diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..3d998ea
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/lvdm/__init__.py b/lvdm/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/lvdm/basics.py b/lvdm/basics.py
new file mode 100644
index 0000000..3fd14ad
--- /dev/null
+++ b/lvdm/basics.py
@@ -0,0 +1,102 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+import torch.nn as nn
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+from ..utils.utils import instantiate_from_config
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return ops.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return ops.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return ops.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def nonlinearity(type='silu'):
+    if type == 'silu':
+        return nn.SiLU()
+    elif type == 'leaky_relu':
+        return nn.LeakyReLU()
+
+
+class GroupNormSpecific(ops.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def normalization(channels, num_groups=32, dtype=None, device=None):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNormSpecific(num_groups, channels, dtype=dtype, device=device)
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
\ No newline at end of file
diff --git a/lvdm/common.py b/lvdm/common.py
new file mode 100644
index 0000000..55a150b
--- /dev/null
+++ b/lvdm/common.py
@@ -0,0 +1,94 @@
+import math
+from inspect import isfunction
+import torch
+from torch import nn
+import torch.distributed as dist
+
+
+def gather_data(data, return_np=True):
+    ''' gather data from multiple processes to one list '''
+    data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
+    dist.all_gather(data_list, data)  # gather not supported with NCCL
+    if return_np:
+        data_list = [data.cpu().numpy() for data in data_list]
+    return data_list
+
+def autocast(f):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(enabled=True,
+                                     dtype=torch.get_autocast_gpu_dtype(),
+                                     cache_enabled=torch.is_autocast_cache_enabled()):
+            return f(*args, **kwargs)
+    return do_autocast
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+def exists(val):
+    return val is not None
+
+def identity(*args, **kwargs):
+    return nn.Identity()
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+def isimage(x):
+    if not isinstance(x,torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+def shape_to_str(x):
+    shape_str = "x".join([str(x) for x in x.shape])
+    return shape_str
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+ckpt = torch.utils.checkpoint.checkpoint
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        return ckpt(func, *inputs, use_reentrant=False)
+    else:
+        return func(*inputs)
\ No newline at end of file
diff --git a/lvdm/distributions.py b/lvdm/distributions.py
new file mode 100644
index 0000000..9a2a82e
--- /dev/null
+++ b/lvdm/distributions.py
@@ -0,0 +1,95 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self, noise=None):
+        if noise is None:
+            noise = torch.randn(self.mean.shape)
+        
+        x = self.mean + self.std * noise.to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
\ No newline at end of file
diff --git a/lvdm/ema.py b/lvdm/ema.py
new file mode 100644
index 0000000..cd2f8e3
--- /dev/null
+++ b/lvdm/ema.py
@@ -0,0 +1,76 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
+                             else torch.tensor(-1,dtype=torch.int))
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                #remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.','')
+                self.m_name2s_name.update({name:s_name})
+                self.register_buffer(s_name,p.clone().detach().data)
+
+        self.collected_params = []
+
+    def forward(self,model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)
\ No newline at end of file
diff --git a/lvdm/models/autoencoder.py b/lvdm/models/autoencoder.py
new file mode 100644
index 0000000..cfa86e9
--- /dev/null
+++ b/lvdm/models/autoencoder.py
@@ -0,0 +1,219 @@
+import os
+from contextlib import contextmanager
+import torch
+import numpy as np
+from einops import rearrange
+import torch.nn.functional as F
+import pytorch_lightning as pl
+from ...modules.networks.ae_modules import Encoder, Decoder
+from ...distributions import DiagonalGaussianDistribution
+from utils.utils import instantiate_from_config
+
+
+class AutoencoderKL(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 test=False,
+                 logdir=None,
+                 input_dim=4,
+                 test_args=None,
+                 ):
+        super().__init__()
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        assert ddconfig["double_z"]
+        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+        self.input_dim = input_dim
+        self.test = test
+        self.test_args = test_args
+        self.logdir = logdir
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        if self.test:
+            self.init_test()
+    
+    def init_test(self,):
+        self.test = True
+        save_dir = os.path.join(self.logdir, "test")
+        if 'ckpt' in self.test_args:
+            ckpt_name = os.path.basename(self.test_args.ckpt).split('.ckpt')[0] + f'_epoch{self._cur_epoch}'
