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# -------------------------------------------------------- | |
# Stable-Diffusion-Torch | |
# Based on Stable-Diffusion (https://github.com/CompVis/stable-diffusion) | |
# Removed Pytorch-lightning by Zigang Geng (zigang@mail.ustc.edu.cn) | |
# -------------------------------------------------------- | |
from abc import abstractmethod | |
from functools import partial | |
import math | |
from typing import Iterable | |
import numpy as np | |
import torch as th | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from ldm.modules.diffusionmodules.util import ( | |
checkpoint, | |
conv_nd, | |
linear, | |
avg_pool_nd, | |
zero_module, | |
normalization, | |
timestep_embedding, | |
) | |
from ldm.modules.attention import SpatialTransformer, CrossAttention, FeedForward | |
# dummy replace | |
def convert_module_to_f16(l): | |
""" | |
Convert primitive modules to float16. | |
""" | |
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): | |
l.weight.data = l.weight.data.half() | |
if l.bias is not None: | |
l.bias.data = l.bias.data.half() | |
def convert_module_to_f32(l): | |
""" | |
Convert primitive modules to float32, undoing convert_module_to_f16(). | |
""" | |
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): | |
l.weight.data = l.weight.data.float() | |
if l.bias is not None: | |
l.bias.data = l.bias.data.float() | |
def convert_some_linear_to_f16(l): | |
""" | |
Convert linear modules to float16. | |
""" | |
if isinstance(l, nn.Linear): | |
l.weight.data = l.weight.data.half() | |
if l.bias is not None: | |
l.bias.data = l.bias.data.half() | |
def convert_some_linear_to_f32(l): | |
""" | |
Convert linear modules to float32. | |
""" | |
if isinstance(l, nn.Linear): | |
l.weight.data = l.weight.data.float() | |
if l.bias is not None: | |
l.bias.data = l.bias.data.float() | |
class PositionEmbedding(nn.Module): | |
def __init__(self, embed_dim, spacial_dim): | |
super().__init__() | |
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) | |
def forward(self): | |
return self.positional_embedding | |
## go | |
class AttentionPool2d(nn.Module): | |
""" | |
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py | |
""" | |
def __init__( | |
self, | |
spacial_dim: int, | |
embed_dim: int, | |
num_heads_channels: int, | |
output_dim: int = None, | |
): | |
super().__init__() | |
self.positional_embedding = PositionEmbedding(embed_dim, spacial_dim) | |
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) | |
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) | |
self.num_heads = embed_dim // num_heads_channels | |
self.attention = QKVAttention(self.num_heads) | |
def forward(self, x): | |
b, c, *_spatial = x.shape | |
x = x.reshape(b, c, -1) # NC(HW) | |
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) | |
x = x + self.positional_embedding()[None, :, :].to(x.dtype) # NC(HW+1) | |
x = self.qkv_proj(x) | |
x = self.attention(x) | |
x = self.c_proj(x) | |
return x[:, :, 0] | |
class TimestepBlock(nn.Module): | |
""" | |
Any module where forward() takes timestep embeddings as a second argument. | |
""" | |
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): | |
for layer in self: | |
if isinstance(layer, TimestepBlock): | |
x = layer(x, emb) | |
elif isinstance(layer, SpatialTransformer): | |
x = layer(x, context) | |
else: | |
if isinstance(layer, Downsample) or isinstance(layer, Upsample): | |
x = layer(x) | |
else: | |
if hasattr(layer, 'weight'): | |
x = layer(x.type(layer.weight.dtype)) | |
else: | |
x = layer(x.type(emb.dtype)) | |
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): | |
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 = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) | |
def forward(self, x): | |
assert x.shape[1] == self.channels | |
if self.dims == 3: | |
x = F.interpolate( | |
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" | |
) | |
else: | |
x = F.interpolate(x, scale_factor=2, mode="nearest") | |
if self.use_conv: | |
x = self.conv(x) | |
return x | |
class TransposedUpsample(nn.Module): | |
