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add MLP based time conditioning to all convnexts, in addition to cross attention. also add an initial convolution, given convnext first depthwise conv

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This commit is contained in:
Phil Wang
2022-05-01 08:41:02 -07:00
parent 5bfbccda22
commit 67fcab1122
2 changed files with 51 additions and 20 deletions
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69
dalle2_pytorch/dalle2_pytorch.py
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@@ -922,6 +922,7 @@ class ConvNextBlock(nn.Module):
dim_out,
*,
cond_dim = None,
time_cond_dim = None,
mult = 2,
norm = True
):
@@ -940,6 +941,14 @@ class ConvNextBlock(nn.Module):
)
)
self.time_mlp = None
if exists(time_cond_dim):
self.time_mlp = nn.Sequential(
nn.GELU(),
nn.Linear(time_cond_dim, dim)
)
self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
inner_dim = int(dim_out * mult)
@@ -952,9 +961,13 @@ class ConvNextBlock(nn.Module):
self.res_conv = nn.Conv2d(dim, dim_out, 1) if need_projection else nn.Identity()
def forward(self, x, cond = None):
def forward(self, x, cond = None, time = None):
h = self.ds_conv(x)
if exists(time) and exists(self.time_mlp):
t = self.time_mlp(time)
h = rearrange(t, 'b c -> b c 1 1') + h
if exists(self.cross_attn):
assert exists(cond)
h = self.cross_attn(h, context = cond) + h
@@ -1076,22 +1089,33 @@ class Unet(nn.Module):
self.channels = channels
init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
init_dim = dim // 2
dims = [init_channels, *map(lambda m: dim * m, dim_mults)]
self.init_conv = nn.Conv2d(init_channels, init_dim, 7, padding = 3)
dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
in_out = list(zip(dims[:-1], dims[1:]))
# time, image embeddings, and optional text encoding
cond_dim = default(cond_dim, dim)
time_cond_dim = dim * 4
self.time_mlp = nn.Sequential(
self.to_time_hiddens = nn.Sequential(
SinusoidalPosEmb(dim),
nn.Linear(dim, dim * 4),
nn.GELU(),
nn.Linear(dim * 4, cond_dim * num_time_tokens),
nn.Linear(dim, time_cond_dim),
nn.GELU()
)
self.to_time_tokens = nn.Sequential(
nn.Linear(time_cond_dim, cond_dim * num_time_tokens),
Rearrange('b (r d) -> b r d', r = num_time_tokens)
)
self.to_time_cond = nn.Sequential(
nn.Linear(time_cond_dim, time_cond_dim)
)
self.image_to_cond = nn.Sequential(
nn.Linear(image_embed_dim, cond_dim * num_image_tokens),
Rearrange('b (n d) -> b n d', n = num_image_tokens)
@@ -1133,26 +1157,26 @@ class Unet(nn.Module):
layer_cond_dim = cond_dim if not is_first else None
self.downs.append(nn.ModuleList([
ConvNextBlock(dim_in, dim_out, norm = ind != 0),
ConvNextBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, norm = ind != 0),
Residual(GridAttention(dim_out, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim),
ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
Downsample(dim_out) if not is_last else nn.Identity()
]))
mid_dim = dims[-1]
self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
is_last = ind >= (num_resolutions - 2)
layer_cond_dim = cond_dim if not is_last else None
self.ups.append(nn.ModuleList([
ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim),
ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
Residual(GridAttention(dim_in, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim),
ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
Upsample(dim_in)
]))
@@ -1214,9 +1238,16 @@ class Unet(nn.Module):
if exists(lowres_cond_img):
x = torch.cat((x, lowres_cond_img), dim = 1)
# initial convolution
x = self.init_conv(x)
# time conditioning
time_tokens = self.time_mlp(time)
time_hiddens = self.to_time_hiddens(time)
time_tokens = self.to_time_tokens(time_hiddens)
t = self.to_time_cond(time_hiddens)
# conditional dropout
@@ -1283,24 +1314,24 @@ class Unet(nn.Module):
hiddens = []
for convnext, sparse_attn, convnext2, downsample in self.downs:
x = convnext(x, c)
x = convnext(x, c, t)
x = sparse_attn(x)
x = convnext2(x, c)
x = convnext2(x, c, t)
hiddens.append(x)
x = downsample(x)
x = self.mid_block1(x, mid_c)
x = self.mid_block1(x, mid_c, t)
if exists(self.mid_attn):
x = self.mid_attn(x)
x = self.mid_block2(x, mid_c)
x = self.mid_block2(x, mid_c, t)
for convnext, sparse_attn, convnext2, upsample in self.ups:
x = torch.cat((x, hiddens.pop()), dim=1)
x = convnext(x, c)
x = convnext(x, c, t)
x = sparse_attn(x)
x = convnext2(x, c)
x = convnext2(x, c, t)
x = upsample(x)
return self.final_conv(x)

2
setup.py
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@@ -10,7 +10,7 @@ setup(
'dream = dalle2_pytorch.cli:dream'
],
},
version = '0.0.82',
version = '0.0.84',
license='MIT',
description = 'DALL-E 2',
author = 'Phil Wang',