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update mid block (#70) 

* update mid block

* finish mid block
This commit is contained in:
Patrick von Platen 2022-07-04 11:52:22 +02:00 committed by GitHub
parent 4e2674934f
commit 94566e6dd8
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7 changed files with 361 additions and 147 deletions
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268
src/diffusers/models/attention.py
View File

@ -1,6 +1,8 @@
import math
from inspect import isfunction
import torch
import torch.nn.functional as F
from torch import nn
@ -43,18 +45,16 @@ class AttentionBlock(nn.Module):
self,
channels,
num_heads=1,
num_head_channels=-1,
num_head_channels=None,
num_groups=32,
use_checkpoint=False,
encoder_channels=None,
use_new_attention_order=False, # TODO(Patrick) -> is never used, maybe delete?
overwrite_qkv=False,
overwrite_linear=False,
rescale_output_factor=1.0,
):
super().__init__()
self.channels = channels
if num_head_channels == -1:
if num_head_channels is None:
self.num_heads = num_heads
else:
assert (
@ -62,7 +62,6 @@ class AttentionBlock(nn.Module):
), 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 = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-5, affine=True)
self.qkv = nn.Conv1d(channels, channels * 3, 1)
self.n_heads = self.num_heads
@ -160,115 +159,135 @@ class AttentionBlock(nn.Module):
return result
# unet_score_estimation.py
# class AttnBlockpp(nn.Module):
# """Channel-wise self-attention block. Modified from DDPM."""
#
# def __init__(
# self,
# channels,
# skip_rescale=False,
# init_scale=0.0,
# num_heads=1,
# num_head_channels=-1,
# use_checkpoint=False,
# encoder_channels=None,
# use_new_attention_order=False, # TODO(Patrick) -> is never used, maybe delete?
# overwrite_qkv=False,
# overwrite_from_grad_tts=False,
# ):
# super().__init__()
# num_groups = min(channels // 4, 32)
# self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=channels, eps=1e-6)
# self.NIN_0 = NIN(channels, channels)
# self.NIN_1 = NIN(channels, channels)
# self.NIN_2 = NIN(channels, channels)
# self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
# self.skip_rescale = skip_rescale
#
# 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 = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-6)
# self.qkv = nn.Conv1d(channels, channels * 3, 1)
# self.n_heads = self.num_heads
#
# self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
#
# self.is_weight_set = False
#
# def set_weights(self):
# self.qkv.weight.data = torch.concat([self.NIN_0.W.data.T, self.NIN_1.W.data.T, self.NIN_2.W.data.T], dim=0)[:, :, None]
# self.qkv.bias.data = torch.concat([self.NIN_0.b.data, self.NIN_1.b.data, self.NIN_2.b.data], dim=0)
#
# self.proj_out.weight.data = self.NIN_3.W.data.T[:, :, None]
# self.proj_out.bias.data = self.NIN_3.b.data
#
# self.norm.weight.data = self.GroupNorm_0.weight.data
# self.norm.bias.data = self.GroupNorm_0.bias.data
#
# def forward(self, x):
# if not self.is_weight_set:
# self.set_weights()
# self.is_weight_set = True
#
# B, C, H, W = x.shape
# h = self.GroupNorm_0(x)
# q = self.NIN_0(h)
# k = self.NIN_1(h)
# v = self.NIN_2(h)
#
# w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
# w = torch.reshape(w, (B, H, W, H * W))
# w = F.softmax(w, dim=-1)
# w = torch.reshape(w, (B, H, W, H, W))
# h = torch.einsum("bhwij,bcij->bchw", w, v)
# h = self.NIN_3(h)
#
# if not self.skip_rescale:
# result = x + h
# else:
# result = (x + h) / np.sqrt(2.0)
#
# result = self.forward_2(x)
#
# return result
#
# def forward_2(self, x, encoder_out=None):
# b, c, *spatial = x.shape
# hid_states = self.norm(x).view(b, c, -1)
#
# qkv = self.qkv(hid_states)
# 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)
#
# if encoder_out is not None:
# encoder_kv = self.encoder_kv(encoder_out)
# assert encoder_kv.shape[1] == self.n_heads * ch * 2
# ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
# k = torch.cat([ek, k], dim=-1)
# v = torch.cat([ev, v], dim=-1)
#
# scale = 1 / math.sqrt(math.sqrt(ch))
# weight = torch.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards
# weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
#
# a = torch.einsum("bts,bcs->bct", weight, v)
# h = a.reshape(bs, -1, length)
#
# h = self.proj_out(h)
# h = h.reshape(b, c, *spatial)
#
# return (x + h) / np.sqrt(2.0)
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
standard transformer action. Finally, reshape to image
"""
def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)
]
)
self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
for block in self.transformer_blocks:
x = block(x, context=context)
x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
x = self.proj_out(x)
return x + x_in
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True):
super().__init__()
self.attn1 = CrossAttention(
query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(
query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head**-0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def reshape_batch_dim_to_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def forward(self, x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if exists(mask):
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return self.to_out(out)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
def forward(self, x):
return self.net(x)
# TODO(Patrick) - this can and should be removed
@ -287,3 +306,24 @@ class NIN(nn.Module):
super().__init__()
self.W = nn.Parameter(torch.zeros(in_dim, num_units), requires_grad=True)
self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)