'Learned 2x upsampling without padding' | |
def __init__(self, channels, out_channels=None, ks=5): | |
super().__init__() | |
self.channels = channels | |
self.out_channels = out_channels or channels | |
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) | |
def forward(self,x): | |
return self.up(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): | |
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 = conv_nd( | |
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding | |
) | |
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, | |
): | |
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( | |
normalization(channels), | |
nn.SiLU(), | |
conv_nd(dims, channels, self.out_channels, 3, padding=1), | |
) | |
self.updown = up or down | |
if up: | |
self.h_upd = Upsample(channels, False, dims) | |
self.x_upd = Upsample(channels, False, dims) | |
elif down: | |
self.h_upd = Downsample(channels, False, dims) | |
self.x_upd = Downsample(channels, False, dims) | |
else: | |
self.h_upd = self.x_upd = nn.Identity() | |
self.emb_layers = nn.Sequential( | |
nn.SiLU(), | |
linear( | |
emb_channels, | |
2 * self.out_channels if use_scale_shift_norm else self.out_channels, | |
), | |
) | |
self.out_layers = nn.Sequential( | |
normalization(self.out_channels), | |
nn.SiLU(), | |
nn.Dropout(p=dropout), | |
zero_module( | |
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) | |
), | |
) | |
if self.out_channels == channels: | |
self.skip_connection = nn.Identity() | |
elif use_conv: | |
self.skip_connection = conv_nd( | |
dims, channels, self.out_channels, 3, padding=1 | |
) | |
else: | |
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) | |
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.use_checkpoint | |
# ) | |
return checkpoint( | |
self._forward, (x, emb), self.parameters(), self.use_checkpoint | |
) | |
def _forward(self, x, emb): | |
x = x.type(self.emb_layers[1].weight.dtype) | |
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(self.emb_layers[1].weight.dtype)).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.type(self.emb_layers[1].weight.dtype) | |
class AttentionBlock(nn.Module): | |
""" | |
An attention block that allows spatial positions to attend to each other. | |
Originally ported from here, but adapted to the N-d case. | |
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. | |
""" | |
def __init__( | |
self, | |
channels, | |
num_heads=1, | |
num_head_channels=-1, | |
use_checkpoint=False, | |
use_new_attention_order=False, | |
): | |
super().__init__() | |
self.channels = channels | |
if num_head_channels == -1: | |
self.num_heads = num_heads | |
else: | |
assert ( | |
channels % num_head_channels == 0 | |
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" | |
self.num_heads = channels // num_head_channels | |
self.use_checkpoint = use_checkpoint | |
self.norm = normalization(channels) | |
self.qkv = conv_nd(1, channels, channels * 3, 1) | |
if use_new_attention_order: | |
# split qkv before split heads | |
self.attention = QKVAttention(self.num_heads) | |
else: | |
# split heads before split qkv | |
self.attention = QKVAttentionLegacy(self.num_heads) | |
self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) | |
def forward(self, x): | |
# return checkpoint(self._forward, (x,), self.use_checkpoint) | |
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! | |
def _forward(self, x): | |
b, c, *spatial = x.shape | |
x = x.reshape(b, c, -1) | |
qkv = self.qkv(self.norm(x)) | |
h = self.attention(qkv) | |
h = self.proj_out(h) | |
return (x + h).reshape(b, c, *spatial) | |
def count_flops_attn(model, _x, y): | |
""" | |
A counter for the `thop` package to count the operations in an | |
attention operation. | |
Meant to be used like: | |
macs, params = thop.profile( | |
model, | |
inputs=(inputs, timestamps), | |
custom_ops={QKVAttention: QKVAttention.count_flops}, | |
) | |
""" | |
b, c, *spatial = y[0].shape | |
num_spatial = int(np.prod(spatial)) | |
# We perform two matmuls with the same number of ops. | |
# The first computes the weight matrix, the second computes | |
# the combination of the value vectors. | |
matmul_ops = 2 * b * (num_spatial ** 2) * c | |
