mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
1072 lines
35 KiB
Python
1072 lines
35 KiB
Python
"""Classes for image and text encoding"""
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import math
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from contextlib import nullcontext
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from functools import partial
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from typing import Dict, List, Optional, Tuple, Union
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import kornia
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import numpy as np
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import open_clip
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import torch
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import torch.nn as nn
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from einops import rearrange, repeat
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from omegaconf import ListConfig
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from torch.utils.checkpoint import checkpoint
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from transformers import (
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ByT5Tokenizer,
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CLIPTextModel,
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CLIPTokenizer,
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T5EncoderModel,
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T5Tokenizer,
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)
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from imaginairy.modules.sgm.autoencoding.regularizers import DiagonalGaussianRegularizer
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from imaginairy.modules.sgm.diffusionmodules.model import Encoder
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from imaginairy.modules.sgm.diffusionmodules.openaimodel import Timestep
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from imaginairy.modules.sgm.diffusionmodules.util import (
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extract_into_tensor,
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make_beta_schedule,
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)
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from imaginairy.modules.sgm.distributions.distributions import (
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DiagonalGaussianDistribution,
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)
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from imaginairy.utils import (
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default,
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disabled_train,
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expand_dims_like,
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get_device,
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instantiate_from_config,
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platform_appropriate_autocast,
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)
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from imaginairy.vendored.k_diffusion.utils import append_dims
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# from ..util import (append_dims, autocast, count_params, default,
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# disabled_train, expand_dims_like, instantiate_from_config)
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class AbstractEmbModel(nn.Module):
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def __init__(self):
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super().__init__()
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self._is_trainable = None
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self._ucg_rate = None
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self._input_key = None
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@property
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def is_trainable(self) -> bool:
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return self._is_trainable
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@property
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def ucg_rate(self) -> Union[float, torch.Tensor]:
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return self._ucg_rate
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@property
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def input_key(self) -> str:
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return self._input_key
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@is_trainable.setter
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def is_trainable(self, value: bool):
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self._is_trainable = value
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@ucg_rate.setter
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def ucg_rate(self, value: Union[float, torch.Tensor]):
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self._ucg_rate = value
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@input_key.setter
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def input_key(self, value: str):
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self._input_key = value
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@is_trainable.deleter
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def is_trainable(self):
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del self._is_trainable
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@ucg_rate.deleter
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def ucg_rate(self):
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del self._ucg_rate
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@input_key.deleter
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def input_key(self):
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del self._input_key
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class GeneralConditioner(nn.Module):
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OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
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KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}
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def __init__(self, emb_models: Union[List, ListConfig]):
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super().__init__()
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embedders = []
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for n, embconfig in enumerate(emb_models):
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embedder = instantiate_from_config(embconfig)
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assert isinstance(
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embedder, AbstractEmbModel
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), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
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embedder.is_trainable = embconfig.get("is_trainable", False)
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embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
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if not embedder.is_trainable:
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embedder.train = disabled_train
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for param in embedder.parameters():
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param.requires_grad = False
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embedder.eval()
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# print(
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# f"Initialized embedder #{n}: {embedder.__class__.__name__} "
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# f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
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# )
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if "input_key" in embconfig:
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embedder.input_key = embconfig["input_key"]
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elif "input_keys" in embconfig:
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embedder.input_keys = embconfig["input_keys"]
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else:
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msg = f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
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raise KeyError(msg)
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embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
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if embedder.legacy_ucg_val is not None:
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embedder.ucg_prng = np.random.RandomState()
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embedders.append(embedder)
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self.embedders = nn.ModuleList(embedders)
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def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict:
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assert embedder.legacy_ucg_val is not None
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p = embedder.ucg_rate
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val = embedder.legacy_ucg_val
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for i in range(len(batch[embedder.input_key])):
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if embedder.ucg_prng.choice(2, p=[1 - p, p]):
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batch[embedder.input_key][i] = val
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return batch
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def forward(
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self, batch: Dict, force_zero_embeddings: Optional[List] = None
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) -> Dict:
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output = {}
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if force_zero_embeddings is None:
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force_zero_embeddings = []
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for embedder in self.embedders:
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embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
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with embedding_context():
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if hasattr(embedder, "input_key") and (embedder.input_key is not None):
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if embedder.legacy_ucg_val is not None:
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batch = self.possibly_get_ucg_val(embedder, batch)
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emb_out = embedder(batch[embedder.input_key])
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elif hasattr(embedder, "input_keys"):
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emb_out = embedder(*[batch[k] for k in embedder.input_keys])
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assert isinstance(
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emb_out, (torch.Tensor, list, tuple)
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), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
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if not isinstance(emb_out, (list, tuple)):
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emb_out = [emb_out]
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for emb in emb_out:
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out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
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if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
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emb = (
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expand_dims_like(
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torch.bernoulli(
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(1.0 - embedder.ucg_rate)
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* torch.ones(emb.shape[0], device=emb.device)
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),
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emb,
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)
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* emb
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)
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if (
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hasattr(embedder, "input_key")
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and embedder.input_key in force_zero_embeddings
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):
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emb = torch.zeros_like(emb)
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if out_key in output:
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output[out_key] = torch.cat(
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(output[out_key], emb), self.KEY2CATDIM[out_key]
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)
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else:
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output[out_key] = emb
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return output
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def get_unconditional_conditioning(
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self,
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batch_c: Dict,
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batch_uc: Optional[Dict] = None,
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force_uc_zero_embeddings: Optional[List[str]] = None,
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force_cond_zero_embeddings: Optional[List[str]] = None,
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):
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if force_uc_zero_embeddings is None:
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force_uc_zero_embeddings = []
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ucg_rates = []
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for embedder in self.embedders:
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ucg_rates.append(embedder.ucg_rate)
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embedder.ucg_rate = 0.0
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c = self(batch_c, force_cond_zero_embeddings)
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uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings)
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for embedder, rate in zip(self.embedders, ucg_rates):
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embedder.ucg_rate = rate
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return c, uc
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class InceptionV3(nn.Module):
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"""Wrapper around the https://github.com/mseitzer/pytorch-fid inception
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port with an additional squeeze at the end"""
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def __init__(self, normalize_input=False, **kwargs):
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super().__init__()
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from pytorch_fid import inception
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kwargs["resize_input"] = True
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self.model = inception.InceptionV3(normalize_input=normalize_input, **kwargs)
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def forward(self, inp):
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outp = self.model(inp)
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if len(outp) == 1:
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return outp[0].squeeze()
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return outp
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class IdentityEncoder(AbstractEmbModel):
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def encode(self, x):
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return x
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def forward(self, x):
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return x
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class ClassEmbedder(AbstractEmbModel):
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def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
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super().__init__()
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self.embedding = nn.Embedding(n_classes, embed_dim)
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self.n_classes = n_classes
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self.add_sequence_dim = add_sequence_dim
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def forward(self, c):
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c = self.embedding(c)
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if self.add_sequence_dim:
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c = c[:, None, :]
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return c
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def get_unconditional_conditioning(self, bs, device=None):
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device = default(device, get_device)
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uc_class = (
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self.n_classes - 1
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) # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
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uc = torch.ones((bs,), device=device) * uc_class
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uc = {self.key: uc.long()}
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return uc
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class ClassEmbedderForMultiCond(ClassEmbedder):
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def forward(self, batch, key=None, disable_dropout=False):
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out = batch
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key = default(key, self.key)
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islist = isinstance(batch[key], list)
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if islist:
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batch[key] = batch[key][0]
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c_out = super().forward(batch, key, disable_dropout)
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out[key] = [c_out] if islist else c_out
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return out
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class FrozenT5Embedder(AbstractEmbModel):
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"""Uses the T5 transformer encoder for text"""
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def __init__(
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self, version="google/t5-v1_1-xxl", device=None, max_length=77, freeze=True
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): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
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super().__init__()
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device = default(device, get_device)
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self.tokenizer = T5Tokenizer.from_pretrained(version)
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self.transformer = T5EncoderModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt",
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)
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tokens = batch_encoding["input_ids"].to(self.device)
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with platform_appropriate_autocast(enabled=False):
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenByT5Embedder(AbstractEmbModel):
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"""
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Uses the ByT5 transformer encoder for text. Is character-aware.
