mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
another way to use backend (#638)
* another way to use backend * another way to use backend
This commit is contained in:
lllyasviel
@@ -0,0 +1,737 @@
|
||||
# pytorch_diffusion + derived encoder decoder
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import rearrange
|
||||
from typing import Optional, Any
|
||||
|
||||
from ..attention import MemoryEfficientCrossAttention
|
||||
from comfy import model_management
|
||||
import comfy.ops
|
||||
|
||||
if model_management.xformers_enabled_vae():
|
||||
import xformers
|
||||
import xformers.ops
|
||||
|
||||
def get_timestep_embedding(timesteps, embedding_dim):
|
||||
"""
|
||||
This matches the implementation in Denoising Diffusion Probabilistic Models:
|
||||
From Fairseq.
|
||||
Build sinusoidal embeddings.
|
||||
This matches the implementation in tensor2tensor, but differs slightly
|
||||
from the description in Section 3.5 of "Attention Is All You Need".
|
||||
"""
|
||||
assert len(timesteps.shape) == 1
|
||||
|
||||
half_dim = embedding_dim // 2
|
||||
emb = math.log(10000) / (half_dim - 1)
|
||||
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
|
||||
emb = emb.to(device=timesteps.device)
|
||||
emb = timesteps.float()[:, None] * emb[None, :]
|
||||
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
||||
if embedding_dim % 2 == 1: # zero pad
|
||||
emb = torch.nn.functional.pad(emb, (0,1,0,0))
|
||||
return emb
|
||||
|
||||
|
||||
def nonlinearity(x):
|
||||
# swish
|
||||
return x*torch.sigmoid(x)
|
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32):
|
||||
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
self.conv = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
try:
|
||||
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
except: #operation not implemented for bf16
|
||||
b, c, h, w = x.shape
|
||||
out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device)
|
||||
split = 8
|
||||
l = out.shape[1] // split
|
||||
for i in range(0, out.shape[1], l):
|
||||
out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype)
|
||||
del x
|
||||
x = out
|
||||
|
||||
if self.with_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
# no asymmetric padding in torch conv, must do it ourselves
|
||||
self.conv = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x):
|
||||
if self.with_conv:
|
||||
pad = (0,1,0,1)
|
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
||||
x = self.conv(x)
|
||||
else:
|
||||
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
|
||||
return x
|
||||
|
||||
|
||||
class ResnetBlock(nn.Module):
|
||||
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
|
||||
dropout, temb_channels=512):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
out_channels = in_channels if out_channels is None else out_channels
|
||||
self.out_channels = out_channels
|
||||
self.use_conv_shortcut = conv_shortcut
|
||||
|
||||
self.swish = torch.nn.SiLU(inplace=True)
|
||||
self.norm1 = Normalize(in_channels)
|
||||
self.conv1 = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if temb_channels > 0:
|
||||
self.temb_proj = comfy.ops.Linear(temb_channels,
|
||||
out_channels)
|
||||
self.norm2 = Normalize(out_channels)
|
||||
self.dropout = torch.nn.Dropout(dropout, inplace=True)
|
||||
self.conv2 = comfy.ops.Conv2d(out_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if self.in_channels != self.out_channels:
|
||||
if self.use_conv_shortcut:
|
||||
self.conv_shortcut = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
else:
|
||||
self.nin_shortcut = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x, temb):
|
||||
h = x
|
||||
h = self.norm1(h)
|
||||
h = self.swish(h)
|
||||
h = self.conv1(h)
|
||||
|
||||
if temb is not None:
|
||||
h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
|
||||
|
||||
h = self.norm2(h)
|
||||
h = self.swish(h)
|
||||
h = self.dropout(h)
|
||||
h = self.conv2(h)
|
||||
|
||||
if self.in_channels != self.out_channels:
|
||||
if self.use_conv_shortcut:
|
||||
x = self.conv_shortcut(x)
|
||||
else:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return x+h
|
||||
|
||||
def slice_attention(q, k, v):
|
||||
r1 = torch.zeros_like(k, device=q.device)
|
||||
scale = (int(q.shape[-1])**(-0.5))
|
||||
|
||||
mem_free_total = model_management.get_free_memory(q.device)
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
|
||||
while True:
|
||||
try:
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = torch.bmm(q[:, i:end], k) * scale
|
||||
|
||||
s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
|
||||
del s1
|
||||
|
||||
r1[:, :, i:end] = torch.bmm(v, s2)
|
||||
del s2
|
||||
break
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
model_management.soft_empty_cache(True)
|
||||
steps *= 2
|
||||
if steps > 128:
|
||||
raise e
|
||||
print("out of memory error, increasing steps and trying again", steps)
|
||||
|
||||
return r1
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b,c,h,w = q.shape
|
||||
|
||||
q = q.reshape(b,c,h*w)
|
||||
q = q.permute(0,2,1) # b,hw,c
|
||||
k = k.reshape(b,c,h*w) # b,c,hw
|
||||
