mirror of
https://github.com/Stability-AI/generative-models.git
synced 2025-12-20 14:54:21 +01:00
Stable Video Diffusion
This commit is contained in:
Tim Dockhorn
@@ -5,19 +5,9 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ....modules.distributions.distributions import DiagonalGaussianDistribution
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class AbstractRegularizer(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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raise NotImplementedError()
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@abstractmethod
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def get_trainable_parameters(self) -> Any:
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raise NotImplementedError()
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from ....modules.distributions.distributions import \
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DiagonalGaussianDistribution
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from .base import AbstractRegularizer
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class DiagonalGaussianRegularizer(AbstractRegularizer):
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@@ -39,15 +29,3 @@ class DiagonalGaussianRegularizer(AbstractRegularizer):
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kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
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log["kl_loss"] = kl_loss
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return z, log
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def measure_perplexity(predicted_indices, num_centroids):
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# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
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# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
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encodings = (
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F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
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)
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avg_probs = encodings.mean(0)
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perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
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cluster_use = torch.sum(avg_probs > 0)
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return perplexity, cluster_use
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40
sgm/modules/autoencoding/regularizers/base.py
Normal file
40
sgm/modules/autoencoding/regularizers/base.py
Normal file
@@ -0,0 +1,40 @@
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from abc import abstractmethod
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from typing import Any, Tuple
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import torch
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import torch.nn.functional as F
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from torch import nn
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class AbstractRegularizer(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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raise NotImplementedError()
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@abstractmethod
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def get_trainable_parameters(self) -> Any:
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raise NotImplementedError()
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class IdentityRegularizer(AbstractRegularizer):
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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return z, dict()
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def get_trainable_parameters(self) -> Any:
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yield from ()
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def measure_perplexity(
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predicted_indices: torch.Tensor, num_centroids: int
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
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# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
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encodings = (
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F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
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)
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avg_probs = encodings.mean(0)
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perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
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cluster_use = torch.sum(avg_probs > 0)
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return perplexity, cluster_use
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487
sgm/modules/autoencoding/regularizers/quantize.py
Normal file
487
sgm/modules/autoencoding/regularizers/quantize.py
Normal file
@@ -0,0 +1,487 @@
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import logging
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from abc import abstractmethod
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from typing import Dict, Iterator, Literal, Optional, Tuple, Union
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from torch import einsum
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from .base import AbstractRegularizer, measure_perplexity
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logpy = logging.getLogger(__name__)
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class AbstractQuantizer(AbstractRegularizer):
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def __init__(self):
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super().__init__()
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# Define these in your init
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# shape (N,)
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self.used: Optional[torch.Tensor]
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self.re_embed: int
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self.unknown_index: Union[Literal["random"], int]
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def remap_to_used(self, inds: torch.Tensor) -> torch.Tensor:
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assert self.used is not None, "You need to define used indices for remap"
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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match = (inds[:, :, None] == used[None, None, ...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2) < 1
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if self.unknown_index == "random":
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new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(
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device=new.device
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)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds: torch.Tensor) -> torch.Tensor:
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assert self.used is not None, "You need to define used indices for remap"
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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return back.reshape(ishape)
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@abstractmethod
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def get_codebook_entry(
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self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
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) -> torch.Tensor:
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raise NotImplementedError()
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def get_trainable_parameters(self) -> Iterator[torch.nn.Parameter]:
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yield from self.parameters()
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class GumbelQuantizer(AbstractQuantizer):
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"""
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credit to @karpathy:
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https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
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Gumbel Softmax trick quantizer
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Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
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https://arxiv.org/abs/1611.01144
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"""
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def __init__(
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self,
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num_hiddens: int,
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embedding_dim: int,
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n_embed: int,
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straight_through: bool = True,
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kl_weight: float = 5e-4,
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temp_init: float = 1.0,
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remap: Optional[str] = None,
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unknown_index: str = "random",
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loss_key: str = "loss/vq",
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) -> None:
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super().__init__()
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self.loss_key = loss_key
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self.embedding_dim = embedding_dim
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self.n_embed = n_embed
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self.straight_through = straight_through
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self.temperature = temp_init
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self.kl_weight = kl_weight
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self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
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self.embed = nn.Embedding(n_embed, embedding_dim)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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else:
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self.used = None
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self.re_embed = n_embed
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if unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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else:
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assert unknown_index == "random" or isinstance(
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unknown_index, int
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), "unknown index needs to be 'random', 'extra' or any integer"
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.remap is not None:
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logpy.info(
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f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices."