18
src/diffusers/models/unet.py
View File

@ -23,6 +23,7 @@ from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
def nonlinearity(x):
@ -105,13 +106,8 @@ class UNetModel(ModelMixin, ConfigMixin):
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock2D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
)
self.mid.attn_1 = AttentionBlock(block_in, overwrite_qkv=True)
self.mid.block_2 = ResnetBlock2D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
self.mid = UNetMidBlock2D(
in_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, overwrite_qkv=True, overwrite_unet=True
)
# upsampling
@ -171,10 +167,10 @@ class UNetModel(ModelMixin, ConfigMixin):
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)
h = self.mid(hs[-1], temb)
# 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)):

26
src/diffusers/models/unet_glide.py
View File

@ -7,6 +7,7 @@ from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
def convert_module_to_f16(l):
@ -193,7 +194,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
encoder_channels=transformer_dim,
@ -226,6 +226,20 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
ds *= 2
self._feature_size += ch
self.mid = UNetMidBlock2D(
in_channels=ch,
dropout=dropout,
temb_channels=time_embed_dim,
resnet_eps=1e-5,
resnet_act_fn="silu",
resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
attn_num_heads=num_heads,
attn_num_head_channels=num_head_channels,
attn_encoder_channels=transformer_dim,
)
# TODO(Patrick) - delete after weight conversion
# init to be able to overwrite `self.mid`
self.middle_block = TimestepEmbedSequential(
ResnetBlock2D(
in_channels=ch,
@ -238,7 +252,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
encoder_channels=transformer_dim,
@ -253,6 +266,10 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
overwrite_for_glide=True,
),
)
self.mid.resnet_1 = self.middle_block[0]
self.mid.attn = self.middle_block[1]
self.mid.resnet_2 = self.middle_block[2]
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
@ -276,7 +293,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
encoder_channels=transformer_dim,
@ -343,7 +359,7 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
for module in self.input_blocks:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
h = self.mid(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
@ -438,7 +454,7 @@ class GlideTextToImageUNetModel(GlideUNetModel):
for module in self.input_blocks:
h = module(h, emb, transformer_out)
hs.append(h)
h = self.middle_block(h, emb, transformer_out)
h = self.mid(h, emb, transformer_out)
for module in self.output_blocks:
other = hs.pop()
h = torch.cat([h, other], dim=1)

2
src/diffusers/models/unet_grad_tts.py
View File

@ -133,6 +133,8 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
overwrite_for_grad_tts=True,
)
# self.mid = UNetMidBlock2D
for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
self.ups.append(
torch.nn.ModuleList(

35
src/diffusers/models/unet_ldm.py
View File

@ -11,6 +11,7 @@ from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
# from .resnet import ResBlock
@ -239,14 +240,12 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
conv_resample=conv_resample,
dims=dims,
num_classes=num_classes,
use_checkpoint=use_checkpoint,
use_fp16=use_fp16,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
resblock_updown=resblock_updown,
use_new_attention_order=use_new_attention_order,
use_spatial_transformer=use_spatial_transformer,
transformer_depth=transformer_depth,
context_dim=context_dim,
@ -283,7 +282,6 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype_ = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
@ -333,10 +331,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
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(
@ -366,6 +362,25 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
if dim_head < 0:
dim_head = None
self.mid = UNetMidBlock2D(
in_channels=ch,
dropout=dropout,
temb_channels=time_embed_dim,
resnet_eps=1e-5,
resnet_act_fn="silu",
resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
attention_layer_type="self" if not use_spatial_transformer else "spatial",
attn_num_heads=num_heads,
attn_num_head_channels=dim_head,
attn_depth=transformer_depth,
attn_encoder_channels=context_dim,
)
# TODO(Patrick) - delete after weight conversion
# init to be able to overwrite `self.mid`
self.middle_block = TimestepEmbedSequential(
ResnetBlock2D(
in_channels=ch,
@ -378,10 +393,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
),
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, depth=transformer_depth, context_dim=context_dim),
@ -395,6 +408,10 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
overwrite_for_ldm=True,
),
)
self.mid.resnet_1 = self.middle_block[0]
self.mid.attn = self.middle_block[1]
self.mid.resnet_2 = self.middle_block[2]
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
@ -425,10 +442,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
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(
@ -493,7 +508,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
for module in self.input_blocks:
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
h = self.mid(h, emb, context)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb, context)

