277 lines
9.0 KiB
Python
277 lines
9.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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import torch
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import torch.nn as nn
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from fastvideo.layers.activation import get_act_fn
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from fastvideo.layers.linear import ReplicatedLinear
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from fastvideo.layers.mlp import MLP
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class PatchEmbed(nn.Module):
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"""2D Image to Patch Embedding
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Image to Patch Embedding using Conv2d
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A convolution based approach to patchifying a 2D image w/ embedding projection.
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Based on the impl in https://github.com/google-research/vision_transformer
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Hacked together by / Copyright 2020 Ross Wightman
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Remove the _assert function in forward function to be compatible with multi-resolution images.
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"""
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def __init__(self,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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norm_layer=None,
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flatten=True,
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bias=True,
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dtype=None,
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prefix: str = ""):
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super().__init__()
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# Convert patch_size to 2-tuple
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if isinstance(patch_size, list | tuple):
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if len(patch_size) == 1:
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patch_size = (patch_size[0], patch_size[0])
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else:
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patch_size = (patch_size, patch_size)
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self.patch_size = patch_size
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self.flatten = flatten
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self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x):
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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x = self.norm(x)
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return x
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class WanCamControlPatchEmbedding(nn.Module):
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"""Lingbot World Patch embedding for Plucker features."""
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def __init__(
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self,
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patch_size=(1, 2, 2),
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in_chans=384, # 6 * 64
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embed_dim=2048,
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bias=True,
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dtype=None,
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prefix: str = ""):
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super().__init__()
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# must be 3-tuple
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if isinstance(patch_size, list | tuple):
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if len(patch_size) != 3:
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raise ValueError(f"patch_size must have length 3, got {len(patch_size)}")
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else:
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raise ValueError(f"Unsupported patch_size type: {type(patch_size)}")
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self.patch_size = patch_size
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pt, ph, pw = self.patch_size
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self.in_features = in_chans * pt * ph * pw
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self.proj = nn.Linear(self.in_features, embed_dim, bias=bias, dtype=dtype)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if x.dim() != 5:
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raise ValueError(f"Expected camera embedding shape [B, C, F, H, W], got {x.shape}")
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bsz, channels, frames, height, width = x.shape
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pt, ph, pw = self.patch_size
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if (frames % pt) != 0 or (height % ph) != 0 or (width % pw) != 0:
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raise ValueError(f"Input shape {x.shape} must be divisible by patch_size {self.patch_size}")
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# '1 c (f c1) (h c2) (w c3) -> 1 (f h w) (c c1 c2 c3)',
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x = x.view(
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bsz,
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channels,
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frames // pt,
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pt,
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height // ph,
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ph,
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width // pw,
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pw,
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)
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x = x.permute(0, 2, 4, 6, 1, 3, 5, 7).reshape(bsz, -1, self.in_features)
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return self.proj(x)
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(
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self,
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hidden_size,
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act_layer="silu",
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frequency_embedding_size=256,
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max_period=10000,
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dtype=None,
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freq_dtype=torch.float32,
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prefix: str = "",
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):
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super().__init__()
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self.frequency_embedding_size = frequency_embedding_size
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self.max_period = max_period
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self.mlp = MLP(frequency_embedding_size, hidden_size, hidden_size, act_type=act_layer, dtype=dtype)
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self.freq_dtype = freq_dtype
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def forward(self, t: torch.Tensor, timestep_seq_len: int | None = None) -> torch.Tensor:
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t_freq = timestep_embedding(t, self.frequency_embedding_size, self.max_period,
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dtype=self.freq_dtype).to(self.mlp.fc_in.weight.dtype)
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if timestep_seq_len is not None:
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t_freq = t_freq.unflatten(0, (1, timestep_seq_len))
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# t_freq = t_freq.to(self.mlp.fc_in.weight.dtype)
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t_emb = self.mlp(t_freq)
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return t_emb
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def timestep_embedding(t: torch.Tensor,
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dim: int,
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max_period: int = 10000,
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dtype: torch.dtype = torch.float32) -> torch.Tensor:
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"""
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Create sinusoidal timestep embeddings.
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Args:
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t: Tensor of shape [B] with timesteps
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dim: Embedding dimension
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max_period: Controls the minimum frequency of the embeddings
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Returns:
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Tensor of shape [B, dim] with embeddings
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"""
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half = dim // 2
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=dtype) / half).to(device=t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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class ModulateProjection(nn.Module):
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"""Modulation layer for DiT blocks."""
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def __init__(
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self,
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hidden_size: int,
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factor: int = 2,
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act_layer: str = "silu",
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dtype: torch.dtype | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.factor = factor
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self.hidden_size = hidden_size
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self.linear = ReplicatedLinear(hidden_size, hidden_size * factor, bias=True, params_dtype=dtype)
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self.act = get_act_fn(act_layer)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.act(x)
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x, _ = self.linear(x)
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return x
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def unpatchify(x, t, h, w, patch_size, channels) -> torch.Tensor:
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"""
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Convert patched representation back to image space.
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Args:
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x: Tensor of shape [B, T*H*W, C*P_t*P_h*P_w]
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t, h, w: Temporal and spatial dimensions
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Returns:
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Unpatchified tensor of shape [B, C, T*P_t, H*P_h, W*P_w]
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"""
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assert x.ndim == 3, f"x.ndim: {x.ndim}"
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assert len(patch_size) == 3, f"patch_size: {patch_size}"
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assert t * h * w == x.shape[1], f"t * h * w: {t * h * w}, x.shape[1]: {x.shape[1]}"
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c = channels
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pt, ph, pw = patch_size
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x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw))
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x = torch.einsum("nthwcopq->nctohpwq", x)
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imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
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return imgs
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def get_timestep_embedding(
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timesteps: torch.Tensor,
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embedding_dim: int,
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flip_sin_to_cos: bool = False,
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downscale_freq_shift: float = 1,
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scale: float = 1,
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max_period: int = 10000,
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) -> torch.Tensor:
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"""
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This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
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Args
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timesteps (torch.Tensor):
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a 1-D Tensor of N indices, one per batch element. These may be fractional.
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embedding_dim (int):
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the dimension of the output.
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flip_sin_to_cos (bool):
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Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
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downscale_freq_shift (float):
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Controls the delta between frequencies between dimensions
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scale (float):
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Scaling factor applied to the embeddings.
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max_period (int):
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Controls the maximum frequency of the embeddings
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Returns
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torch.Tensor: an [N x dim] Tensor of positional embeddings.
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"""
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assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
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half_dim = embedding_dim // 2
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exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
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exponent = exponent / (half_dim - downscale_freq_shift)
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emb = torch.exp(exponent)
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emb = timesteps[:, None].float() * emb[None, :]
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# scale embeddings
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emb = scale * emb
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# concat sine and cosine embeddings
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
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# flip sine and cosine embeddings
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if flip_sin_to_cos:
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emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
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# zero pad
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if embedding_dim % 2 == 1:
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emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
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return emb
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class Timesteps(nn.Module):
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def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
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super().__init__()
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self.num_channels = num_channels
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self.flip_sin_to_cos = flip_sin_to_cos
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self.downscale_freq_shift = downscale_freq_shift
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self.scale = scale
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def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
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t_emb = get_timestep_embedding(
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timesteps,
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self.num_channels,
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flip_sin_to_cos=self.flip_sin_to_cos,
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downscale_freq_shift=self.downscale_freq_shift,
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scale=self.scale,
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)
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return t_emb
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