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)
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def forward(
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self, z: torch.Tensor, temp: Optional[float] = None, return_logits: bool = False
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) -> Tuple[torch.Tensor, Dict]:
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# force hard = True when we are in eval mode, as we must quantize.
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# actually, always true seems to work
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hard = self.straight_through if self.training else True
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temp = self.temperature if temp is None else temp
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out_dict = {}
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logits = self.proj(z)
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if self.remap is not None:
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# continue only with used logits
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full_zeros = torch.zeros_like(logits)
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logits = logits[:, self.used, ...]
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soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
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if self.remap is not None:
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# go back to all entries but unused set to zero
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full_zeros[:, self.used, ...] = soft_one_hot
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soft_one_hot = full_zeros
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z_q = einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
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# + kl divergence to the prior loss
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qy = F.softmax(logits, dim=1)
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diff = (
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self.kl_weight
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* torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
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)
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out_dict[self.loss_key] = diff
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ind = soft_one_hot.argmax(dim=1)
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out_dict["indices"] = ind
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if self.remap is not None:
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ind = self.remap_to_used(ind)
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if return_logits:
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out_dict["logits"] = logits
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return z_q, out_dict
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def get_codebook_entry(self, indices, shape):
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# TODO: shape not yet optional
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b, h, w, c = shape
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assert b * h * w == indices.shape[0]
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indices = rearrange(indices, "(b h w) -> b h w", b=b, h=h, w=w)
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if self.remap is not None:
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indices = self.unmap_to_all(indices)
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one_hot = (
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F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
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)
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z_q = einsum("b n h w, n d -> b d h w", one_hot, self.embed.weight)
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return z_q
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class VectorQuantizer(AbstractQuantizer):
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"""
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____________________________________________
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Discretization bottleneck part of the VQ-VAE.
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Inputs:
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- n_e : number of embeddings
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- e_dim : dimension of embedding
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- beta : commitment cost used in loss term,
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beta * ||z_e(x)-sg[e]||^2
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_____________________________________________
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"""
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def __init__(
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self,
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n_e: int,
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e_dim: int,
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beta: float = 0.25,
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remap: Optional[str] = None,
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unknown_index: str = "random",
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sane_index_shape: bool = False,
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log_perplexity: bool = False,
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embedding_weight_norm: bool = False,
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loss_key: str = "loss/vq",
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):
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super().__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.loss_key = loss_key
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if not embedding_weight_norm:
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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else:
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self.embedding = torch.nn.utils.weight_norm(
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nn.Embedding(self.n_e, self.e_dim), dim=1
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)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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else:
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self.used = None
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self.re_embed = n_e
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if unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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else:
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assert unknown_index == "random" or isinstance(
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unknown_index, int
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), "unknown index needs to be 'random', 'extra' or any integer"
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.remap is not None:
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logpy.info(
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f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices."