+            self.root = os.path.join(save_dir, ckpt_name)
+        else:
+            self.root = save_dir
+        if 'test_subdir' in self.test_args:
+            self.root = os.path.join(save_dir, self.test_args.test_subdir)
+
+        self.root_zs = os.path.join(self.root, "zs")
+        self.root_dec = os.path.join(self.root, "reconstructions")
+        self.root_inputs = os.path.join(self.root, "inputs")
+        os.makedirs(self.root, exist_ok=True)
+
+        if self.test_args.save_z:
+            os.makedirs(self.root_zs, exist_ok=True)
+        if self.test_args.save_reconstruction:
+            os.makedirs(self.root_dec, exist_ok=True)
+        if self.test_args.save_input:
+            os.makedirs(self.root_inputs, exist_ok=True)
+        assert(self.test_args is not None)
+        self.test_maximum = getattr(self.test_args, 'test_maximum', None) 
+        self.count = 0
+        self.eval_metrics = {}
+        self.decodes = []
+        self.save_decode_samples = 2048
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")
+        try:
+            self._cur_epoch = sd['epoch']
+            sd = sd["state_dict"]
+        except:
+            self._cur_epoch = 'null'
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        # self.load_state_dict(sd, strict=True)
+        print(f"Restored from {path}")
+
+    def encode(self, x, **kwargs):
+        
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z, **kwargs):
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z)
+        return dec
+
+    def forward(self, input, sample_posterior=True):
+        posterior = self.encode(input)
+        if sample_posterior:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        return dec, posterior
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if x.dim() == 5 and self.input_dim == 4:
+            b,c,t,h,w = x.shape
+            self.b = b
+            self.t = t 
+            x = rearrange(x, 'b c t h w -> (b t) c h w')
+
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+
+        if optimizer_idx == 0:
+            # train encoder+decoder+logvar
+            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # train the discriminator
+            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                                last_layer=self.get_last_layer(), split="train")
+
+            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+                                        last_layer=self.get_last_layer(), split="val")
+
+        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+                                            last_layer=self.get_last_layer(), split="val")
+
+        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+    
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr, betas=(0.5, 0.9))
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    @torch.no_grad()
+    def log_images(self, batch, only_inputs=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if not only_inputs:
+            xrec, posterior = self(x)
+            if x.shape[1] > 3:
+                # colorize with random projection
+                assert xrec.shape[1] > 3
+                x = self.to_rgb(x)
+                xrec = self.to_rgb(xrec)
+            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+            log["reconstructions"] = xrec
+        log["inputs"] = x
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+class IdentityFirstStage(torch.nn.Module):
+    def __init__(self, *args, vq_interface=False, **kwargs):
+        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
+        super().__init__()
+
+    def encode(self, x, *args, **kwargs):
+        return x
+
+    def decode(self, x, *args, **kwargs):
+        return x
+
+    def quantize(self, x, *args, **kwargs):
+        if self.vq_interface:
+            return x, None, [None, None, None]
+        return x
+
+    def forward(self, x, *args, **kwargs):
+        return x
\ No newline at end of file
diff --git a/lvdm/models/ddpm3d.py b/lvdm/models/ddpm3d.py
new file mode 100644
index 0000000..a126ed9
--- /dev/null
+++ b/lvdm/models/ddpm3d.py
@@ -0,0 +1,762 @@
+"""
+wild mixture of
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+from functools import partial
+from contextlib import contextmanager
+import numpy as np
+from tqdm import tqdm
+from einops import rearrange, repeat
+import logging
+mainlogger = logging.getLogger('mainlogger')
+import torch
+import torch.nn as nn
+from torchvision.utils import make_grid
+
+from ...utils.utils import instantiate_from_config
+from ..ema import LitEma
+from ..distributions import DiagonalGaussianDistribution
+from ..models.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr
+from ..basics import disabled_train
+from ..common import (
+    extract_into_tensor,
+    noise_like,
+    exists,
+    default
+)
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+class DDPM(nn.Module):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor=None,
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 rescale_betas_zero_snr=False,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+        self.parameterization = parameterization
+        mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.channels = channels
+        self.temporal_length = unet_config.params.temporal_length
+        self.image_size = image_size  # try conv?
