add the main contributions of the paper

README.md

@@ -10,6 +10,91 @@ The gist of the paper comes down to, take a SOTA text-to-image model (here they
 
 <a href="https://www.youtube.com/watch?v=AcvmyqGgMh8">AI Coffee Break explanation</a>
 
+## Install
+
+```bash
+$ pip install make-a-video
+```
+
+## Usage
+
+Passing in video features
+
+```python
+import torch
+from make_a_video_pytorch import Pseudo3DConv, SpatioTemporalAttention
+
+conv = Pseudo3DConv(
+ dim = 256,
+ kernel_size = 3
+)
+
+attn = SpatioTemporalAttention(
+ dim = 256,
+ dim_head = 64,
+ heads = 8
+)
+
+video = torch.randn(1, 256, 8, 16, 16) # (batch, features, frames, height, width)
+
+conv_out = conv(video) # (1, 256, 8, 16, 16)
+attn_out = attn(video) # (1, 256, 8, 16, 16)
+```
+
+Passing in images (if one were to pretrain on images first, both temporal convolution and attention will be automatically skipped)
+
+```python
+import torch
+from make_a_video_pytorch import Pseudo3DConv, SpatioTemporalAttention
+
+conv = Pseudo3DConv(
+ dim = 256,
+ kernel_size = 3
+)
+
+attn = SpatioTemporalAttention(
+ dim = 256,
+ dim_head = 64,
+ heads = 8
+)
+
+images = torch.randn(1, 256, 16, 16) # (batch, features, height, width)
+
+conv_out = conv(images) # (1, 256, 16, 16)
+attn_out = attn(images) # (1, 256, 16, 16)
+```
+
+You can also control the two modules so that when fed 3-dimensional features, it only does training spatially
+
+```python
+import torch
+from make_a_video_pytorch import Pseudo3DConv, SpatioTemporalAttention
+
+conv = Pseudo3DConv(
+ dim = 256,
+ kernel_size = 3
+)
+
+attn = SpatioTemporalAttention(
+ dim = 256,
+ dim_head = 64,
+ heads = 8
+)
+
+video = torch.randn(1, 256, 8, 16, 16) # (batch, features, frames, height, width)
+
+# below it will not train across time
+
+conv_out = conv(video, convolve_across_time = False) # (1, 256, 8, 16, 16)
+attn_out = attn(video, attend_across_time = False) # (1, 256, 8, 16, 16)
+```
+
+## Todo
+
+- [ ] wire up <a href="https://github.com/lucidrains/dalle2-pytorch">dalle2-pytorch</a> unet with pseudo 3d convs + spatial temporal attention
+- [ ] give attention the best positional embeddings research has to offer
+- [ ] soup up the attention
+
 ## Citations
 
 ```bibtex

make_a_video_pytorch/__init__.py

@@ -0,0 +1 @@
+from make_a_video_pytorch.make_a_video import Pseudo3DConv, SpatioTemporalAttention

make_a_video_pytorch/make_a_video.py

@@ -0,0 +1,162 @@
+import torch
+from torch import nn, einsum
+from einops import rearrange
+
+# helper functions
+
+def exists(val):
+ return val is not None
+
+def default(val, d):
+ return val if exists(val) else d
+
+# layernorm 3d
+
+class LayerNorm(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.g = nn.Parameter(torch.ones(dim))
+
+ def forward(self, x):
+ eps = 1e-5 if x.dtype == torch.float32 else 1e-3
+ var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+ mean = torch.mean(x, dim = 1, keepdim = True)
+ return (x - mean) * (var + eps).rsqrt() * self.g
+
+# helper classes
+
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_head = 64,
+ heads = 8
+ ):
+ super().__init__()
+ self.heads = heads
+ self.scale = dim_head ** -0.5
+ inner_dim = dim_head * heads
+
+ self.norm = LayerNorm(dim)
+
+ self.to_q = nn.Linear(dim, inner_dim, bias = False)
+ self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
+ self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+ nn.init.zeros_(self.to_out.weight.data) # identity with skip connection
+
+ def forward(self, x):
+ x = self.norm(x)
+
+ q, k, v = self.to_q(x), *self.to_kv(x).chunk(2, dim = -1)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), (q, k, v))
+
+ q = q * self.scale
+
+ sim = einsum('b h i d, b h j d -> b h i j', q, k)
+
+ attn = sim.softmax(dim = -1)
+
+ out = einsum('b h i j, b h j d -> b h i d', attn, v)
+
+ out = rearrange(out, 'b h n d -> b n (h d)')
+ return self.to_out(out)
+
+# main contribution - pseudo 3d conv
+
+class Pseudo3DConv(nn.Module):
+ def __init__(
+ self,
+ dim,
+ *,
+ kernel_size,
+ dim_out = None,
+ temporal_kernel_size = None,
+ **kwargs
+ ):
+ super().__init__()
+ dim_out = default(dim_out, dim)
+ temporal_kernel_size = default(temporal_kernel_size, kernel_size)
+
+ self.spatial_conv = nn.Conv2d(dim, dim_out, kernel_size = kernel_size, padding = kernel_size // 2)
+ self.temporal_conv = nn.Conv1d(dim_out, dim_out, kernel_size = temporal_kernel_size, padding = temporal_kernel_size // 2)
+
+ nn.init.dirac_(self.temporal_conv.weight.data) # initialized to be identity
+ nn.init.zeros_(self.temporal_conv.bias.data)
+
+ def forward(
+ self,
+ x,
+ convolve_across_time = True
+ ):
+ b, c, *_, h, w = x.shape
+
+ is_video = x.ndim == 5
+ convolve_across_time &= is_video
+
+ if is_video:
+ x = rearrange(x, 'b c f h w -> (b f) c h w')
+
+ x = self.spatial_conv(x)
+
+ if is_video:
+ x = rearrange(x, '(b f) c h w -> b c f h w', b = b)
+
+ if not convolve_across_time:
+ return x
+
+ x = rearrange(x, 'b c f h w -> (b h w) c f')
+
+ x = self.temporal_conv(x)
+
+ x = rearrange(x, '(b h w) c f -> b c f h w', h = h, w = w)
+
+ return x
+
+# factorized spatial temporal attention from Ho et al.
+# todo - take care of relative positional biases + rotary embeddings
+
+class SpatioTemporalAttention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ *,
+ dim_head = 64,
+ heads = 8
+ ):
+ super().__init__()
+ self.spatial_attn = Attention(dim = dim, dim_head = dim_head, heads = heads)
+ self.temporal_attn = Attention(dim = dim, dim_head = dim_head, heads = heads)
+
+ def forward(
+ self,
+ x,
+ attend_across_time = True
+ ):
+ b, c, *_, h, w = x.shape
+ is_video = x.ndim == 5
+ attend_across_time &= is_video
+
+ if is_video:
+ x = rearrange(x, 'b c f h w -> (b f) (h w) c')
+ else:
+ x = rearrange(x, 'b c h w -> b (h w) c')
+
+ x = self.spatial_attn(x) + x
+
+ if is_video:
+ x = rearrange(x, '(b f) (h w) c -> b c f h w', b = b, h = h, w = w)
+ else:
+ x = rearrange(x, 'b (h w) c -> b c h w', h = h, w = w)
+
+ if not attend_across_time:
+ return x
+
+ x = rearrange(x, 'b c f h w -> (b h w) f c')
+
+ x = self.temporal_attn(x) + x
+
+ x = rearrange(x, '(b h w) f c -> b c f h w', w = w, h = h)
+
+ return x