sgm/modules/encoders/modules.py

import math
from contextlib import nullcontext
from functools import partial
from typing import Dict, List, Optional, Tuple, Union

import kornia
import numpy as np
import open_clip
import torch
import torch.nn as nn
from einops import rearrange, repeat
from omegaconf import ListConfig
from torch.utils.checkpoint import checkpoint
from transformers import (
    ByT5Tokenizer,
    CLIPTextModel,
    CLIPTokenizer,
    T5EncoderModel,
    T5Tokenizer,
)

from ...modules.autoencoding.regularizers import DiagonalGaussianRegularizer
from ...modules.diffusionmodules.model import Encoder
from ...modules.diffusionmodules.openaimodel import Timestep
from ...modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ...modules.distributions.distributions import DiagonalGaussianDistribution
from ...util import (
    append_dims,
    autocast,
    count_params,
    default,
    disabled_train,
    expand_dims_like,
    instantiate_from_config,
)


class AbstractEmbModel(nn.Module):
    def __init__(self):
        super().__init__()
        self._is_trainable = None
        self._ucg_rate = None
        self._input_key = None

    @property
    def is_trainable(self) -> bool:
        return self._is_trainable

    @property
    def ucg_rate(self) -> Union[float, torch.Tensor]:
        return self._ucg_rate

    @property
    def input_key(self) -> str:
        return self._input_key

    @is_trainable.setter
    def is_trainable(self, value: bool):
        self._is_trainable = value

    @ucg_rate.setter
    def ucg_rate(self, value: Union[float, torch.Tensor]):
        self._ucg_rate = value

    @input_key.setter
    def input_key(self, value: str):
        self._input_key = value

    @is_trainable.deleter
    def is_trainable(self):
        del self._is_trainable

    @ucg_rate.deleter
    def ucg_rate(self):
        del self._ucg_rate

    @input_key.deleter
    def input_key(self):
        del self._input_key


class GeneralConditioner(nn.Module):
    OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
    KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}

    def __init__(self, emb_models: Union[List, ListConfig]):
        super().__init__()
        embedders = []
        for n, embconfig in enumerate(emb_models):
            embedder = instantiate_from_config(embconfig)
            assert isinstance(
                embedder, AbstractEmbModel
            ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
            embedder.is_trainable = embconfig.get("is_trainable", False)
            embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
            if not embedder.is_trainable:
                embedder.train = disabled_train
                for param in embedder.parameters():
                    param.requires_grad = False
                embedder.eval()
            print(
                f"Initialized embedder #{n}: {embedder.__class__.__name__} "
                f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
            )

            if "input_key" in embconfig:
                embedder.input_key = embconfig["input_key"]
            elif "input_keys" in embconfig:
                embedder.input_keys = embconfig["input_keys"]
            else:
                raise KeyError(
                    f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
                )

            embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
            if embedder.legacy_ucg_val is not None:
                embedder.ucg_prng = np.random.RandomState()

            embedders.append(embedder)
        self.embedders = nn.ModuleList(embedders)

    def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict:
        assert embedder.legacy_ucg_val is not None
        p = embedder.ucg_rate
        val = embedder.legacy_ucg_val
        for i in range(len(batch[embedder.input_key])):
            if embedder.ucg_prng.choice(2, p=[1 - p, p]):
                batch[embedder.input_key][i] = val
        return batch

    def forward(
        self, batch: Dict, force_zero_embeddings: Optional[List] = None
    ) -> Dict:
        output = dict()
        if force_zero_embeddings is None:
            force_zero_embeddings = []
        for embedder in self.embedders:
            embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
            with embedding_context():
                if hasattr(embedder, "input_key") and (embedder.input_key is not None):
                    if embedder.legacy_ucg_val is not None:
                        batch = self.possibly_get_ucg_val(embedder, batch)
                    emb_out = embedder(batch[embedder.input_key])
                elif hasattr(embedder, "input_keys"):
                    emb_out = embedder(*[batch[k] for k in embedder.input_keys])
            assert isinstance(
                emb_out, (torch.Tensor, list, tuple)
            ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
            if not isinstance(emb_out, (list, tuple)):
                emb_out = [emb_out]
            for emb in emb_out:
                out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
                if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
                    emb = (
                        expand_dims_like(
                            torch.bernoulli(
                                (1.0 - embedder.ucg_rate)
                                * torch.ones(emb.shape[0], device=emb.device)
                            ),
                            emb,
                        )
                        * emb
                    )
                if (
                    hasattr(embedder, "input_key")
                    and embedder.input_key in force_zero_embeddings
                ):
                    emb = torch.zeros_like(emb)
                if out_key in output:
                    output[out_key] = torch.cat(
                        (output[out_key], emb), self.KEY2CATDIM[out_key]
                    )
                else:
                    output[out_key] = emb
        return output

    def get_unconditional_conditioning(
        self,
        batch_c: Dict,
        batch_uc: Optional[Dict] = None,
        force_uc_zero_embeddings: Optional[List[str]] = None,
        force_cond_zero_embeddings: Optional[List[str]] = None,
    ):
        if force_uc_zero_embeddings is None:
            force_uc_zero_embeddings = []
        ucg_rates = list()
        for embedder in self.embedders:
            ucg_rates.append(embedder.ucg_rate)
            embedder.ucg_rate = 0.0
        c = self(batch_c, force_cond_zero_embeddings)
        uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings)

        for embedder, rate in zip(self.embedders, ucg_rates):
            embedder.ucg_rate = rate
        return c, uc