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"""
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def __init__(
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self, version="google/byt5-base", device=None, max_length=77, freeze=True
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): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
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super().__init__()
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device = default(device, get_device)
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self.tokenizer = ByT5Tokenizer.from_pretrained(version)
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self.transformer = T5EncoderModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt",
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)
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tokens = batch_encoding["input_ids"].to(self.device)
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with platform_appropriate_autocast(enabled=False):
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenCLIPEmbedder(AbstractEmbModel):
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"""Uses the CLIP transformer encoder for text (from huggingface)"""
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LAYERS = ["last", "pooled", "hidden"]
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def __init__(
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self,
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version="openai/clip-vit-large-patch14",
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device=None,
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max_length=77,
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freeze=True,
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layer="last",
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layer_idx=None,
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always_return_pooled=False,
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): # clip-vit-base-patch32
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super().__init__()
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assert layer in self.LAYERS
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device = default(device, get_device)
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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self.layer_idx = layer_idx
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self.return_pooled = always_return_pooled
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if layer == "hidden":
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assert layer_idx is not None
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assert 0 <= abs(layer_idx) <= 12
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def freeze(self):
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self.transformer = self.transformer.eval()
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for param in self.parameters():
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param.requires_grad = False
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@platform_appropriate_autocast
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def forward(self, text):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt",
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)
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(
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input_ids=tokens, output_hidden_states=self.layer == "hidden"
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)
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if self.layer == "last":
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z = outputs.last_hidden_state
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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if self.return_pooled:
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return z, outputs.pooler_output
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return z
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def encode(self, text):
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return self(text)
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class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
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"""
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Uses the OpenCLIP transformer encoder for text
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"""
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LAYERS = ["pooled", "last", "penultimate"]
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def __init__(
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self,
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arch="ViT-H-14",
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version="laion2b_s32b_b79k",
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device=None,
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max_length=77,
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freeze=True,
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layer="last",
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always_return_pooled=False,
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legacy=True,
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):
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super().__init__()
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assert layer in self.LAYERS