model.total_ops += th.DoubleTensor([matmul_ops]) | |
class QKVAttentionLegacy(nn.Module): | |
""" | |
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping | |
""" | |
def __init__(self, n_heads): | |
super().__init__() | |
self.n_heads = n_heads | |
def forward(self, qkv): | |
""" | |
Apply QKV attention. | |
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. | |
:return: an [N x (H * C) x T] tensor after attention. | |
""" | |
bs, width, length = qkv.shape | |
assert width % (3 * self.n_heads) == 0 | |
ch = width // (3 * self.n_heads) | |
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) | |
scale = 1 / math.sqrt(math.sqrt(ch)) | |
weight = th.einsum( | |
"bct,bcs->bts", q * scale, k * scale | |
) # More stable with f16 than dividing afterwards | |
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) | |
a = th.einsum("bts,bcs->bct", weight, v) | |
return a.reshape(bs, -1, length) | |
def count_flops(model, _x, y): | |
return count_flops_attn(model, _x, y) | |
class QKVAttention(nn.Module): | |
""" | |
A module which performs QKV attention and splits in a different order. | |
""" | |
def __init__(self, n_heads): | |
super().__init__() | |
self.n_heads = n_heads | |
def forward(self, qkv): | |
""" | |
Apply QKV attention. | |
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. | |
:return: an [N x (H * C) x T] tensor after attention. | |
""" | |
bs, width, length = qkv.shape | |
assert width % (3 * self.n_heads) == 0 | |
ch = width // (3 * self.n_heads) | |
q, k, v = qkv.chunk(3, dim=1) | |
scale = 1 / math.sqrt(math.sqrt(ch)) | |
weight = th.einsum( | |
"bct,bcs->bts", | |
(q * scale).view(bs * self.n_heads, ch, length), | |
(k * scale).view(bs * self.n_heads, ch, length), | |
) # More stable with f16 than dividing afterwards | |
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) | |
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) | |
return a.reshape(bs, -1, length) | |
def count_flops(model, _x, y): | |
return count_flops_attn(model, _x, y) | |
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, | |
default_eps=False, | |
force_type_convert=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, | |
): | |
super().__init__() | |
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 | |
self.num_res_blocks = num_res_blocks | |
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.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( | |
linear(model_channels, time_embed_dim), | |
nn.SiLU(), | |
linear(time_embed_dim, time_embed_dim), | |
) | |
if self.num_classes is not None: | |
self.label_emb = nn.Embedding(num_classes, time_embed_dim) | |
self.input_blocks = nn.ModuleList( | |
[ | |
TimestepEmbedSequential( | |
conv_nd(dims, in_channels, model_channels, 3, padding=1) | |
) | |
] | |
) | |
self._feature_size = model_channels | |
input_block_chans = [model_channels] | |
ch = model_channels | |
ds = 1 | |
for level, mult in enumerate(channel_mult): | |
for _ in range(num_res_blocks): | |
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, | |
) | |
] | |
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 | |
layers.append( | |
AttentionBlock( | |
ch, | |
use_checkpoint=use_checkpoint, | |
num_heads=num_heads, | |
num_head_channels=dim_head, | |
use_new_attention_order=use_new_attention_order, | |
) if not use_spatial_transformer else SpatialTransformer( | |
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim | |
) | |
) | |
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, | |
) | |
if resblock_updown | |
else Downsample( | |
ch, conv_resample, dims=dims, out_channels=out_ch | |
) | |
) | |
) | |
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, | |
), | |
AttentionBlock( | |
ch, | |
use_checkpoint=use_checkpoint, | |
num_heads=num_heads, | |
num_head_channels=dim_head, | |
use_new_attention_order=use_new_attention_order, | |
) if not use_spatial_transformer else SpatialTransformer( | |
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim | |
), | |
ResBlock( | |
ch, | |