v = v.reshape(b,c,h*w)
|
||||
|
||||
r1 = slice_attention(q, k, v)
|
||||
h_ = r1.reshape(b,c,h,w)
|
||||
del r1
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
|
||||
class MemoryEfficientAttnBlock(nn.Module):
|
||||
"""
|
||||
Uses xformers efficient implementation,
|
||||
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
||||
Note: this is a single-head self-attention operation
|
||||
"""
|
||||
#
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
B, C, H, W = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
try:
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
||||
out = out.transpose(1, 2).reshape(B, C, H, W)
|
||||
except NotImplementedError as e:
|
||||
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
|
||||
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
||||
class MemoryEfficientAttnBlockPytorch(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
B, C, H, W = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
try:
|
||||
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
||||
out = out.transpose(2, 3).reshape(B, C, H, W)
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
print("scaled_dot_product_attention OOMed: switched to slice attention")
|
||||
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
|
||||
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
||||
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
|
||||
def forward(self, x, context=None, mask=None):
|
||||
b, c, h, w = x.shape
|
||||
x = rearrange(x, 'b c h w -> b (h w) c')
|
||||
out = super().forward(x, context=context, mask=mask)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
|
||||
return x + out
|
||||
|
||||
|
||||
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
||||
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
|
||||
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
|
||||
attn_type = "vanilla-xformers"
|
||||
if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
|
||||
attn_type = "vanilla-pytorch"
|
||||
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
|
||||
if attn_type == "vanilla":
|
||||
assert attn_kwargs is None
|
||||
return AttnBlock(in_channels)
|
||||
elif attn_type == "vanilla-xformers":
|
||||
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
|
||||
return MemoryEfficientAttnBlock(in_channels)
|
||||
elif attn_type == "vanilla-pytorch":
|
||||
return MemoryEfficientAttnBlockPytorch(in_channels)
|
||||
elif type == "memory-efficient-cross-attn":
|
||||
attn_kwargs["query_dim"] = in_channels
|
||||
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
|
||||
elif attn_type == "none":
|
||||
return nn.Identity(in_channels)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
||||
resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
|
||||
super().__init__()
|
||||
if use_linear_attn: attn_type = "linear"
|
||||
self.ch = ch
|
||||
self.temb_ch = self.ch*4
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.use_timestep = use_timestep
|
||||
if self.use_timestep:
|
||||
# timestep embedding
|
||||
self.temb = nn.Module()
|
||||
self.temb.dense = nn.ModuleList([
|
||||
comfy.ops.Linear(self.ch,
|
||||
self.temb_ch),
|
||||
comfy.ops.Linear(self.temb_ch,
|
||||
self.temb_ch),
|
||||
])
|
||||
|
||||
# downsampling
|
||||
self.conv_in = comfy.ops.Conv2d(in_channels,
|
||||
self.ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
curr_res = resolution
|
||||
in_ch_mult = (1,)+tuple(ch_mult)
|
||||
self.down = nn.ModuleList()
|
||||
for i_level in range(self.num_resolutions):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_in = ch*in_ch_mult[i_level]
|
||||
block_out = ch*ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks):
|
||||
block.append(ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_out,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout))
|
||||
block_in = block_out
|
||||
if curr_res in attn_resolutions:
|
||||
attn.append(make_attn(block_in, attn_type=attn_type))
|
||||
down = nn.Module()
|
||||
down.block = block
|
||||
down.attn = attn
|
||||
if i_level != self.num_resolutions-1:
|
||||
down.downsample = Downsample(block_in, resamp_with_conv)
|
||||
curr_res = curr_res // 2
|
||||
self.down.append(down)
|
||||
|
||||
# middle
|
||||
self.mid = nn.Module()
|
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
|
||||
# upsampling
|
||||
self.up = nn.ModuleList()
|
||||
for i_level in reversed(range(self.num_resolutions)):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_out = ch*ch_mult[i_level]
|
||||
skip_in = ch*ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
if i_block == self.num_res_blocks:
|
||||
skip_in = ch*in_ch_mult[i_level]
|
||||
block.append(ResnetBlock(in_channels=block_in+skip_in,
|
||||
out_channels=block_out,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout))
|
||||
block_in = block_out
|
||||
if curr_res in attn_resolutions:
|
||||
attn.append(make_attn(block_in, attn_type=attn_type))
|
||||
up = nn.Module()
|
||||
up.block = block
|
||||
up.attn = attn
|
||||
if i_level != 0:
|
||||
up.upsample = Upsample(block_in, resamp_with_conv)
|
||||
curr_res = curr_res * 2
|
||||