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device = default(device, get_device)
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model, _, _ = open_clip.create_model_and_transforms(
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arch,
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device=torch.device("cpu"),
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pretrained=version,
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)
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del model.visual
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self.model = model
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self.device = device
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self.max_length = max_length
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self.return_pooled = always_return_pooled
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "last":
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self.layer_idx = 0
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elif self.layer == "penultimate":
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self.layer_idx = 1
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else:
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raise NotImplementedError()
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self.legacy = legacy
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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@platform_appropriate_autocast
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def forward(self, text):
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tokens = open_clip.tokenize(text)
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z = self.encode_with_transformer(tokens.to(self.device))
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if not self.return_pooled and self.legacy:
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return z
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if self.return_pooled:
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assert not self.legacy
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return z[self.layer], z["pooled"]
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return z[self.layer]
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def encode_with_transformer(self, text):
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x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
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x = x + self.model.positional_embedding
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
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if self.legacy:
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x = x[self.layer]
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x = self.model.ln_final(x)
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return x
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else:
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# x is a dict and will stay a dict
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o = x["last"]
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o = self.model.ln_final(o)
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pooled = self.pool(o, text)
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x["pooled"] = pooled
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return x
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def pool(self, x, text):
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# take features from the eot embedding (eot_token is the highest number in each sequence)
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x = (
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x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
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@ self.model.text_projection
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)
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return x
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def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
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outputs = {}
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for i, r in enumerate(self.model.transformer.resblocks):
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if i == len(self.model.transformer.resblocks) - 1:
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outputs["penultimate"] = x.permute(1, 0, 2) # LND -> NLD
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if (
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self.model.transformer.grad_checkpointing
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and not torch.jit.is_scripting()
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):
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x = checkpoint(r, x, attn_mask)
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else:
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x = r(x, attn_mask=attn_mask)
|
|
outputs["last"] = x.permute(1, 0, 2) # LND -> NLD
|
|
return outputs
|
|
|
|
def encode(self, text):
|
|
return self(text)
|
|
|
|
|
|
class FrozenOpenCLIPEmbedder(AbstractEmbModel):
|
|
LAYERS = [
|
|
# "pooled",
|
|
"last",
|
|
"penultimate",
|
|
]
|
|
|
|
def __init__(
|
|
self,
|
|
arch="ViT-H-14",
|
|
version="laion2b_s32b_b79k",
|
|
device=None,
|
|
max_length=77,
|
|
freeze=True,
|
|
layer="last",
|
|
):
|
|
super().__init__()
|
|
device = default(device, get_device)
|
|
assert layer in self.LAYERS
|
|
model, _, _ = open_clip.create_model_and_transforms(