time_embed_dim, | |
dropout, | |
dims=dims, | |
use_checkpoint=use_checkpoint, | |
use_scale_shift_norm=use_scale_shift_norm, | |
), | |
) | |
self._feature_size += ch | |
self.output_blocks = nn.ModuleList([]) | |
for level, mult in list(enumerate(channel_mult))[::-1]: | |
for i in range(num_res_blocks + 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, | |
) | |
] | |
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 | |
layers.append( | |
AttentionBlock( | |
ch, | |
use_checkpoint=use_checkpoint, | |
num_heads=num_heads_upsample, | |
num_head_channels=dim_head, | |
use_new_attention_order=use_new_attention_order, | |
) if not use_spatial_transformer else SpatialTransformer( | |
ch, num_heads, dim_head, default_eps=default_eps, force_type_convert=force_type_convert, depth=transformer_depth, context_dim=context_dim | |
) | |
) | |
if level and i == num_res_blocks: | |
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, | |
) | |
if resblock_updown | |
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) | |
) | |
ds //= 2 | |
self.output_blocks.append(TimestepEmbedSequential(*layers)) | |
self._feature_size += ch | |
self.out = nn.Sequential( | |
normalization(ch), | |
nn.SiLU(), | |
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), | |
) | |
if self.predict_codebook_ids: | |
self.id_predictor = nn.Sequential( | |
normalization(ch), | |
conv_nd(dims, model_channels, n_embed, 1), | |
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits | |
) | |
# if use_fp16: | |
# self.convert_to_fp16() | |
# for name, m in self.named_modules(): | |
# if 'output_blocks.11.1' in name: | |
# if isinstance(m, (nn.Conv2d, nn.LayerNorm)) or 'proj_out' in name: | |
# m.weight.data = m.weight.data.float() | |
# m.bias.data = m.bias.data.float() | |
def convert_to_fp16(self): | |
""" | |
Convert the torso of the model to float16. | |
""" | |
self.input_blocks.apply(convert_module_to_f16) | |
self.middle_block.apply(convert_module_to_f16) | |
self.output_blocks.apply(convert_module_to_f16) | |
for m in self.modules(): | |
if isinstance(m, (CrossAttention, FeedForward)): | |
m.apply(convert_some_linear_to_f16) | |
def convert_to_fp32(self): | |
""" | |
Convert the torso of the model to float32. | |
""" | |
self.input_blocks.apply(convert_module_to_f32) | |
self.middle_block.apply(convert_module_to_f32) | |
self.output_blocks.apply(convert_module_to_f32) | |
for m in self.modules(): | |
if isinstance(m, (CrossAttention, FeedForward)): | |
m.apply(convert_some_linear_to_f32) | |
def forward(self, x, timesteps=None, context=None, y=None,**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. | |
""" | |
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) | |
emb = self.time_embed(t_emb.type(self.time_embed[0].weight.dtype)) | |
if self.num_classes is not None: | |
assert y.shape == (x.shape[0],) | |
emb = emb + self.label_emb(y) | |
h = x.type(self.dtype) | |
for module in self.input_blocks: | |
h = module(h, emb, context) | |
hs.append(h) | |
h = self.middle_block(h, emb, context) | |
for module in self.output_blocks: | |
h = th.cat([h, hs.pop()], dim=1) | |
h = module(h, emb, context) | |
h = h.type(x.dtype) | |
if self.predict_codebook_ids: | |
return self.id_predictor(h) | |
else: | |
return self.out(h) | |
class EncoderUNetModel(nn.Module): | |
""" | |
The half UNet model with attention and timestep embedding. | |
For usage, see UNet. | |
""" | |
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, | |
use_checkpoint=False, | |
use_fp16=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, | |
pool="adaptive", | |
*args, | |
**kwargs | |
): | |
super().__init__() | |
if num_heads_upsample == -1: | |
num_heads_upsample = num_heads | |
self.in_channels = in_channels | |
self.model_channels = model_channels | |
self.out_channels = out_channels | |
self.num_res_blocks = num_res_blocks | |
self.attention_resolutions = attention_resolutions | |
self.dropout = dropout | |
self.channel_mult = channel_mult | |
self.conv_resample = conv_resample | |
self.use_checkpoint = use_checkpoint | |
self.dtype = th.float16 if use_fp16 else th.float32 | |