128
src/diffusers/models/unet_new.py Normal file
View File

@ -0,0 +1,128 @@
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from torch import nn
from .attention import AttentionBlock, SpatialTransformer
from .resnet import ResnetBlock2D
class UNetMidBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
attention_layer_type: str = "self",
attn_num_heads=1,
attn_num_head_channels=None,
attn_encoder_channels=None,
attn_dim_head=None,
attn_depth=None,
output_scale_factor=1.0,
overwrite_qkv=False,
overwrite_unet=False,
):
super().__init__()
self.resnet_1 = ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
)
if attention_layer_type == "self":
self.attn = AttentionBlock(
in_channels,
num_heads=attn_num_heads,
num_head_channels=attn_num_head_channels,
encoder_channels=attn_encoder_channels,
overwrite_qkv=overwrite_qkv,
rescale_output_factor=output_scale_factor,
)
elif attention_layer_type == "spatial":
self.attn = (
SpatialTransformer(
in_channels,
attn_num_heads,
attn_num_head_channels,
depth=attn_depth,
context_dim=attn_encoder_channels,
),
)
self.resnet_2 = ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
)
# TODO(Patrick) - delete all of the following code
self.is_overwritten = False
self.overwrite_unet = overwrite_unet
if self.overwrite_unet:
block_in = in_channels
self.temb_ch = temb_channels
self.block_1 = ResnetBlock2D(
in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
eps=resnet_eps,
)
self.attn_1 = AttentionBlock(
block_in,
num_heads=attn_num_heads,
num_head_channels=attn_num_head_channels,
encoder_channels=attn_encoder_channels,
overwrite_qkv=True,
)
self.block_2 = ResnetBlock2D(
in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
eps=resnet_eps,
)
def forward(self, hidden_states, temb=None, encoder_states=None):
if not self.is_overwritten and self.overwrite_unet:
self.resnet_1 = self.block_1
self.attn = self.attn_1
self.resnet_2 = self.block_2
self.is_overwritten = True
hidden_states = self.resnet_1(hidden_states, temb)
if encoder_states is None:
hidden_states = self.attn(hidden_states)
else:
hidden_states = self.attn(hidden_states, encoder_states)
hidden_states = self.resnet_2(hidden_states, temb)
return hidden_states

31
src/diffusers/models/unet_sde_score_estimation.py
View File

@ -27,6 +27,7 @@ from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import GaussianFourierProjection, get_timestep_embedding
from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
class Combine(nn.Module):
@ -214,6 +215,16 @@ class NCSNpp(ModelMixin, ConfigMixin):
hs_c.append(in_ch)
# mid
self.mid = UNetMidBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=math.sqrt(2.0),
resnet_act_fn="silu",
resnet_groups=min(in_ch // 4, 32),
dropout=dropout,
)
in_ch = hs_c[-1]
modules.append(
ResnetBlock2D(
@ -238,6 +249,9 @@ class NCSNpp(ModelMixin, ConfigMixin):
overwrite_for_score_vde=True,
)
)
self.mid.resnet_1 = modules[len(modules) - 3]
self.mid.attn = modules[len(modules) - 2]
self.mid.resnet_2 = modules[len(modules) - 1]
pyramid_ch = 0
# Upsampling block
@ -378,13 +392,16 @@ class NCSNpp(ModelMixin, ConfigMixin):
hs.append(h)
h = hs[-1]
h = modules[m_idx](h, temb)
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
h = modules[m_idx](h, temb)
m_idx += 1
# h = hs[-1]
# h = modules[m_idx](h, temb)
# m_idx += 1
# h = modules[m_idx](h)
# m_idx += 1
# h = modules[m_idx](h, temb)
# m_idx += 1
h = self.mid(h, temb)
m_idx += 3
pyramid = None