Spaces:
Running
on
L40S
Running
on
L40S
File size: 5,448 Bytes
4450790 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
#original code from https://github.com/genmoai/models under apache 2.0 license
#adapted to ComfyUI
import collections.abc
import math
from itertools import repeat
from typing import Callable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import comfy.ldm.common_dit
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
class TimestepEmbedder(nn.Module):
def __init__(
self,
hidden_size: int,
frequency_embedding_size: int = 256,
*,
bias: bool = True,
timestep_scale: Optional[float] = None,
dtype=None,
device=None,
operations=None,
):
super().__init__()
self.mlp = nn.Sequential(
operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device),
nn.SiLU(),
operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
self.timestep_scale = timestep_scale
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
half = dim // 2
freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
freqs.mul_(-math.log(max_period) / half).exp_()
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
)
return embedding
def forward(self, t, out_dtype):
if self.timestep_scale is not None:
t = t * self.timestep_scale
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype)
t_emb = self.mlp(t_freq)
return t_emb
class FeedForward(nn.Module):
def __init__(
self,
in_features: int,
hidden_size: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
device: Optional[torch.device] = None,
dtype=None,
operations=None,
):
super().__init__()
# keep parameter count and computation constant compared to standard FFN
hidden_size = int(2 * hidden_size / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_size = int(ffn_dim_multiplier * hidden_size)
hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of)
self.hidden_dim = hidden_size
self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype)
self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype)
def forward(self, x):
x, gate = self.w1(x).chunk(2, dim=-1)
x = self.w2(F.silu(x) * gate)
return x
class PatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
bias: bool = True,
dynamic_img_pad: bool = False,
dtype=None,
device=None,
operations=None,
):
super().__init__()
self.patch_size = to_2tuple(patch_size)
self.flatten = flatten
self.dynamic_img_pad = dynamic_img_pad
self.proj = operations.Conv2d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=patch_size,
bias=bias,
device=device,
dtype=dtype,
)
assert norm_layer is None
self.norm = (
norm_layer(embed_dim, device=device) if norm_layer else nn.Identity()
)
def forward(self, x):
B, _C, T, H, W = x.shape
if not self.dynamic_img_pad:
assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
else:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T)
x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular')
x = self.proj(x)
# Flatten temporal and spatial dimensions.
if not self.flatten:
raise NotImplementedError("Must flatten output.")
x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T)
x = self.norm(x)
return x
class RMSNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
super().__init__()
self.eps = eps
self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype))
self.register_parameter("bias", None)
def forward(self, x):
return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
|