+        if isinstance(self.image_size, int):
+            self.image_size = [self.image_size, self.image_size]
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        #count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        self.rescale_betas_zero_snr = rescale_betas_zero_snr
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        if self.rescale_betas_zero_snr:
+            betas = rescale_zero_terminal_snr(betas)
+        
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., 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. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+
+        if self.parameterization != 'v':
+            self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+            self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+        else:
+            self.register_buffer('sqrt_recip_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod)))
+            self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        elif self.parameterization == "v":
+            lvlb_weights = torch.ones_like(self.betas ** 2 / (
+                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                mainlogger.info(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    mainlogger.info(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    mainlogger.info("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            mainlogger.info(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            mainlogger.info(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def predict_start_from_z_and_v(self, x_t, t, v):
+        # 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. - alphas_cumprod)))
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+        )
+
+    def predict_eps_from_z_and_v(self, x_t, t, v):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    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 get_v(self, x, noise, t):
+        return (
+                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+        )
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+
+class LatentDiffusion(DDPM):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="caption",
+                 cond_stage_trainable=False,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 uncond_prob=0.2,
+                 uncond_type="empty_seq",
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 encoder_type="2d",
+                 only_model=False,
+                 noise_strength=0,
+                 use_dynamic_rescale=False,
+                 base_scale=0.7,
+                 turning_step=400,
+                 loop_video=False,
+                 fps_condition_type='fs',
+                 perframe_ae=False,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        conditioning_key = default(conditioning_key, 'crossattn')
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        self.noise_strength = noise_strength
+        self.use_dynamic_rescale = use_dynamic_rescale
+        self.loop_video = loop_video
+        self.fps_condition_type = fps_condition_type
+        self.perframe_ae = perframe_ae
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+
+        if use_dynamic_rescale:
+            scale_arr1 = np.linspace(1.0, base_scale, turning_step)
+            scale_arr2 = np.full(self.num_timesteps, base_scale)
+            scale_arr = np.concatenate((scale_arr1, scale_arr2))
+            to_torch = partial(torch.tensor, dtype=torch.float32)
+            self.register_buffer('scale_arr', to_torch(scale_arr))
+
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.first_stage_config = first_stage_config
+        self.cond_stage_config = cond_stage_config        
+        self.clip_denoised = False
+
+        self.cond_stage_forward = cond_stage_forward
+        self.encoder_type = encoder_type
+        assert(encoder_type in ["2d", "3d"])
+        self.uncond_prob = uncond_prob
+        self.classifier_free_guidance = True if uncond_prob > 0 else False
+        assert(uncond_type in ["zero_embed", "empty_seq"])
+        self.uncond_type = uncond_type
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
+            self.restarted_from_ckpt = True
+                
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model.eval()
+            self.cond_stage_model.train = disabled_train
+            for param in self.cond_stage_model.parameters():
+                param.requires_grad = False
+        else:
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+    
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def get_first_stage_encoding(self, encoder_posterior, noise=None):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample(noise=noise)
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+   
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if self.encoder_type == "2d" and x.dim() == 5:
+            b, _, t, _, _ = x.shape
+            x = rearrange(x, 'b c t h w -> (b t) c h w')
+            reshape_back = True
+        else:
+            reshape_back = False
+        
+        ## consume more GPU memory but faster
+        if not self.perframe_ae:
+            encoder_posterior = self.first_stage_model.encode(x)
+            results = self.get_first_stage_encoding(encoder_posterior).detach()
+        else:  ## consume less GPU memory but slower
+            results = []
+            for index in range(x.shape[0]):
+                frame_batch = self.first_stage_model.encode(x[index:index+1,:,:,:])
+                frame_result = self.get_first_stage_encoding(frame_batch).detach()
+                results.append(frame_result)
+            results = torch.cat(results, dim=0)
+
+        if reshape_back:
+            results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
+        
+        return results
+    
+    def decode_core(self, z, **kwargs):
+        if self.encoder_type == "2d" and z.dim() == 5:
+            b, _, t, _, _ = z.shape
+            z = rearrange(z, 'b c t h w -> (b t) c h w')
+            reshape_back = True
+        else:
+            reshape_back = False
+            
+        if not self.perframe_ae:    
+            z = 1. / self.scale_factor * z
+            results = self.first_stage_model.decode(z, **kwargs)
+        else:
+            results = []
+            for index in range(z.shape[0]):
+                frame_z = 1. / self.scale_factor * z[index:index+1,:,:,:]
+                frame_result = self.first_stage_model.decode(frame_z, **kwargs)
+                results.append(frame_result)
+            results = torch.cat(results, dim=0)
+
+        if reshape_back:
+            results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
+        return results
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, **kwargs):
+        return self.decode_core(z, **kwargs)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, **kwargs):
+        return self.decode_core(z, **kwargs)
+    
+    def forward(self, x, c, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.use_dynamic_rescale:
+            x = x * extract_into_tensor(self.scale_arr, t, x.shape)
+        return self.p_losses(x, c, t, **kwargs)
+
+    def apply_model(self, x_noisy, t, cond, **kwargs):
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        x_recon = self.model(x_noisy, t, **cond, **kwargs)
+
+        if isinstance(x_recon, tuple):
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _get_denoise_row_from_list(self, samples, desc=''):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device)))
+        n_log_timesteps = len(denoise_row)
+
+        denoise_row = torch.stack(denoise_row)  # n_log_timesteps, b, C, H, W
+        
+        if denoise_row.dim() == 5:
+            denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+            denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+            denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps)
+        elif denoise_row.dim() == 6:
+            # video, grid_size=[n_log_timesteps*bs, t]
+            video_length = denoise_row.shape[3]
+            denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w')
+            denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
+            denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
+            denoise_grid = make_grid(denoise_grid, nrow=video_length)
+        else:
+            raise ValueError
+
+        return denoise_grid
+
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, **kwargs)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+
+        if return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs)
+        if return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
+                      timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]        
+        # sample an initial noise
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+
+class LatentVisualDiffusion(LatentDiffusion):
+    def __init__(self, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._init_embedder(img_cond_stage_config, freeze_embedder)
+        self.image_proj_model = instantiate_from_config(image_proj_stage_config)
+
+    def _init_embedder(self, config, freeze=True):
+        embedder = instantiate_from_config(config)
+        if freeze:
+            self.embedder = embedder.eval()
+            self.embedder.train = disabled_train
+            for param in self.embedder.parameters():
+                param.requires_grad = False
+
+
+class DiffusionWrapper(nn.Module):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
+                c_adm=None, s=None, mask=None, **kwargs):
+        # temporal_context = fps is foNone
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t, **kwargs)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc, **kwargs)
+        elif self.conditioning_key == 'hybrid':
+            ## it is just right [b,c,t,h,w]: concatenate in channel dim
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, **kwargs)
+        elif self.conditioning_key == 'resblockcond':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        elif self.conditioning_key == 'hybrid-adm':
+            assert c_adm is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
+        elif self.conditioning_key == 'hybrid-time':
+            assert s is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, s=s)
+        elif self.conditioning_key == 'concat-time-mask':
+            # assert s is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t, context=None, s=s, mask=mask)
+        elif self.conditioning_key == 'concat-adm-mask':
+            # assert s is not None
+            if c_concat is not None:
+                xc = torch.cat([x] + c_concat, dim=1)
+            else:
+                xc = x
+            out = self.diffusion_model(xc, t, context=None, y=s, mask=mask)
+        elif self.conditioning_key == 'hybrid-adm-mask':
+            cc = torch.cat(c_crossattn, 1)
+            if c_concat is not None:
+                xc = torch.cat([x] + c_concat, dim=1)
+            else:
+                xc = x
+            out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask)
+        elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
+            # assert s is not None
+            assert c_adm is not None
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm)
+        elif self.conditioning_key == 'crossattn-adm':
+            assert c_adm is not None
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc, y=c_adm)
+        else:
+            raise NotImplementedError()
+
+        return out
\ No newline at end of file
diff --git a/lvdm/models/samplers/ddim.py b/lvdm/models/samplers/ddim.py
new file mode 100644
index 0000000..a3270a0
--- /dev/null
+++ b/lvdm/models/samplers/ddim.py
@@ -0,0 +1,317 @@
+import numpy as np
+from tqdm import tqdm
+import torch