|
|
arch, device=torch.device("cpu"), pretrained=version
|
|
)
|
|
del model.visual
|
|
self.model = model
|
|
|
|
self.device = device
|
|
self.max_length = max_length
|
|
if freeze:
|
|
self.freeze()
|
|
self.layer = layer
|
|
if self.layer == "last":
|
|
self.layer_idx = 0
|
|
elif self.layer == "penultimate":
|
|
self.layer_idx = 1
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
def freeze(self):
|
|
self.model = self.model.eval()
|
|
for param in self.parameters():
|
|
param.requires_grad = False
|
|
|
|
def forward(self, text):
|
|
tokens = open_clip.tokenize(text)
|
|
z = self.encode_with_transformer(tokens.to(self.device))
|
|
return z
|
|
|
|
def encode_with_transformer(self, text):
|
|
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
|
|
x = x + self.model.positional_embedding
|
|
x = x.permute(1, 0, 2) # NLD -> LND
|
|
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
|
|
x = x.permute(1, 0, 2) # LND -> NLD
|
|
x = self.model.ln_final(x)
|
|
return x
|
|
|
|
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
|
|
for i, r in enumerate(self.model.transformer.resblocks):
|
|
if i == len(self.model.transformer.resblocks) - self.layer_idx:
|
|
break
|
|
if (
|
|
self.model.transformer.grad_checkpointing
|
|
and not torch.jit.is_scripting()
|
|
):
|
|
x = checkpoint(r, x, attn_mask)
|
|
else:
|
|
x = r(x, attn_mask=attn_mask)
|
|
return x
|
|
|
|
def encode(self, text):
|
|
return self(text)
|
|
|
|
|
|
class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
|
|
"""
|
|
Uses the OpenCLIP vision transformer encoder for images
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
arch="ViT-H-14",
|
|
version="laion2b_s32b_b79k",
|
|
device=None,
|
|
max_length=77,
|
|
freeze=True,
|
|
antialias=True,
|
|
ucg_rate=0.0,
|
|
unsqueeze_dim=False,
|
|
repeat_to_max_len=False,
|
|
num_image_crops=0,
|
|
output_tokens=False,
|
|
init_device=None,
|
|
):
|
|
device = default(device, get_device)
|
|
super().__init__()
|
|
model, _, _ = open_clip.create_model_and_transforms(
|
|
arch,
|
|
device=torch.device(default(init_device, "cpu")),
|
|
pretrained=version,
|
|
)
|
|
del model.transformer
|
|
self.model = model
|
|
self.max_crops = num_image_crops
|
|
self.pad_to_max_len = self.max_crops > 0
|
|
self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
|
|
self.device = device
|
|
self.max_length = max_length
|
|
if freeze:
|
|
self.freeze()
|
|
|
|
self.antialias = antialias
|
|
|
|
self.register_buffer(
|
|
"mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
|
|
)
|
|
self.register_buffer(
|
|
"std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
|
|
)
|
|
self.ucg_rate = ucg_rate
|
|
self.unsqueeze_dim = unsqueeze_dim
|
|
self.stored_batch = None
|
|
self.model.visual.output_tokens = output_tokens
|
|
self.output_tokens = output_tokens
|
|
|
|
def preprocess(self, x):
|
|
# normalize to [0,1]
|
|
x = kornia.geometry.resize(
|
|
x,
|
|
(224, 224),
|
|
interpolation="bicubic",
|
|
align_corners=True,
|
|
antialias=self.antialias,
|
|
)
|
|
x = (x + 1.0) / 2.0
|
|
# renormalize according to clip
|
|
x = kornia.enhance.normalize(x, self.mean, self.std)
|
|
return x
|
|
|
|
def freeze(self):
|
|
self.model = self.model.eval()
|
|
for param in self.parameters():
|
|
param.requires_grad = False
|
|
|
|
@platform_appropriate_autocast()
|
|
def forward(self, image, no_dropout=False):
|
|
z = self.encode_with_vision_transformer(image)
|
|
tokens = None
|
|
if self.output_tokens:
|
|
z, tokens = z[0], z[1]
|
|
z = z.to(image.dtype)
|
|
if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
|
|
z = (
|
|
torch.bernoulli(
|
|
(1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
|
|
)[:, None]
|
|
* z
|
|
)
|
|
if tokens is not None:
|
|
tokens = (
|
|
expand_dims_like(
|
|
torch.bernoulli(
|
|
(1.0 - self.ucg_rate)
|
|
* torch.ones(tokens.shape[0], device=tokens.device)
|
|
),
|
|
tokens,
|
|
)
|
|
* tokens
|
|
)
|
|
if self.unsqueeze_dim:
|
|
z = z[:, None, :]
|
|
if self.output_tokens:
|
|
assert not self.repeat_to_max_len
|
|
assert not self.pad_to_max_len
|
|
return tokens, z
|
|
if self.repeat_to_max_len:
|
|
z_ = z[:, None, :] if z.dim() == 2 else z
|
|
return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
|
|
elif self.pad_to_max_len:
|
|
assert z.dim() == 3
|
|
z_pad = torch.cat(
|
|
(
|
|
z,
|
|
torch.zeros(
|
|
z.shape[0],
|
|
self.max_length - z.shape[1],
|
|
z.shape[2],
|
|
device=z.device,
|
|
),
|
|
),
|
|
1,
|
|
)
|
|
return z_pad, z_pad[:, 0, ...]
|
|
return z
|
|
|
|
def encode_with_vision_transformer(self, img):
|
|
# if self.max_crops > 0:
|
|
# img = self.preprocess_by_cropping(img)
|
|
if img.dim() == 5:
|
|
assert self.max_crops == img.shape[1]
|
|
img = rearrange(img, "b n c h w -> (b n) c h w")
|
|
img = self.preprocess(img)
|
|
if not self.output_tokens:
|
|
assert not self.model.visual.output_tokens
|
|
x = self.model.visual(img)
|
|
tokens = None
|
|
else:
|
|
assert self.model.visual.output_tokens
|
|
x, tokens = self.model.visual(img)
|
|
if self.max_crops > 0:
|
|
x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
|
|
# drop out between 0 and all along the sequence axis
|
|
x = (
|
|
torch.bernoulli(
|
|
(1.0 - self.ucg_rate)
|
|
* torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
|
|
)
|
|
* x
|
|
)
|
|
if tokens is not None:
|
|
tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
|
|
print(
|
|
f"You are running very experimental token-concat in {self.__class__.__name__}. "
|
|
f"Check what you are doing, and then remove this message."