self.up.insert(0, up) # prepend to get consistent order
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x, t=None, context=None):
|
||||
#assert x.shape[2] == x.shape[3] == self.resolution
|
||||
if context is not None:
|
||||
# assume aligned context, cat along channel axis
|
||||
x = torch.cat((x, context), dim=1)
|
||||
if self.use_timestep:
|
||||
# timestep embedding
|
||||
assert t is not None
|
||||
temb = get_timestep_embedding(t, self.ch)
|
||||
temb = self.temb.dense[0](temb)
|
||||
temb = nonlinearity(temb)
|
||||
temb = self.temb.dense[1](temb)
|
||||
else:
|
||||
temb = None
|
||||
|
||||
# downsampling
|
||||
hs = [self.conv_in(x)]
|
||||
for i_level in range(self.num_resolutions):
|
||||
for i_block in range(self.num_res_blocks):
|
||||
h = self.down[i_level].block[i_block](hs[-1], temb)
|
||||
if len(self.down[i_level].attn) > 0:
|
||||
h = self.down[i_level].attn[i_block](h)
|
||||
hs.append(h)
|
||||
if i_level != self.num_resolutions-1:
|
||||
hs.append(self.down[i_level].downsample(hs[-1]))
|
||||
|
||||
# middle
|
||||
h = hs[-1]
|
||||
h = self.mid.block_1(h, temb)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h, temb)
|
||||
|
||||
# upsampling
|
||||
for i_level in reversed(range(self.num_resolutions)):
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
h = self.up[i_level].block[i_block](
|
||||
torch.cat([h, hs.pop()], dim=1), temb)
|
||||
if len(self.up[i_level].attn) > 0:
|
||||
h = self.up[i_level].attn[i_block](h)
|
||||
if i_level != 0:
|
||||
h = self.up[i_level].upsample(h)
|
||||
|
||||
# end
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
def get_last_layer(self):
|
||||
return self.conv_out.weight
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
||||
resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
|
||||
**ignore_kwargs):
|
||||
super().__init__()
|
||||
if use_linear_attn: attn_type = "linear"
|
||||
self.ch = ch
|
||||
self.temb_ch = 0
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.in_channels = in_channels
|
||||
|
||||
# downsampling
|
||||
self.conv_in = comfy.ops.Conv2d(in_channels,
|
||||
self.ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
curr_res = resolution
|
||||
in_ch_mult = (1,)+tuple(ch_mult)
|
||||
self.in_ch_mult = in_ch_mult
|
||||
self.down = nn.ModuleList()
|
||||
for i_level in range(self.num_resolutions):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_in = ch*in_ch_mult[i_level]
|
||||
block_out = ch*ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks):
|
||||
block.append(ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_out,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout))
|
||||
block_in = block_out
|
||||
if curr_res in attn_resolutions:
|
||||
attn.append(make_attn(block_in, attn_type=attn_type))
|
||||
down = nn.Module()
|
||||
down.block = block
|
||||
down.attn = attn
|
||||
if i_level != self.num_resolutions-1:
|
||||
down.downsample = Downsample(block_in, resamp_with_conv)
|
||||
curr_res = curr_res // 2
|
||||
self.down.append(down)
|
||||
|
||||
# middle
|
||||
self.mid = nn.Module()
|
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
2*z_channels if double_z else z_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
# timestep embedding
|
||||
temb = None
|
||||
# downsampling
|
||||
h = self.conv_in(x)
|
||||
for i_level in range(self.num_resolutions):
|
||||
for i_block in range(self.num_res_blocks):
|
||||
h = self.down[i_level].block[i_block](h, temb)
|
||||
if len(self.down[i_level].attn) > 0:
|
||||
h = self.down[i_level].attn[i_block](h)
|
||||
if i_level != self.num_resolutions-1:
|
||||
h = self.down[i_level].downsample(h)
|
||||
|
||||
# middle
|
||||
h = self.mid.block_1(h, temb)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h, temb)
|
||||
|
||||
# end
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
||||
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
|
||||
attn_type="vanilla", **ignorekwargs):
|
||||
super().__init__()
|
||||
if use_linear_attn: attn_type = "linear"
|
||||
self.ch = ch
|
||||
self.temb_ch = 0
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.in_channels = in_channels
|
||||
self.give_pre_end = give_pre_end
|
||||
self.tanh_out = tanh_out
|
||||
|
||||
# compute in_ch_mult, block_in and curr_res at lowest res
|
||||
in_ch_mult = (1,)+tuple(ch_mult)
|
||||
block_in = ch*ch_mult[self.num_resolutions-1]
|
||||
curr_res = resolution // 2**(self.num_resolutions-1)
|
||||
self.z_shape = (1,z_channels,curr_res,curr_res)
|
||||
print("Working with z of shape {} = {} dimensions.".format(
|
||||
self.z_shape, np.prod(self.z_shape)))
|
||||
|
||||
# z to block_in
|
||||
self.conv_in = comfy.ops.Conv2d(z_channels,
|
||||
block_in,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
# middle
|
||||
self.mid = nn.Module()
|
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_in,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout)
|
||||
|
||||
# upsampling
|
||||
self.up = nn.ModuleList()
|
||||
for i_level in reversed(range(self.num_resolutions)):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_out = ch*ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