+from ..models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg
+from ..common import noise_like
+from ..common import extract_into_tensor
+import copy
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+        self.counter = 0
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        if self.model.use_dynamic_rescale:
+            self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
+            self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]])
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               schedule_verbose=False,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               precision=None,
+               fs=None,
+               timestep_spacing='uniform', #uniform_trailing for starting from last timestep
+               guidance_rescale=0.0,
+               **kwargs
+               ):
+        
+        # check condition bs
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                try:
+                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                except:
+                    cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
+
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose)
+        
+        # make shape
+        if len(shape) == 3:
+            C, H, W = shape
+            size = (batch_size, C, H, W)
+        elif len(shape) == 4:
+            C, T, H, W = shape
+            size = (batch_size, C, T, H, W)
+
+        samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    verbose=verbose,
+                                                    precision=precision,
+                                                    fs=fs,
+                                                    guidance_rescale=guidance_rescale,
+                                                    **kwargs)
+        return samples, intermediates
+
+    @torch.no_grad()
+    def ddim_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0,
+                      **kwargs):
+        device = self.model.betas.device        
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+        if precision is not None:
+            if precision == 16:
+                img = img.to(dtype=torch.float16)
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+            
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        if verbose:
+            iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+        else:
+            iterator = time_range
+
+        clean_cond = kwargs.pop("clean_cond", False)
+
+        # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning)
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            ## use mask to blend noised original latent (img_orig) & new sampled latent (img)
+            if mask is not None:
+                assert x0 is not None
+                if clean_cond:
+                    img_orig = x0
+                else:
+                    img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass? <ddim inversion>
+                img = img_orig * mask + (1. - mask) * img # keep original & modify use img
+
+
+
+
+            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale,
+                                      **kwargs)
+            
+
+            img, pred_x0 = outs
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs):
+        b, *_, device = *x.shape, x.device
+        if x.dim() == 5:
+            is_video = True
+        else:
+            is_video = False
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
+        else:
+            ### do_classifier_free_guidance
+            if isinstance(c, torch.Tensor) or isinstance(c, dict):
+                e_t_cond = self.model.apply_model(x, t, c, **kwargs)
+                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
+            else:
+                raise NotImplementedError
+
+            model_output = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond)
+
+            if guidance_rescale > 0.0:
+                model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale)
+
+        if self.model.parameterization == "v":
+            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+        else:
+            e_t = model_output
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps", 'not implemented'
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        # sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        # select parameters corresponding to the currently considered timestep
+        
+        if is_video:
+            size = (b, 1, 1, 1, 1)
+        else:
+            size = (b, 1, 1, 1)
+        a_t = torch.full(size, alphas[index], device=device)
+        a_prev = torch.full(size, alphas_prev[index], device=device)
+        sigma_t = torch.full(size, sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        if self.model.parameterization != "v":
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        else:
+            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+        
+        if self.model.use_dynamic_rescale:
+            scale_t = torch.full(size, self.ddim_scale_arr[index], device=device)
+            prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device)
+            rescale = (prev_scale_t / scale_t)
+            pred_x0 *= rescale
+
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+        return x_prev, pred_x0
+
+    @torch.no_grad()
+    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+               use_original_steps=False, callback=None):
+
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = timesteps[:t_start]
+
+        time_range = np.flip(timesteps)
+        total_steps = timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+        x_dec = x_latent
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+                                          unconditional_guidance_scale=unconditional_guidance_scale,
