aljaz/DALLE2-pytorch
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make memory efficient unet design from imagen toggle-able

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This commit is contained in:
Phil Wang
2022-06-15 13:40:26 -07:00
parent 2219348a6e
commit b7f9607258
2 changed files with 15 additions and 6 deletions
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19
dalle2_pytorch/dalle2_pytorch.py
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@@ -1352,6 +1352,7 @@ class Unet(nn.Module):
init_cross_embed_kernel_sizes = (3, 7, 15), init_cross_embed_kernel_sizes = (3, 7, 15),
cross_embed_downsample = False, cross_embed_downsample = False,
cross_embed_downsample_kernel_sizes = (2, 4), cross_embed_downsample_kernel_sizes = (2, 4),
memory_efficient = False,
**kwargs **kwargs
): ):
super().__init__() super().__init__()
@@ -1462,10 +1463,11 @@ class Unet(nn.Module):
layer_cond_dim = cond_dim if not is_first else None layer_cond_dim = cond_dim if not is_first else None
self.downs.append(nn.ModuleList([ self.downs.append(nn.ModuleList([
downsample_klass(dim_in, dim_out = dim_out), downsample_klass(dim_in, dim_out = dim_out) if memory_efficient else None,
ResnetBlock(dim_out, dim_out, time_cond_dim = time_cond_dim, groups = groups), ResnetBlock(dim_out if memory_efficient else dim_in, dim_out, time_cond_dim = time_cond_dim, groups = groups),
Residual(LinearAttention(dim_out, **attn_kwargs)) if sparse_attn else nn.Identity(), Residual(LinearAttention(dim_out, **attn_kwargs)) if sparse_attn else nn.Identity(),
nn.ModuleList([ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]), nn.ModuleList([ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]),
downsample_klass(dim_out) if not is_last and not memory_efficient else None
])) ]))
mid_dim = dims[-1] mid_dim = dims[-1]
@@ -1474,7 +1476,9 @@ class Unet(nn.Module):
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_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 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1]) self.mid_block2 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(reversed(in_out), reversed(resnet_groups), reversed(num_resnet_blocks))): up_in_out_slice = slice(1 if not memory_efficient else None, None)
for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(reversed(in_out[up_in_out_slice]), reversed(resnet_groups), reversed(num_resnet_blocks))):
is_last = ind >= (num_resolutions - 2) is_last = ind >= (num_resolutions - 2)
layer_cond_dim = cond_dim if not is_last else None layer_cond_dim = cond_dim if not is_last else None
@@ -1655,8 +1659,10 @@ class Unet(nn.Module):
hiddens = [] hiddens = []
for downsample, init_block, sparse_attn, resnet_blocks in self.downs: for pre_downsample, init_block, sparse_attn, resnet_blocks, post_downsample in self.downs:
x = downsample(x) if exists(pre_downsample):
x = pre_downsample(x)
x = init_block(x, c, t) x = init_block(x, c, t)
x = sparse_attn(x) x = sparse_attn(x)
@@ -1665,6 +1671,9 @@ class Unet(nn.Module):
hiddens.append(x) hiddens.append(x)
if exists(post_downsample):
x = post_downsample(x)
x = self.mid_block1(x, mid_c, t) x = self.mid_block1(x, mid_c, t)
if exists(self.mid_attn): if exists(self.mid_attn):

2
dalle2_pytorch/version.py
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@@ -1 +1 @@
__version__ = '0.8.0' __version__ = '0.8.1'