|
|
)
|
|
if self.output_tokens:
|
|
return x, tokens
|
|
return x
|
|
|
|
def encode(self, text):
|
|
return self(text)
|
|
|
|
|
|
class FrozenCLIPT5Encoder(AbstractEmbModel):
|
|
def __init__(
|
|
self,
|
|
clip_version="openai/clip-vit-large-patch14",
|
|
t5_version="google/t5-v1_1-xl",
|
|
device=None,
|
|
clip_max_length=77,
|
|
t5_max_length=77,
|
|
):
|
|
device = default(device, get_device)
|
|
super().__init__()
|
|
self.clip_encoder = FrozenCLIPEmbedder(
|
|
clip_version, device, max_length=clip_max_length
|
|
)
|
|
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
|
|
# print(
|
|
# f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
|
|
# f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
|
|
# )
|
|
|
|
def encode(self, text):
|
|
return self(text)
|
|
|
|
def forward(self, text):
|
|
clip_z = self.clip_encoder.encode(text)
|
|
t5_z = self.t5_encoder.encode(text)
|
|
return [clip_z, t5_z]
|
|
|
|
|
|
class SpatialRescaler(nn.Module):
|
|
def __init__(
|
|
self,
|
|
n_stages=1,
|
|
method="bilinear",
|
|
multiplier=0.5,
|
|
in_channels=3,
|
|
out_channels=None,
|
|
bias=False,
|
|
wrap_video=False,
|
|
kernel_size=1,
|
|
remap_output=False,
|
|
):
|
|
super().__init__()
|
|
self.n_stages = n_stages
|
|
assert self.n_stages >= 0
|
|
assert method in [
|
|
"nearest",
|
|
"linear",
|
|
"bilinear",
|
|
"trilinear",
|
|
"bicubic",
|
|
"area",
|
|
]
|
|
self.multiplier = multiplier
|
|
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
|
|
self.remap_output = out_channels is not None or remap_output
|
|
if self.remap_output:
|
|
print(
|
|
f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
|
|
)
|
|
self.channel_mapper = nn.Conv2d(
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=kernel_size,
|
|
bias=bias,
|
|
padding=kernel_size // 2,
|
|
)
|
|
self.wrap_video = wrap_video
|
|
|
|
def forward(self, x):
|
|
if self.wrap_video and x.ndim == 5:
|
|
B, C, T, H, W = x.shape
|
|
x = rearrange(x, "b c t h w -> b t c h w")
|
|
x = rearrange(x, "b t c h w -> (b t) c h w")
|
|
|
|
for stage in range(self.n_stages):
|
|
x = self.interpolator(x, scale_factor=self.multiplier)
|
|
|
|
if self.wrap_video:
|
|
x = rearrange(x, "(b t) c h w -> b t c h w", b=B, t=T, c=C)
|
|
x = rearrange(x, "b t c h w -> b c t h w")
|
|
if self.remap_output:
|
|
x = self.channel_mapper(x)
|
|
return x
|
|
|
|
def encode(self, x):
|
|
return self(x)
|
|
|
|
|
|
class LowScaleEncoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
model_config,
|
|
linear_start,
|
|
linear_end,
|
|
timesteps=1000,
|
|
max_noise_level=250,
|
|
output_size=64,
|
|
scale_factor=1.0,
|
|
):
|
|
super().__init__()
|
|
self.max_noise_level = max_noise_level
|
|
self.model = instantiate_from_config(model_config)
|
|
self.augmentation_schedule = self.register_schedule(
|
|
timesteps=timesteps, linear_start=linear_start, linear_end=linear_end
|
|
)
|
|
self.out_size = output_size
|
|
self.scale_factor = scale_factor
|
|
|
|
def register_schedule(
|
|
self,
|
|
beta_schedule="linear",
|
|
timesteps=1000,
|
|
linear_start=1e-4,
|
|
linear_end=2e-2,
|
|
cosine_s=8e-3,
|
|
):
|
|