+                                          unconditional_conditioning=unconditional_conditioning)
+            if callback: callback(i)
+        return x_dec
+
+    @torch.no_grad()
+    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+        # fast, but does not allow for exact reconstruction
+        # t serves as an index to gather the correct alphas
+        if use_original_steps:
+            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+        else:
+            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+        if noise is None:
+            noise = torch.randn_like(x0)
+        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
diff --git a/lvdm/models/samplers/ddim_multiplecond.py b/lvdm/models/samplers/ddim_multiplecond.py
new file mode 100644
index 0000000..1968fa7
--- /dev/null
+++ b/lvdm/models/samplers/ddim_multiplecond.py
@@ -0,0 +1,323 @@
+import numpy as np
+from tqdm import tqdm
+import torch
+from ...models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg
+from ..common import noise_like
+from ..common import extract_into_tensor
+import copy
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+        self.counter = 0
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        if self.model.use_dynamic_rescale:
+            self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
+            self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]])
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               schedule_verbose=False,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               precision=None,
+               fs=None,
+               timestep_spacing='uniform', #uniform_trailing for starting from last timestep
+               guidance_rescale=0.0,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        
+        # check condition bs
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                try:
+                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                except:
+                    cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
+
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # print('==> timestep_spacing: ', timestep_spacing, guidance_rescale)
+        self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose)
+        
+        # make shape
+        if len(shape) == 3:
+            C, H, W = shape
+            size = (batch_size, C, H, W)
+        elif len(shape) == 4:
+            C, T, H, W = shape
+            size = (batch_size, C, T, H, W)
+        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+        
+        samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    verbose=verbose,
+                                                    precision=precision,
+                                                    fs=fs,
+                                                    guidance_rescale=guidance_rescale,
+                                                    **kwargs)
+        return samples, intermediates
+
+    @torch.no_grad()
+    def ddim_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0,
+                      **kwargs):
+        device = self.model.betas.device        
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+        if precision is not None:
+            if precision == 16:
+                img = img.to(dtype=torch.float16)
+
+        
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+            
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        if verbose:
+            iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+        else:
+            iterator = time_range
+
+        clean_cond = kwargs.pop("clean_cond", False)
+
+        # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning)
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            ## use mask to blend noised original latent (img_orig) & new sampled latent (img)
+            if mask is not None:
+                assert x0 is not None
+                if clean_cond:
+                    img_orig = x0
+                else:
+                    img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass? <ddim inversion>
+                img = img_orig * mask + (1. - mask) * img # keep original & modify use img
+
+
+
+
+            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale,
+                                      **kwargs)
+            
+
+
+            img, pred_x0 = outs
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      uc_type=None, cfg_img=None,mask=None,x0=None,guidance_rescale=0.0, **kwargs):
+        b, *_, device = *x.shape, x.device
+        if x.dim() == 5:
+            is_video = True
+        else:
+            is_video = False
+        if cfg_img is None:
+            cfg_img = unconditional_guidance_scale
+
+        unconditional_conditioning_img_nonetext = kwargs['unconditional_conditioning_img_nonetext']
+
+        
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
+        else:
+            ### with unconditional condition
+            e_t_cond = self.model.apply_model(x, t, c, **kwargs)
+            e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
+            e_t_uncond_img = self.model.apply_model(x, t, unconditional_conditioning_img_nonetext, **kwargs)
+            # text cfg
+            model_output = e_t_uncond + cfg_img * (e_t_uncond_img - e_t_uncond) + unconditional_guidance_scale * (e_t_cond - e_t_uncond_img)
+            if guidance_rescale > 0.0:
+                model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale)
+        
+        if self.model.parameterization == "v":
+            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+        else:
+            e_t = model_output
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps", 'not implemented'
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        # select parameters corresponding to the currently considered timestep