class InceptionV3(nn.Module):
    """Wrapper around the https://github.com/mseitzer/pytorch-fid inception
    port with an additional squeeze at the end"""

    def __init__(self, normalize_input=False, **kwargs):
        super().__init__()
        from pytorch_fid import inception

        kwargs["resize_input"] = True
        self.model = inception.InceptionV3(normalize_input=normalize_input, **kwargs)

    def forward(self, inp):
        outp = self.model(inp)

        if len(outp) == 1:
            return outp[0].squeeze()

        return outp


class IdentityEncoder(AbstractEmbModel):
    def encode(self, x):
        return x

    def forward(self, x):
        return x


class ClassEmbedder(AbstractEmbModel):
    def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
        super().__init__()
        self.embedding = nn.Embedding(n_classes, embed_dim)
        self.n_classes = n_classes
        self.add_sequence_dim = add_sequence_dim

    def forward(self, c):
        c = self.embedding(c)
        if self.add_sequence_dim:
            c = c[:, None, :]
        return c

    def get_unconditional_conditioning(self, bs, device="cuda"):
        uc_class = (
            self.n_classes - 1
        )  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
        uc = torch.ones((bs,), device=device) * uc_class
        uc = {self.key: uc.long()}
        return uc


class ClassEmbedderForMultiCond(ClassEmbedder):
    def forward(self, batch, key=None, disable_dropout=False):
        out = batch
        key = default(key, self.key)
        islist = isinstance(batch[key], list)
        if islist:
            batch[key] = batch[key][0]
        c_out = super().forward(batch, key, disable_dropout)
        out[key] = [c_out] if islist else c_out
        return out


class FrozenT5Embedder(AbstractEmbModel):
    """Uses the T5 transformer encoder for text"""

    def __init__(
        self, version="google/t5-v1_1-xxl", device="cuda", max_length=77, freeze=True
    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
        super().__init__()
        self.tokenizer = T5Tokenizer.from_pretrained(version)
        self.transformer = T5EncoderModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        with torch.autocast("cuda", enabled=False):
            outputs = self.transformer(input_ids=tokens)
        z = outputs.last_hidden_state
        return z

    def encode(self, text):
        return self(text)


class FrozenByT5Embedder(AbstractEmbModel):
    """
    Uses the ByT5 transformer encoder for text. Is character-aware.
    """

    def __init__(
        self, version="google/byt5-base", device="cuda", max_length=77, freeze=True
    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
        super().__init__()
        self.tokenizer = ByT5Tokenizer.from_pretrained(version)
        self.transformer = T5EncoderModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        with torch.autocast("cuda", enabled=False):
            outputs = self.transformer(input_ids=tokens)
        z = outputs.last_hidden_state
        return z

    def encode(self, text):
        return self(text)


class FrozenCLIPEmbedder(AbstractEmbModel):
    """Uses the CLIP transformer encoder for text (from huggingface)"""

    LAYERS = ["last", "pooled", "hidden"]

    def __init__(
        self,
        version="openai/clip-vit-large-patch14",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
        layer_idx=None,
        always_return_pooled=False,
    ):  # clip-vit-base-patch32
        super().__init__()
        assert layer in self.LAYERS
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.transformer = CLIPTextModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()
        self.layer = layer
        self.layer_idx = layer_idx
        self.return_pooled = always_return_pooled
        if layer == "hidden":
            assert layer_idx is not None
            assert 0 <= abs(layer_idx) <= 12

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    @autocast
    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        outputs = self.transformer(
            input_ids=tokens, output_hidden_states=self.layer == "hidden"
        )
        if self.layer == "last":
            z = outputs.last_hidden_state
        elif self.layer == "pooled":
            z = outputs.pooler_output[:, None, :]
        else:
            z = outputs.hidden_states[self.layer_idx]
        if self.return_pooled:
            return z, outputs.pooler_output
        return z

    def encode(self, text):
        return self(text)


class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
    """
    Uses the OpenCLIP transformer encoder for text
    """

    LAYERS = ["pooled", "last", "penultimate"]

    def __init__(
        self,
        arch="ViT-H-14",
        version="laion2b_s32b_b79k",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
        always_return_pooled=False,
        legacy=True,
    ):
        super().__init__()
        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
        self.return_pooled = always_return_pooled
        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()
        self.legacy = legacy

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    @autocast
    def forward(self, text):
        tokens = open_clip.tokenize(text)
        z = self.encode_with_transformer(tokens.to(self.device))
        if not self.return_pooled and self.legacy:
            return z
        if self.return_pooled:
            assert not self.legacy
            return z[self.layer], z["pooled"]
        return z[self.layer]

    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)
        if self.legacy:
            x = x[self.layer]
            x = self.model.ln_final(x)
            return x
        else:
            # x is a dict and will stay a dict
            o = x["last"]
            o = self.model.ln_final(o)
            pooled = self.pool(o, text)
            x["pooled"] = pooled
            return x

    def pool(self, x, text):
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = (
            x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
            @ self.model.text_projection
        )
        return x

    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
        outputs = {}
        for i, r in enumerate(self.model.transformer.resblocks):
            if i == len(self.model.transformer.resblocks) - 1:
                outputs["penultimate"] = x.permute(1, 0, 2)  # LND -> NLD
            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)
        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="cuda",
        max_length=77,
        freeze=True,
        layer="last",
    ):
        super().__init__()
        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="cuda",
        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,
    ):
        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

    @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:
            if z.dim() == 2:
                z_ = z[:, None, :]
            else:
                z_ = 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="cuda",
        clip_max_length=77,
        t5_max_length=77,
    ):
        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 torch.autocast("cuda", 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