block.append(ResnetBlock(in_channels=block_in,
|
||||
out_channels=block_out,
|
||||
temb_channels=self.temb_ch,
|
||||
dropout=dropout))
|
||||
block_in = block_out
|
||||
if curr_res in attn_resolutions:
|
||||
attn.append(make_attn(block_in, attn_type=attn_type))
|
||||
up = nn.Module()
|
||||
up.block = block
|
||||
up.attn = attn
|
||||
if i_level != 0:
|
||||
up.upsample = Upsample(block_in, resamp_with_conv)
|
||||
curr_res = curr_res * 2
|
||||
self.up.insert(0, up) # prepend to get consistent order
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, z):
|
||||
#assert z.shape[1:] == self.z_shape[1:]
|
||||
self.last_z_shape = z.shape
|
||||
|
||||
# timestep embedding
|
||||
temb = None
|
||||
|
||||
# z to block_in
|
||||
h = self.conv_in(z)
|
||||
|
||||
# middle
|
||||
h = self.mid.block_1(h, temb)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h, temb)
|
||||
|
||||
# upsampling
|
||||
for i_level in reversed(range(self.num_resolutions)):
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
h = self.up[i_level].block[i_block](h, temb)
|
||||
if len(self.up[i_level].attn) > 0:
|
||||
h = self.up[i_level].attn[i_block](h)
|
||||
if i_level != 0:
|
||||
h = self.up[i_level].upsample(h)
|
||||
|
||||
# end
|
||||
if self.give_pre_end:
|
||||
return h
|
||||
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h)
|
||||
if self.tanh_out:
|
||||
h = torch.tanh(h)
|
||||
return h
|
||||
@@ -0,0 +1,664 @@
|
||||
from abc import abstractmethod
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch as th
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .util import (
|
||||
checkpoint,
|
||||
avg_pool_nd,
|
||||
zero_module,
|
||||
normalization,
|
||||
timestep_embedding,
|
||||
)
|
||||
from ..attention import SpatialTransformer
|
||||
from comfy.ldm.util import exists
|
||||
import comfy.ops
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
Any module where forward() takes timestep embeddings as a second argument.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the module to `x` given `emb` timestep embeddings.
|
||||
"""
|
||||
|
||||
|
||||
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
|
||||
"""
|
||||
A sequential module that passes timestep embeddings to the children that
|
||||
support it as an extra input.
|
||||
"""
|
||||
|
||||
def forward(self, x, emb, context=None, transformer_options={}, output_shape=None):
|
||||
for layer in self:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, emb)
|
||||
elif isinstance(layer, SpatialTransformer):
|
||||
x = layer(x, context, transformer_options)
|
||||
elif isinstance(layer, Upsample):
|
||||
x = layer(x, output_shape=output_shape)
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
#This is needed because accelerate makes a copy of transformer_options which breaks "current_index"
|
||||
def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
|
||||
for layer in ts:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, emb)
|
||||
elif isinstance(layer, SpatialTransformer):
|
||||
x = layer(x, context, transformer_options)
|
||||
transformer_options["current_index"] += 1
|
||||
elif isinstance(layer, Upsample):
|
||||
x = layer(x, output_shape=output_shape)
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
class Upsample(nn.Module):
|
||||
"""
|
||||
An upsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
upsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
if use_conv:
|
||||
self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, output_shape=None):
|
||||
assert x.shape[1] == self.channels
|
||||
if self.dims == 3:
|
||||
shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
|
||||
if output_shape is not None:
|
||||
shape[1] = output_shape[3]
|
||||
shape[2] = output_shape[4]
|
||||
else:
|
||||
shape = [x.shape[2] * 2, x.shape[3] * 2]
|
||||
if output_shape is not None:
|
||||
shape[0] = output_shape[2]
|
||||
shape[1] = output_shape[3]
|
||||
|
||||
x = F.interpolate(x, size=shape, mode="nearest")
|
||||
if self.use_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
class Downsample(nn.Module):
|
||||
"""
|
||||
A downsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
downsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
if use_conv:
|
||||
self.op = operations.conv_nd(
|
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
assert self.channels == self.out_channels
|
||||
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
"""
|
||||
A residual block that can optionally change the number of channels.
|
||||
:param channels: the number of input channels.
|
||||
:param emb_channels: the number of timestep embedding channels.
|
||||
:param dropout: the rate of dropout.
|
||||
:param out_channels: if specified, the number of out channels.
|
||||
:param use_conv: if True and out_channels is specified, use a spatial
|
||||
convolution instead of a smaller 1x1 convolution to change the
|
||||
channels in the skip connection.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param use_checkpoint: if True, use gradient checkpointing on this module.
|
||||
:param up: if True, use this block for upsampling.