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)
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self.sane_index_shape = sane_index_shape
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self.log_perplexity = log_perplexity
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def forward(
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self,
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z: torch.Tensor,
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) -> Tuple[torch.Tensor, Dict]:
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do_reshape = z.ndim == 4
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if do_reshape:
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# # reshape z -> (batch, height, width, channel) and flatten
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z = rearrange(z, "b c h w -> b h w c").contiguous()
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else:
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assert z.ndim < 4, "No reshaping strategy for inputs > 4 dimensions defined"
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z = z.contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = (
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torch.sum(z_flattened**2, dim=1, keepdim=True)
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+ torch.sum(self.embedding.weight**2, dim=1)
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- 2
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* torch.einsum(
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"bd,dn->bn", z_flattened, rearrange(self.embedding.weight, "n d -> d n")
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)
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)
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min_encoding_indices = torch.argmin(d, dim=1)
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z_q = self.embedding(min_encoding_indices).view(z.shape)
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loss_dict = {}
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if self.log_perplexity:
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perplexity, cluster_usage = measure_perplexity(
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min_encoding_indices.detach(), self.n_e
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)
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loss_dict.update({"perplexity": perplexity, "cluster_usage": cluster_usage})
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# compute loss for embedding
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loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean(
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(z_q - z.detach()) ** 2
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)
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loss_dict[self.loss_key] = loss
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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if do_reshape:
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z_q = rearrange(z_q, "b h w c -> b c h w").contiguous()
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|
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if self.remap is not None:
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min_encoding_indices = min_encoding_indices.reshape(
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z.shape[0], -1
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) # add batch axis
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min_encoding_indices = self.remap_to_used(min_encoding_indices)
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min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
|
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|
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if self.sane_index_shape:
|
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if do_reshape:
|
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min_encoding_indices = min_encoding_indices.reshape(
|
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z_q.shape[0], z_q.shape[2], z_q.shape[3]
|
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)
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else:
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min_encoding_indices = rearrange(
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min_encoding_indices, "(b s) 1 -> b s", b=z_q.shape[0]
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)
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|
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loss_dict["min_encoding_indices"] = min_encoding_indices
|
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|
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return z_q, loss_dict
|
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|
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def get_codebook_entry(
|
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self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
|
||||
) -> torch.Tensor:
|
||||
# shape specifying (batch, height, width, channel)
|
||||
if self.remap is not None:
|
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assert shape is not None, "Need to give shape for remap"
|
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indices = indices.reshape(shape[0], -1) # add batch axis
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indices = self.unmap_to_all(indices)
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indices = indices.reshape(-1) # flatten again
|
||||
|
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# get quantized latent vectors
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z_q = self.embedding(indices)
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|
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if shape is not None:
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z_q = z_q.view(shape)
|
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# reshape back to match original input shape
|
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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|
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return z_q
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|
||||
|
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class EmbeddingEMA(nn.Module):
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def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
|
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super().__init__()
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self.decay = decay
|
||||
self.eps = eps
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weight = torch.randn(num_tokens, codebook_dim)
|
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self.weight = nn.Parameter(weight, requires_grad=False)
|
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self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False)
|
||||
self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False)
|
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self.update = True
|
||||
|
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def forward(self, embed_id):
|
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return F.embedding(embed_id, self.weight)
|
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|
||||
def cluster_size_ema_update(self, new_cluster_size):
|
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self.cluster_size.data.mul_(self.decay).add_(
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new_cluster_size, alpha=1 - self.decay
|
||||
)
|
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|
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def embed_avg_ema_update(self, new_embed_avg):
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self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
|
||||
|
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def weight_update(self, num_tokens):
|
||||
n = self.cluster_size.sum()
|
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smoothed_cluster_size = (
|
||||
(self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
|
||||
)
|
||||
# normalize embedding average with smoothed cluster size
|
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embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
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self.weight.data.copy_(embed_normalized)
|
||||
|
||||
|
||||
class EMAVectorQuantizer(AbstractQuantizer):
|
||||
def __init__(
|
||||
self,
|
||||
n_embed: int,
|
||||
embedding_dim: int,
|
||||
beta: float,
|
||||
decay: float = 0.99,
|
||||
eps: float = 1e-5,
|
||||
remap: Optional[str] = None,
|
||||
unknown_index: str = "random",
|
||||
loss_key: str = "loss/vq",
|
||||
):
|
||||
super().__init__()
|
||||
self.codebook_dim = embedding_dim
|
||||
self.num_tokens = n_embed
|
||||
self.beta = beta
|
||||
self.loss_key = loss_key
|
||||
|
||||
self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
|
||||
|
||||
self.remap = remap
|
||||
if self.remap is not None:
|
||||
self.register_buffer("used", torch.tensor(np.load(self.remap)))
|
||||
self.re_embed = self.used.shape[0]
|
||||
else:
|
||||
self.used = None
|
||||
self.re_embed = n_embed
|
||||
if unknown_index == "extra":
|
||||
self.unknown_index = self.re_embed
|
||||
self.re_embed = self.re_embed + 1
|
||||
else:
|
||||
assert unknown_index == "random" or isinstance(
|
||||
unknown_index, int
|
||||
), "unknown index needs to be 'random', 'extra' or any integer"
|
||||
self.unknown_index = unknown_index # "random" or "extra" or integer
|
||||
if self.remap is not None:
|
||||
logpy.info(
|
||||
f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
|
||||
f"Using {self.unknown_index} for unknown indices."