+        
+        if is_video:
+            size = (b, 1, 1, 1, 1)
+        else:
+            size = (b, 1, 1, 1)
+        a_t = torch.full(size, alphas[index], device=device)
+        a_prev = torch.full(size, alphas_prev[index], device=device)
+        sigma_t = torch.full(size, sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        if self.model.parameterization != "v":
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        else:
+            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+        
+        if self.model.use_dynamic_rescale:
+            scale_t = torch.full(size, self.ddim_scale_arr[index], device=device)
+            prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device)
+            rescale = (prev_scale_t / scale_t)
+            pred_x0 *= rescale
+
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+        return x_prev, pred_x0
+
+    @torch.no_grad()
+    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+               use_original_steps=False, callback=None):
+
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = timesteps[:t_start]
+
+        time_range = np.flip(timesteps)
+        total_steps = timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+        x_dec = x_latent
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+                                          unconditional_guidance_scale=unconditional_guidance_scale,
+                                          unconditional_conditioning=unconditional_conditioning)
+            if callback: callback(i)
+        return x_dec
+
+    @torch.no_grad()
+    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+        # fast, but does not allow for exact reconstruction
+        # t serves as an index to gather the correct alphas
+        if use_original_steps:
+            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+        else:
+            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+        if noise is None:
+            noise = torch.randn_like(x0)
+        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
\ No newline at end of file
diff --git a/lvdm/models/samplers/unipc/__init__.py b/lvdm/models/samplers/unipc/__init__.py
new file mode 100644
index 0000000..cdf30d0
--- /dev/null
+++ b/lvdm/models/samplers/unipc/__init__.py
@@ -0,0 +1 @@
+from .sampler import UniPCSampler
\ No newline at end of file
diff --git a/lvdm/models/samplers/unipc/sampler.py b/lvdm/models/samplers/unipc/sampler.py
new file mode 100644
index 0000000..b68ffff
--- /dev/null
+++ b/lvdm/models/samplers/unipc/sampler.py
@@ -0,0 +1,79 @@
+"""SAMPLING ONLY."""
+
+import torch
+
+from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
+
+class UniPCSampler(object):
+    def __init__(self, model, **kwargs):
+        super().__init__()
+        self.model = model
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # sampling
+        C, F, H, W = shape
+        size = (batch_size, C, H, W)
+
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+
+        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+        model_fn = model_wrapper(
+            lambda x, t, c: self.model.apply_model(x, t, c),
+            ns,
+            model_type="noise",
+            guidance_type="classifier-free",
+            condition=conditioning,
+            unconditional_condition=unconditional_conditioning,
+            guidance_scale=unconditional_guidance_scale,
+        )
+
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False)
+        x = uni_pc.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True)
+
+        return x.to(device), None
\ No newline at end of file
diff --git a/lvdm/models/samplers/unipc/uni_pc.py b/lvdm/models/samplers/unipc/uni_pc.py
new file mode 100644
index 0000000..9a42069
--- /dev/null
+++ b/lvdm/models/samplers/unipc/uni_pc.py
@@ -0,0 +1,808 @@
+import torch
+import torch.nn.functional as F
+import math
+
+
+class NoiseScheduleVP:
+    def __init__(
+            self,
+            schedule='discrete',
+            betas=None,
+            alphas_cumprod=None,
+            continuous_beta_0=0.1,
+            continuous_beta_1=20.,
+        ):
+        """Create a wrapper class for the forward SDE (VP type).
+
+        ***
+        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+        ***
+
+        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+
+            log_alpha_t = self.marginal_log_mean_coeff(t)
+            sigma_t = self.marginal_std(t)
+            lambda_t = self.marginal_lambda(t)
+
+        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+
+            t = self.inverse_lambda(lambda_t)
+
+        ===============================================================
+
+        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+
+        1. For discrete-time DPMs:
+
+            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+                t_i = (i + 1) / N
+            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+
+            Args:
+                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+
+            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+
+            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
+                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
+                and
+                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+
+
+        2. For continuous-time DPMs:
+
+            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+            schedule are the default settings in DDPM and improved-DDPM:
+
+            Args:
+                beta_min: A `float` number. The smallest beta for the linear schedule.
+                beta_max: A `float` number. The largest beta for the linear schedule.
+                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+                T: A `float` number. The ending time of the forward process.
