
batch_size, num_channels, height, width = latent.size()
# add padding to the latent to make it match pos_embed
latent_size = height * width * num_channels / 16 # todo check where 16 comes from?
pos_embed_size = self.pos_embed.shape[1]
if latent_size < pos_embed_size:
total_padding = int(pos_embed_size - math.floor(latent_size))
total_padding = total_padding // 16
pad_height = total_padding // 2
pad_width = total_padding - pad_height
# mirror padding on the right side
padding = (0, pad_width, 0, pad_height)
latent = torch.nn.functional.pad(latent, padding, mode='reflect')
elif latent_size > pos_embed_size:
amount_to_remove = latent_size - pos_embed_size
latent = latent[:, :, :-amount_to_remove]
batch_size, num_channels, height, width = latent.size()
latent = latent.view(
batch_size,
num_channels,
height // self.patch_size,
self.patch_size,
width // self.patch_size,
self.patch_size,
)
latent = latent.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
latent = self.proj(latent)
try:
return latent + self.pos_embed
except RuntimeError:
raise RuntimeError(
f"Positional embeddings are too small for the number of patches. "
f"Please increase `pos_embed_max_size` to at least {self.num_patches}."
)
pos_embed.forward = partial(new_forward, pos_embed)
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