Fix runtime

sgm/modules/encoders/modules.py

@@ -1,1062 +1,1064 @@
-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 (
-    autocast,
-    count_params,
-    default,
-    disabled_train,
-    expand_dims_like,
-    instantiate_from_config,
-)
-
-from CKPT_PTH import SDXL_CLIP1_PATH, SDXL_CLIP2_CKPT_PTH
-
-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, 'control_vector': 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, batch_uc=None, force_uc_zero_embeddings=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)
-        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 GeneralConditionerWithControl(GeneralConditioner):
-    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:
-                if 'control_vector' in embedder.input_key:
-                    out_key = 'control_vector'
-                else:
-                    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
-
-        output["control"] = batch["control"]
-        return output
-
-
-class PreparedConditioner(nn.Module):
-    def __init__(self, cond_pth, un_cond_pth=None):
-        super().__init__()
-        conditions = torch.load(cond_pth)
-        for k, v in conditions.items():
-            self.register_buffer(k, v)
-        self.un_cond_pth = un_cond_pth
-        if un_cond_pth is not None:
-            un_conditions = torch.load(un_cond_pth)
-            for k, v in un_conditions.items():
-                self.register_buffer(k+'_uc', v)
-
-
-    @torch.no_grad()
-    def forward(
-            self, batch: Dict, return_uc=False
-    ) -> Dict:
-        output = dict()
-        for k, v in self.state_dict().items():
-            if not return_uc:
-                if k.endswith("_uc"):
-                    continue
-                else:
-                    output[k] = v.detach().clone().repeat(batch['control'].shape[0], *[1 for _ in range(v.ndim - 1)])
-            else:
-                if k.endswith("_uc"):
-                    output[k[:-3]] = v.detach().clone().repeat(batch['control'].shape[0], *[1 for _ in range(v.ndim - 1)])
-                else:
-                    continue
-        output["control"] = batch["control"]
-
-        for k, v in output.items():
-            if isinstance(v, torch.Tensor):
-                assert (torch.isnan(v).any()) is not None
-        return output
-
-    def get_unconditional_conditioning(
-        self, batch_c, batch_uc=None, force_uc_zero_embeddings=None
-    ):
-        c = self(batch_c)
-        if self.un_cond_pth is not None:
-            uc = self(batch_c, return_uc=True)
-        else:
-            uc = None
-        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):
-        # inp = kornia.geometry.resize(inp, (299, 299),
-        #                              interpolation='bicubic',
-        #                              align_corners=False,
-        #                              antialias=True)
-        # inp = inp.clamp(min=-1, max=1)
-
-        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
-
-    # @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)
-        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 if SDXL_CLIP1_PATH is None else SDXL_CLIP1_PATH)
-        self.transformer = CLIPTextModel.from_pretrained(version if SDXL_CLIP1_PATH is None else SDXL_CLIP1_PATH)
-        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 if SDXL_CLIP2_CKPT_PTH is None else SDXL_CLIP2_CKPT_PTH,
-        )
-        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,
-    ):
-        super().__init__()
-        model, _, _ = open_clip.create_model_and_transforms(
-            arch,
-            device=torch.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")
-        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
-        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
+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 (
+    autocast,
+    count_params,
+    default,
+    disabled_train,
+    expand_dims_like,
+    instantiate_from_config,
+)
+
+#from CKPT_PTH import SDXL_CLIP1_PATH, SDXL_CLIP2_CKPT_PTH
+from CKPT_PTH import SDXL_CLIP1_PATH
+
+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, 'control_vector': 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, batch_uc=None, force_uc_zero_embeddings=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)
+        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 GeneralConditionerWithControl(GeneralConditioner):
+    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:
+                if 'control_vector' in embedder.input_key:
+                    out_key = 'control_vector'
+                else:
+                    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
+
+        output["control"] = batch["control"]
+        return output
+
+
+class PreparedConditioner(nn.Module):
+    def __init__(self, cond_pth, un_cond_pth=None):
+        super().__init__()
+        conditions = torch.load(cond_pth)
+        for k, v in conditions.items():
+            self.register_buffer(k, v)
+        self.un_cond_pth = un_cond_pth
+        if un_cond_pth is not None:
+            un_conditions = torch.load(un_cond_pth)
+            for k, v in un_conditions.items():
+                self.register_buffer(k+'_uc', v)
+
+
+    @torch.no_grad()
+    def forward(
+            self, batch: Dict, return_uc=False
+    ) -> Dict:
+        output = dict()
+        for k, v in self.state_dict().items():
+            if not return_uc:
+                if k.endswith("_uc"):
+                    continue
+                else:
+                    output[k] = v.detach().clone().repeat(batch['control'].shape[0], *[1 for _ in range(v.ndim - 1)])
+            else:
+                if k.endswith("_uc"):
+                    output[k[:-3]] = v.detach().clone().repeat(batch['control'].shape[0], *[1 for _ in range(v.ndim - 1)])
+                else:
+                    continue
+        output["control"] = batch["control"]
+
+        for k, v in output.items():
+            if isinstance(v, torch.Tensor):
+                assert (torch.isnan(v).any()) is not None
+        return output
+
+    def get_unconditional_conditioning(
+        self, batch_c, batch_uc=None, force_uc_zero_embeddings=None
+    ):
+        c = self(batch_c)
+        if self.un_cond_pth is not None:
+            uc = self(batch_c, return_uc=True)
+        else:
+            uc = None
+        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):
+        # inp = kornia.geometry.resize(inp, (299, 299),
+        #                              interpolation='bicubic',
+        #                              align_corners=False,
+        #                              antialias=True)
+        # inp = inp.clamp(min=-1, max=1)
+
+        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
+
+    # @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)
+        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 if SDXL_CLIP1_PATH is None else SDXL_CLIP1_PATH)
+        self.transformer = CLIPTextModel.from_pretrained(version if SDXL_CLIP1_PATH is None else SDXL_CLIP1_PATH)
+        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 if SDXL_CLIP2_CKPT_PTH is None else SDXL_CLIP2_CKPT_PTH,
+            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,
+    ):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.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")
+        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
+        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