|
||||
:param down: if True, use this block for downsampling.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
use_conv=False,
|
||||
use_scale_shift_norm=False,
|
||||
dims=2,
|
||||
use_checkpoint=False,
|
||||
up=False,
|
||||
down=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=comfy.ops
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.use_scale_shift_norm = use_scale_shift_norm
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
self.updown = up or down
|
||||
|
||||
if up:
|
||||
self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
elif down:
|
||||
self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
else:
|
||||
self.h_upd = self.x_upd = nn.Identity()
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
operations.Linear(
|
||||
emb_channels,
|
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
|
||||
),
|
||||
)
|
||||
self.out_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
nn.Dropout(p=dropout),
|
||||
zero_module(
|
||||
operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
),
|
||||
)
|
||||
|
||||
if self.out_channels == channels:
|
||||
self.skip_connection = nn.Identity()
|
||||
elif use_conv:
|
||||
self.skip_connection = operations.conv_nd(
|
||||
dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the block to a Tensor, conditioned on a timestep embedding.
|
||||
:param x: an [N x C x ...] Tensor of features.
|
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
return checkpoint(
|
||||
self._forward, (x, emb), self.parameters(), self.use_checkpoint
|
||||
)
|
||||
|
||||
|
||||
def _forward(self, x, emb):
|
||||
if self.updown:
|
||||
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
||||
h = in_rest(x)
|
||||
h = self.h_upd(h)
|
||||
x = self.x_upd(x)
|
||||
h = in_conv(h)
|
||||
else:
|
||||
h = self.in_layers(x)
|
||||
emb_out = self.emb_layers(emb).type(h.dtype)
|
||||
while len(emb_out.shape) < len(h.shape):
|
||||
emb_out = emb_out[..., None]
|
||||
if self.use_scale_shift_norm:
|
||||
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
|
||||
scale, shift = th.chunk(emb_out, 2, dim=1)
|
||||
h = out_norm(h) * (1 + scale) + shift
|
||||
h = out_rest(h)
|
||||
else:
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
class Timestep(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
|
||||
def forward(self, t):
|
||||
return timestep_embedding(t, self.dim)
|
||||
|
||||
|
||||
class UNetModel(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_classes: if specified (as an int), then this model will be
|
||||
class-conditional with `num_classes` classes.
|
||||
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_size,
|
||||
in_channels,
|
||||
model_channels,
|
||||
out_channels,
|
||||
num_res_blocks,
|
||||
attention_resolutions,
|
||||
dropout=0,
|
||||
channel_mult=(1, 2, 4, 8),
|
||||
conv_resample=True,
|
||||
dims=2,
|
||||
num_classes=None,
|
||||
use_checkpoint=False,
|
||||
use_fp16=False,
|
||||
use_bf16=False,
|
||||
num_heads=-1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
use_scale_shift_norm=False,
|
||||
resblock_updown=False,
|
||||
use_new_attention_order=False,
|
||||
use_spatial_transformer=False, # custom transformer support
|
||||
transformer_depth=1, # custom transformer support
|
||||
context_dim=None, # custom transformer support
|
||||
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
|
||||
legacy=True,
|
||||
disable_self_attentions=None,
|
||||
num_attention_blocks=None,
|
||||
disable_middle_self_attn=False,
|
||||
use_linear_in_transformer=False,
|
||||
adm_in_channels=None,
|
||||
transformer_depth_middle=None,
|
||||
device=None,
|
||||
operations=comfy.ops,
|
||||
):
|
||||
super().__init__()
|
||||
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
|
||||
if use_spatial_transformer:
|
||||
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
|
||||
|
||||
if context_dim is not None:
|
||||
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
|
||||
# from omegaconf.listconfig import ListConfig
|
||||
# if type(context_dim) == ListConfig:
|
||||
# context_dim = list(context_dim)
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
if num_heads == -1:
|
||||
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
|
||||
if num_head_channels == -1:
|
||||
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
|
||||
self.image_size = image_size
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
if isinstance(transformer_depth, int):
|
||||
transformer_depth = len(channel_mult) * [transformer_depth]
|
||||
if transformer_depth_middle is None:
|
||||
transformer_depth_middle = transformer_depth[-1]
|
||||
if isinstance(num_res_blocks, int):
|
||||
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
|
||||
else:
|
||||
if len(num_res_blocks) != len(channel_mult):
|
||||
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
|
||||
"as a list/tuple (per-level) with the same length as channel_mult")
|
||||
self.num_res_blocks = num_res_blocks
|
||||
if disable_self_attentions is not None:
|
||||
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
|
||||
assert len(disable_self_attentions) == len(channel_mult)
|
||||
if num_attention_blocks is not None:
|
||||
assert len(num_attention_blocks) == len(self.num_res_blocks)
|
||||
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
|
||||
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
|
||||
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
|
||||
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
|
||||
f"attention will still not be set.")