betas = make_beta_schedule(
|
|
beta_schedule,
|
|
timesteps,
|
|
linear_start=linear_start,
|
|
linear_end=linear_end,
|
|
cosine_s=cosine_s,
|
|
)
|
|
alphas = 1.0 - betas
|
|
alphas_cumprod = np.cumprod(alphas, axis=0)
|
|
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
|
|
|
|
(timesteps,) = betas.shape
|
|
self.num_timesteps = int(timesteps)
|
|
self.linear_start = linear_start
|
|
self.linear_end = linear_end
|
|
assert (
|
|
alphas_cumprod.shape[0] == self.num_timesteps
|
|
), "alphas have to be defined for each timestep"
|
|
|
|
to_torch = partial(torch.tensor, dtype=torch.float32)
|
|
|
|
self.register_buffer("betas", to_torch(betas))
|
|
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
|
|
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
|
|
|
|
# calculations for diffusion q(x_t | x_{t-1}) and others
|
|
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
|
|
self.register_buffer(
|
|
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
|
|
)
|
|
self.register_buffer(
|
|
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
|
|
)
|
|
self.register_buffer(
|
|
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
|
|
)
|
|
self.register_buffer(
|
|
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
|
|
)
|
|
|
|
def q_sample(self, x_start, t, noise=None):
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
return (
|
|
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
|
|
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
|
|
* noise
|
|
)
|
|
|
|
def forward(self, x):
|
|
z = self.model.encode(x)
|
|
if isinstance(z, DiagonalGaussianDistribution):
|
|
z = z.sample()
|
|
z = z * self.scale_factor
|
|
noise_level = torch.randint(
|
|
0, self.max_noise_level, (x.shape[0],), device=x.device
|
|
).long()
|
|
z = self.q_sample(z, noise_level)
|
|
if self.out_size is not None:
|
|
z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")
|
|
return z, noise_level
|
|
|
|
def decode(self, z):
|
|
z = z / self.scale_factor
|
|
return self.model.decode(z)
|
|
|
|
|
|
class ConcatTimestepEmbedderND(AbstractEmbModel):
|
|
"""embeds each dimension independently and concatenates them"""
|
|
|
|
def __init__(self, outdim):
|
|
super().__init__()
|
|
self.timestep = Timestep(outdim)
|
|
self.outdim = outdim
|
|
|
|
def forward(self, x):
|
|
if x.ndim == 1:
|
|
x = x[:, None]
|
|
assert len(x.shape) == 2
|
|
b, dims = x.shape[0], x.shape[1]
|
|
x = rearrange(x, "b d -> (b d)")
|
|
emb = self.timestep(x)
|
|
emb = rearrange(emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
|
|
return emb
|
|
|
|
|
|
class GaussianEncoder(Encoder, AbstractEmbModel):
|
|
def __init__(
|
|
self, weight: float = 1.0, flatten_output: bool = True, *args, **kwargs
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
self.posterior = DiagonalGaussianRegularizer()
|
|
self.weight = weight
|
|
self.flatten_output = flatten_output
|
|
|
|
def forward(self, x) -> Tuple[Dict, torch.Tensor]:
|
|
z = super().forward(x)
|
|
z, log = self.posterior(z)
|
|
log["loss"] = log["kl_loss"]
|
|
log["weight"] = self.weight
|
|
if self.flatten_output:
|
|
z = rearrange(z, "b c h w -> b (h w ) c")