|
||||
)
|
||||
|
||||
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
|
||||
# reshape z -> (batch, height, width, channel) and flatten
|
||||
# z, 'b c h w -> b h w c'
|
||||
z = rearrange(z, "b c h w -> b h w c")
|
||||
z_flattened = z.reshape(-1, self.codebook_dim)
|
||||
|
||||
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
||||
d = (
|
||||
z_flattened.pow(2).sum(dim=1, keepdim=True)
|
||||
+ self.embedding.weight.pow(2).sum(dim=1)
|
||||
- 2 * torch.einsum("bd,nd->bn", z_flattened, self.embedding.weight)
|
||||
) # 'n d -> d n'
|
||||
|
||||
encoding_indices = torch.argmin(d, dim=1)
|
||||
|
||||
z_q = self.embedding(encoding_indices).view(z.shape)
|
||||
encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
|
||||
avg_probs = torch.mean(encodings, dim=0)
|
||||
perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
|
||||
|
||||
if self.training and self.embedding.update:
|
||||
# EMA cluster size
|
||||
encodings_sum = encodings.sum(0)
|
||||
self.embedding.cluster_size_ema_update(encodings_sum)
|
||||
# EMA embedding average
|
||||
embed_sum = encodings.transpose(0, 1) @ z_flattened
|
||||
self.embedding.embed_avg_ema_update(embed_sum)
|
||||
# normalize embed_avg and update weight
|
||||
self.embedding.weight_update(self.num_tokens)
|
||||
|
||||
# compute loss for embedding
|
||||
loss = self.beta * F.mse_loss(z_q.detach(), z)
|
||||
|
||||
# preserve gradients
|
||||
z_q = z + (z_q - z).detach()
|
||||
|
||||
# reshape back to match original input shape
|
||||
# z_q, 'b h w c -> b c h w'
|
||||
z_q = rearrange(z_q, "b h w c -> b c h w")
|
||||
|
||||
out_dict = {
|
||||
self.loss_key: loss,
|
||||
"encodings": encodings,
|
||||
"encoding_indices": encoding_indices,
|
||||
"perplexity": perplexity,
|
||||
}
|
||||
|
||||
return z_q, out_dict
|
||||
|
||||
|
||||
class VectorQuantizerWithInputProjection(VectorQuantizer):
|
||||
def __init__(
|
||||
self,
|
||||
input_dim: int,
|
||||
n_codes: int,
|
||||
codebook_dim: int,
|
||||
beta: float = 1.0,
|
||||
output_dim: Optional[int] = None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(n_codes, codebook_dim, beta, **kwargs)
|
||||
self.proj_in = nn.Linear(input_dim, codebook_dim)
|
||||
self.output_dim = output_dim
|
||||
if output_dim is not None:
|
||||
self.proj_out = nn.Linear(codebook_dim, output_dim)
|
||||
else:
|
||||
self.proj_out = nn.Identity()
|
||||
|
||||
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
|
||||
rearr = False
|
||||
in_shape = z.shape
|
||||
|
||||
if z.ndim > 3:
|
||||
rearr = self.output_dim is not None
|
||||
z = rearrange(z, "b c ... -> b (...) c")
|
||||
z = self.proj_in(z)
|
||||
z_q, loss_dict = super().forward(z)
|
||||
|
||||
z_q = self.proj_out(z_q)
|
||||
if rearr:
|
||||
if len(in_shape) == 4:
|
||||
z_q = rearrange(z_q, "b (h w) c -> b c h w ", w=in_shape[-1])
|
||||
elif len(in_shape) == 5:
|
||||
z_q = rearrange(
|
||||
z_q, "b (t h w) c -> b c t h w ", w=in_shape[-1], h=in_shape[-2]
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"rearranging not available for {len(in_shape)}-dimensional input."
|
||||
)
|
||||
|
||||
return z_q, loss_dict
|
||||
Reference in New Issue
Block a user