self.num_heads = num_heads | |
self.num_head_channels = num_head_channels | |
self.num_heads_upsample = num_heads_upsample | |
time_embed_dim = model_channels * 4 | |
self.time_embed = nn.Sequential( | |
linear(model_channels, time_embed_dim), | |
nn.SiLU(), | |
linear(time_embed_dim, time_embed_dim), | |
) | |
self.input_blocks = nn.ModuleList( | |
[ | |
TimestepEmbedSequential( | |
conv_nd(dims, in_channels, model_channels, 3, padding=1) | |
) | |
] | |
) | |
self._feature_size = model_channels | |
input_block_chans = [model_channels] | |
ch = model_channels | |
ds = 1 | |
for level, mult in enumerate(channel_mult): | |
for _ in range(num_res_blocks): | |
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, | |
) | |
] | |
ch = mult * model_channels | |
if ds in attention_resolutions: | |
layers.append( | |
AttentionBlock( | |
ch, | |
use_checkpoint=use_checkpoint, | |
num_heads=num_heads, | |
num_head_channels=num_head_channels, | |
use_new_attention_order=use_new_attention_order, | |
) | |
) | |
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, | |
) | |
if resblock_updown | |
else Downsample( | |
ch, conv_resample, dims=dims, out_channels=out_ch | |
) | |
) | |
) | |
ch = out_ch | |
input_block_chans.append(ch) | |
ds *= 2 | |
self._feature_size += ch | |
self.middle_block = TimestepEmbedSequential( | |
ResBlock( | |
ch, | |
time_embed_dim, | |
dropout, | |
dims=dims, | |
use_checkpoint=use_checkpoint, | |
use_scale_shift_norm=use_scale_shift_norm, | |
), | |
AttentionBlock( | |
ch, | |
use_checkpoint=use_checkpoint, | |
num_heads=num_heads, | |
num_head_channels=num_head_channels, | |
use_new_attention_order=use_new_attention_order, | |
), | |
ResBlock( | |
ch, | |
time_embed_dim, | |
dropout, | |
dims=dims, | |
use_checkpoint=use_checkpoint, | |
use_scale_shift_norm=use_scale_shift_norm, | |
), | |
) | |
self._feature_size += ch | |
self.pool = pool | |
if pool == "adaptive": | |
self.out = nn.Sequential( | |
normalization(ch), | |
nn.SiLU(), | |
nn.AdaptiveAvgPool2d((1, 1)), | |
zero_module(conv_nd(dims, ch, out_channels, 1)), | |
nn.Flatten(), | |
) | |
elif pool == "attention": | |
assert num_head_channels != -1 | |
self.out = nn.Sequential( | |
normalization(ch), | |
nn.SiLU(), | |
AttentionPool2d( | |
(image_size // ds), ch, num_head_channels, out_channels | |
), | |
) | |
elif pool == "spatial": | |
self.out = nn.Sequential( | |
nn.Linear(self._feature_size, 2048), | |
nn.ReLU(), | |
nn.Linear(2048, self.out_channels), | |
) | |
elif pool == "spatial_v2": | |
self.out = nn.Sequential( | |
nn.Linear(self._feature_size, 2048), | |
normalization(2048), | |
nn.SiLU(), | |
nn.Linear(2048, self.out_channels), | |
) | |
else: | |
raise NotImplementedError(f"Unexpected {pool} pooling") | |
# if use_fp16: | |
# self.convert_to_fp16() | |
def convert_to_fp16(self): | |
""" | |
Convert the torso of the model to float16. | |
""" | |
self.input_blocks.apply(convert_module_to_f16) | |
self.middle_block.apply(convert_module_to_f16) | |
for m in self.modules(): | |
if isinstance(m, (CrossAttention, FeedForward)): | |
m.apply(convert_some_linear_to_f16) | |
def convert_to_fp32(self): | |
""" | |
Convert the torso of the model to float32. | |
""" | |
self.input_blocks.apply(convert_module_to_f32) | |
self.middle_block.apply(convert_module_to_f32) | |
for m in self.modules(): | |
if isinstance(m, (CrossAttention, FeedForward)): | |
m.apply(convert_some_linear_to_f32) | |
def forward(self, x, timesteps): | |
""" | |
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. | |
:return: an [N x K] Tensor of outputs. | |
""" | |
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) | |
results = [] | |
h = x.type(self.dtype) | |
for module in self.input_blocks: | |
h = module(h, emb) | |
if self.pool.startswith("spatial"): | |
results.append(h.type(x.dtype).mean(dim=(2, 3))) | |
h = self.middle_block(h, emb) | |
if self.pool.startswith("spatial"): | |
results.append(h.type(x.dtype).mean(dim=(2, 3))) | |
h = th.cat(results, axis=-1) | |
return self.out(h) | |
else: | |
h = h.type(x.dtype) | |
return self.out(h) | |