|
||||
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.num_classes = num_classes
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.dtype = th.float16 if use_fp16 else th.float32
|
||||
self.dtype = th.bfloat16 if use_bf16 else self.dtype
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.predict_codebook_ids = n_embed is not None
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
|
||||
if self.num_classes is not None:
|
||||
if isinstance(self.num_classes, int):
|
||||
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
||||
elif self.num_classes == "continuous":
|
||||
print("setting up linear c_adm embedding layer")
|
||||
self.label_emb = nn.Linear(1, time_embed_dim)
|
||||
elif self.num_classes == "sequential":
|
||||
assert adm_in_channels is not None
|
||||
self.label_emb = nn.Sequential(
|
||||
nn.Sequential(
|
||||
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise ValueError()
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
)
|
||||
]
|
||||
)
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
for level, mult in enumerate(channel_mult):
|
||||
for nr in range(self.num_res_blocks[level]):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=mult * model_channels,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
if exists(disable_self_attentions):
|
||||
disabled_sa = disable_self_attentions[level]
|
||||
else:
|
||||
disabled_sa = False
|
||||
|
||||
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
|
||||
layers.append(SpatialTransformer(
|
||||
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
|
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
down=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
SpatialTransformer( # always uses a self-attn
|
||||
ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
|
||||
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, mult in list(enumerate(channel_mult))[::-1]:
|
||||
for i in range(self.num_res_blocks[level] + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=model_channels * mult,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
if exists(disable_self_attentions):
|
||||
disabled_sa = disable_self_attentions[level]
|
||||
else:
|
||||
disabled_sa = False
|
||||
|
||||
if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
|
||||
layers.append(
|
||||
SpatialTransformer(
|
||||
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
|
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
if level and i == self.num_res_blocks[level]:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
up=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
|
||||
)
|
||||
if self.predict_codebook_ids:
|
||||
self.id_predictor = nn.Sequential(
|
||||
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
||||
operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
|
||||
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
|
||||
)
|
||||
|
||||
def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param context: conditioning plugged in via crossattn
|
||||
:param y: an [N] Tensor of labels, if class-conditional.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
transformer_options["original_shape"] = list(x.shape)
|
||||
transformer_options["current_index"] = 0
|
||||
transformer_patches = transformer_options.get("patches", {})
|
||||
|
||||
assert (y is not None) == (
|
||||
self.num_classes is not None
|
||||
), "must specify y if and only if the model is class-conditional"
|
||||
hs = []
|
||||
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
|
||||
if self.num_classes is not None:
|
||||
assert y.shape[0] == x.shape[0]
|
||||
emb = emb + self.label_emb(y)
|
||||
|
||||
h = x.type(self.dtype)
|
||||
for id, module in enumerate(self.input_blocks):
|
||||
transformer_options["block"] = ("input", id)
|
||||
h = forward_timestep_embed(module, h, emb, context, transformer_options)
|
||||
if control is not None and 'input' in control and len(control['input']) > 0:
|
||||
ctrl = control['input'].pop()
|
||||
if ctrl is not None:
|
||||
h += ctrl
|
||||
hs.append(h)
|
||||
transformer_options["block"] = ("middle", 0)
|
||||
h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
|
||||
if control is not None and 'middle' in control and len(control['middle']) > 0:
|
||||
ctrl = control['middle'].pop()
|
||||
if ctrl is not None:
|
||||
h += ctrl
|
||||
|
||||
for id, module in enumerate(self.output_blocks):
|
||||
transformer_options["block"] = ("output", id)
|
||||
hsp = hs.pop()
|
||||
if control is not None and 'output' in control and len(control['output']) > 0:
|
||||
ctrl = control['output'].pop()
|
||||
if ctrl is not None:
|
||||
hsp += ctrl
|
||||
|
||||
if "output_block_patch" in transformer_patches:
|
||||
patch = transformer_patches["output_block_patch"]
|
||||
for p in patch:
|
||||
h, hsp = p(h, hsp, transformer_options)
|
||||
|
||||
h = th.cat([h, hsp], dim=1)
|
||||
del hsp
|
||||
if len(hs) > 0:
|
||||
output_shape = hs[-1].shape
|
||||
else:
|
||||
output_shape = None
|
||||
h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
|
||||
h = h.type(x.dtype)
|
||||
if self.predict_codebook_ids:
|
||||
return self.id_predictor(h)
|
||||
else:
|
||||
return self.out(h)
|
||||
@@ -0,0 +1,81 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
|
||||
from .util import extract_into_tensor, make_beta_schedule
|
||||
from comfy.ldm.util import default
|
||||
|
||||
|
||||
class AbstractLowScaleModel(nn.Module):
|
||||
# for concatenating a downsampled image to the latent representation
|
||||
def __init__(self, noise_schedule_config=None):
|
||||
super(AbstractLowScaleModel, self).__init__()
|
||||
if noise_schedule_config is not None:
|
||||
self.register_schedule(**noise_schedule_config)
|
||||
|
||||
def register_schedule(self, beta_schedule="linear", timesteps=1000,