|
|
return log, z
|
|
|
|
|
|
class VideoPredictionEmbedderWithEncoder(AbstractEmbModel):
|
|
def __init__(
|
|
self,
|
|
n_cond_frames: int,
|
|
n_copies: int,
|
|
encoder_config: dict,
|
|
sigma_sampler_config: Optional[dict] = None,
|
|
sigma_cond_config: Optional[dict] = None,
|
|
is_ae: bool = False,
|
|
scale_factor: float = 1.0,
|
|
disable_encoder_autocast: bool = False,
|
|
en_and_decode_n_samples_a_time: Optional[int] = None,
|
|
):
|
|
super().__init__()
|
|
|
|
self.n_cond_frames = n_cond_frames
|
|
self.n_copies = n_copies
|
|
self.encoder = instantiate_from_config(encoder_config)
|
|
self.sigma_sampler = (
|
|
instantiate_from_config(sigma_sampler_config)
|
|
if sigma_sampler_config is not None
|
|
else None
|
|
)
|
|
self.sigma_cond = (
|
|
instantiate_from_config(sigma_cond_config)
|
|
if sigma_cond_config is not None
|
|
else None
|
|
)
|
|
self.is_ae = is_ae
|
|
self.scale_factor = scale_factor
|
|
self.disable_encoder_autocast = disable_encoder_autocast
|
|
self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
|
|
|
|
def forward(
|
|
self, vid: torch.Tensor
|
|
) -> Union[
|
|
torch.Tensor,
|
|
Tuple[torch.Tensor, torch.Tensor],
|
|
Tuple[torch.Tensor, dict],
|
|
Tuple[Tuple[torch.Tensor, torch.Tensor], dict],
|
|
]:
|
|
if self.sigma_sampler is not None:
|
|
b = vid.shape[0] // self.n_cond_frames
|
|
sigmas = self.sigma_sampler(b).to(vid.device)
|
|
if self.sigma_cond is not None:
|
|
sigma_cond = self.sigma_cond(sigmas)
|
|
sigma_cond = repeat(sigma_cond, "b d -> (b t) d", t=self.n_copies)
|
|
sigmas = repeat(sigmas, "b -> (b t)", t=self.n_cond_frames)
|
|
noise = torch.randn_like(vid)
|
|
vid = vid + noise * append_dims(sigmas, vid.ndim)
|
|
|
|
with platform_appropriate_autocast(enabled=not self.disable_encoder_autocast):
|
|
n_samples = (
|
|
self.en_and_decode_n_samples_a_time
|
|
if self.en_and_decode_n_samples_a_time is not None
|
|
else vid.shape[0]
|
|
)
|
|
n_rounds = math.ceil(vid.shape[0] / n_samples)
|
|
all_out = []
|
|
for n in range(n_rounds):
|
|
if self.is_ae:
|
|
out = self.encoder.encode(vid[n * n_samples : (n + 1) * n_samples])
|
|
else:
|
|
out = self.encoder(vid[n * n_samples : (n + 1) * n_samples])
|
|
all_out.append(out)
|
|
|
|
vid = torch.cat(all_out, dim=0)
|
|
vid *= self.scale_factor
|
|
|
|
vid = rearrange(vid, "(b t) c h w -> b () (t c) h w", t=self.n_cond_frames)
|
|
vid = repeat(vid, "b 1 c h w -> (b t) c h w", t=self.n_copies)
|
|
|
|
return_val = (vid, sigma_cond) if self.sigma_cond is not None else vid
|
|
|
|
return return_val
|
|
|
|
|
|
class FrozenOpenCLIPImagePredictionEmbedder(AbstractEmbModel):
|
|
def __init__(
|
|
self,
|
|
open_clip_embedding_config: Dict,
|
|
n_cond_frames: int,
|
|
n_copies: int,
|
|
):
|
|
super().__init__()
|
|
|
|
self.n_cond_frames = n_cond_frames
|
|
self.n_copies = n_copies
|
|
self.open_clip = instantiate_from_config(open_clip_embedding_config)
|
|
|
|
def forward(self, vid):
|
|
vid = self.open_clip(vid)
|
|
vid = rearrange(vid, "(b t) d -> b t d", t=self.n_cond_frames)
|
|
vid = repeat(vid, "b t d -> (b s) t d", s=self.n_copies)
|
|
|
|
return vid
|