|
||||
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
|
||||
cosine_s=cosine_s)
|
||||
alphas = 1. - betas
|
||||
alphas_cumprod = np.cumprod(alphas, axis=0)
|
||||
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
||||
|
||||
timesteps, = betas.shape
|
||||
self.num_timesteps = int(timesteps)
|
||||
self.linear_start = linear_start
|
||||
self.linear_end = linear_end
|
||||
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
|
||||
|
||||
to_torch = partial(torch.tensor, dtype=torch.float32)
|
||||
|
||||
self.register_buffer('betas', to_torch(betas))
|
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
||||
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
|
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others
|
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
|
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
|
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
|
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
|
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
|
||||
|
||||
def q_sample(self, x_start, t, noise=None):
|
||||
noise = default(noise, lambda: torch.randn_like(x_start))
|
||||
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
|
||||
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
|
||||
|
||||
def forward(self, x):
|
||||
return x, None
|
||||
|
||||
def decode(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class SimpleImageConcat(AbstractLowScaleModel):
|
||||
# no noise level conditioning
|
||||
def __init__(self):
|
||||
super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
|
||||
self.max_noise_level = 0
|
||||
|
||||
def forward(self, x):
|
||||
# fix to constant noise level
|
||||
return x, torch.zeros(x.shape[0], device=x.device).long()
|
||||
|
||||
|
||||
class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
|
||||
def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
|
||||
super().__init__(noise_schedule_config=noise_schedule_config)
|
||||
self.max_noise_level = max_noise_level
|
||||
|
||||
def forward(self, x, noise_level=None):
|
||||
if noise_level is None:
|
||||
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
|
||||
else:
|
||||
assert isinstance(noise_level, torch.Tensor)
|
||||
z = self.q_sample(x, noise_level)
|
||||
return z, noise_level
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,278 @@
|
||||
# adopted from
|
||||
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
|
||||
# and
|
||||
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
||||
# and
|
||||
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
|
||||
#
|
||||
# thanks!
|
||||
|
||||
|
||||
import os
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import repeat
|
||||
|
||||
from comfy.ldm.util import instantiate_from_config
|
||||
import comfy.ops
|
||||
|
||||
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||
if schedule == "linear":
|
||||
betas = (
|
||||
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
|
||||
)
|
||||
|
||||
elif schedule == "cosine":
|
||||
timesteps = (
|
||||
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
|
||||
)
|
||||
alphas = timesteps / (1 + cosine_s) * np.pi / 2
|
||||
alphas = torch.cos(alphas).pow(2)
|
||||
alphas = alphas / alphas[0]
|
||||
betas = 1 - alphas[1:] / alphas[:-1]
|
||||
betas = np.clip(betas, a_min=0, a_max=0.999)
|
||||
|
||||
elif schedule == "squaredcos_cap_v2": # used for karlo prior
|
||||
# return early
|
||||
return betas_for_alpha_bar(
|
||||
n_timestep,
|
||||
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
|
||||
)
|
||||
|
||||
elif schedule == "sqrt_linear":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
|
||||
elif schedule == "sqrt":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
|
||||
else:
|
||||
raise ValueError(f"schedule '{schedule}' unknown.")
|
||||
return betas.numpy()
|
||||
|
||||
|
||||
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
|
||||
if ddim_discr_method == 'uniform':
|
||||
c = num_ddpm_timesteps // num_ddim_timesteps
|
||||
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
|
||||
elif ddim_discr_method == 'quad':
|
||||
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
|
||||
else:
|
||||
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
|
||||
|
||||
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
|
||||
# add one to get the final alpha values right (the ones from first scale to data during sampling)
|
||||
steps_out = ddim_timesteps + 1
|
||||
if verbose:
|
||||
print(f'Selected timesteps for ddim sampler: {steps_out}')
|
||||
return steps_out
|
||||
|
||||
|
||||
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
|
||||
# select alphas for computing the variance schedule
|
||||
alphas = alphacums[ddim_timesteps]
|
||||
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
|
||||
|
||||
# according the the formula provided in https://arxiv.org/abs/2010.02502
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
|
||||
if verbose:
|
||||
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
|
||||
print(f'For the chosen value of eta, which is {eta}, '
|
||||
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
|
||||
return sigmas, alphas, alphas_prev
|
||||
|
||||
|
||||
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
|
||||
"""
|
||||
Create a beta schedule that discretizes the given alpha_t_bar function,
|
||||
which defines the cumulative product of (1-beta) over time from t = [0,1].
|
||||
:param num_diffusion_timesteps: the number of betas to produce.
|
||||
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
|
||||
produces the cumulative product of (1-beta) up to that
|
||||
part of the diffusion process.
|
||||
:param max_beta: the maximum beta to use; use values lower than 1 to
|
||||
prevent singularities.
|
||||
"""
|
||||
betas = []
|
||||
for i in range(num_diffusion_timesteps):
|
||||
t1 = i / num_diffusion_timesteps
|
||||
t2 = (i + 1) / num_diffusion_timesteps
|
||||
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
|
||||
return np.array(betas)
|
||||
|
||||
|
||||
def extract_into_tensor(a, t, x_shape):
|
||||
b, *_ = t.shape
|
||||
out = a.gather(-1, t)
|
||||
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
||||
|
||||
|
||||
def checkpoint(func, inputs, params, flag):
|
||||
"""
|
||||
Evaluate a function without caching intermediate activations, allowing for
|
||||
reduced memory at the expense of extra compute in the backward pass.
|
||||
:param func: the function to evaluate.
|
||||
:param inputs: the argument sequence to pass to `func`.
|
||||
:param params: a sequence of parameters `func` depends on but does not
|
||||
explicitly take as arguments.
|
||||
:param flag: if False, disable gradient checkpointing.
|
||||
"""
|
||||
if flag:
|
||||
args = tuple(inputs) + tuple(params)
|
||||
return CheckpointFunction.apply(func, len(inputs), *args)
|
||||
else:
|
||||
return func(*inputs)
|
||||
|
||||
|
||||
class CheckpointFunction(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, run_function, length, *args):
|
||||
ctx.run_function = run_function
|
||||
ctx.input_tensors = list(args[:length])
|
||||
ctx.input_params = list(args[length:])
|
||||
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
|
||||
"dtype": torch.get_autocast_gpu_dtype(),
|
||||
"cache_enabled": torch.is_autocast_cache_enabled()}
|
||||
with torch.no_grad():
|
||||
output_tensors = ctx.run_function(*ctx.input_tensors)
|
||||
return output_tensors
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, *output_grads):
|
||||
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
|
||||
with torch.enable_grad(), \
|
||||
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
|
||||
# Fixes a bug where the first op in run_function modifies the
|
||||
# Tensor storage in place, which is not allowed for detach()'d
|
||||
# Tensors.
|
||||
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
|
||||
output_tensors = ctx.run_function(*shallow_copies)
|
||||
input_grads = torch.autograd.grad(
|
||||
output_tensors,
|
||||
ctx.input_tensors + ctx.input_params,
|
||||
output_grads,
|
||||
allow_unused=True,
|
||||
)
|
||||
del ctx.input_tensors
|
||||
del ctx.input_params
|
||||
del output_tensors
|
||||
return (None, None) + input_grads
|
||||
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an [N x dim] Tensor of positional embeddings.
|
||||
"""
|
||||
if not repeat_only:
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
||||
).to(device=timesteps.device)
|
||||
args = timesteps[:, 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)
|
||||
else:
|
||||
embedding = repeat(timesteps, 'b -> b d', d=dim)
|
||||
return embedding
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
"""
|
||||
Zero out the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().zero_()
|
||||
return module
|
||||
|
||||
|
||||
def scale_module(module, scale):
|
||||
"""
|
||||
Scale the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().mul_(scale)
|
||||
return module
|
||||
|
||||
|
||||
def mean_flat(tensor):
|
||||
"""
|
||||
Take the mean over all non-batch dimensions.
|
||||
"""
|
||||
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
||||
|
||||
|
||||
def normalization(channels, dtype=None):
|
||||
"""
|
||||
Make a standard normalization layer.
|
||||
:param channels: number of input channels.
|
||||
:return: an nn.Module for normalization.
|
||||
"""
|
||||
return GroupNorm32(32, channels, dtype=dtype)
|
||||
|
||||
|
||||
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
|
||||
class SiLU(nn.Module):
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class GroupNorm32(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D convolution module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.Conv1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return comfy.ops.Conv2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.Conv3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def linear(*args, **kwargs):
|
||||
"""
|
||||
Create a linear module.
|
||||
"""
|
||||
return comfy.ops.Linear(*args, **kwargs)
|
||||
|
||||
|
||||
def avg_pool_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D average pooling module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.AvgPool1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.AvgPool2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.AvgPool3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
class HybridConditioner(nn.Module):
|
||||
|
||||
def __init__(self, c_concat_config, c_crossattn_config):
|
||||
super().__init__()
|
||||
self.concat_conditioner = instantiate_from_config(c_concat_config)
|
||||
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
|
||||
|
||||
def forward(self, c_concat, c_crossattn):
|
||||
c_concat = self.concat_conditioner(c_concat)
|
||||
c_crossattn = self.crossattn_conditioner(c_crossattn)
|
||||
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
|
||||
|
||||
|
||||
def noise_like(shape, device, repeat=False):
|
||||
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
|
||||
noise = lambda: torch.randn(shape, device=device)
|
||||
return repeat_noise() if repeat else noise()
|
||||
Reference in New Issue
Block a user