+
+        ===============================================================
+
+        Args:
+            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+                    'linear' or 'cosine' for continuous-time DPMs.
+        Returns:
+            A wrapper object of the forward SDE (VP type).
+        
+        ===============================================================
+
+        Example:
+
+        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', betas=betas)
+
+        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+
+        # For continuous-time DPMs (VPSDE), linear schedule:
+        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+
+        """
+
+        if schedule not in ['discrete', 'linear', 'cosine']:
+            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+
+        self.schedule = schedule
+        if schedule == 'discrete':
+            if betas is not None:
+                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+            else:
+                assert alphas_cumprod is not None
+                log_alphas = 0.5 * torch.log(alphas_cumprod)
+            self.total_N = len(log_alphas)
+            self.T = 1.
+            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+            self.log_alpha_array = log_alphas.reshape((1, -1,))
+        else:
+            self.total_N = 1000
+            self.beta_0 = continuous_beta_0
+            self.beta_1 = continuous_beta_1
+            self.cosine_s = 0.008
+            self.cosine_beta_max = 999.
+            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+            self.schedule = schedule
+            if schedule == 'cosine':
+                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+                self.T = 0.9946
+            else:
+                self.T = 1.
+
+    def marginal_log_mean_coeff(self, t):
+        """
+        Compute log(alpha_t) of a given continuous-time label t in [0, T].
+        """
+        if self.schedule == 'discrete':
+            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
+        elif self.schedule == 'linear':
+            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+        elif self.schedule == 'cosine':
+            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
+            return log_alpha_t
+
+    def marginal_alpha(self, t):
+        """
+        Compute alpha_t of a given continuous-time label t in [0, T].
+        """
+        return torch.exp(self.marginal_log_mean_coeff(t))
+
+    def marginal_std(self, t):
+        """
+        Compute sigma_t of a given continuous-time label t in [0, T].
+        """
+        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+    def marginal_lambda(self, t):
+        """
+        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+        """
+        log_mean_coeff = self.marginal_log_mean_coeff(t)
+        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+        return log_mean_coeff - log_std
+
+    def inverse_lambda(self, lamb):
+        """
+        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+        """
+        if self.schedule == 'linear':
+            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            Delta = self.beta_0**2 + tmp
+            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+        elif self.schedule == 'discrete':
+            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
+            return t.reshape((-1,))
+        else:
+            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            t = t_fn(log_alpha)
+            return t
+
+
+def model_wrapper(
+    model,
+    noise_schedule,
+    model_type="noise",
+    model_kwargs={},
+    guidance_type="uncond",
+    condition=None,
+    unconditional_condition=None,
+    guidance_scale=1.,
+    classifier_fn=None,
+    classifier_kwargs={},
+):
+    """Create a wrapper function for the noise prediction model.
+
+    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+
+    We support four types of the diffusion model by setting `model_type`:
+
+        1. "noise": noise prediction model. (Trained by predicting noise).
+
+        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+
+        3. "v": velocity prediction model. (Trained by predicting the velocity).
+            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+
+            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+                arXiv preprint arXiv:2202.00512 (2022).
+            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+                arXiv preprint arXiv:2210.02303 (2022).
+    
+        4. "score": marginal score function. (Trained by denoising score matching).
+            Note that the score function and the noise prediction model follows a simple relationship:
+            ```
+                noise(x_t, t) = -sigma_t * score(x_t, t)
+            ```
+
+    We support three types of guided sampling by DPMs by setting `guidance_type`:
+        1. "uncond": unconditional sampling by DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+
+        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            `` 
+
+            The input `classifier_fn` has the following format:
+            ``
+                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+            ``
+
+            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+
+        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+            `` 
+            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+
+            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+                arXiv preprint arXiv:2207.12598 (2022).
+        
+
+    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+    or continuous-time labels (i.e. epsilon to T).
+
+    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+    ``
+        def model_fn(x, t_continuous) -> noise:
+            t_input = get_model_input_time(t_continuous)
+            return noise_pred(model, x, t_input, **model_kwargs)         
+    ``
+    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+
+    ===============================================================
+
+    Args:
+        model: A diffusion model with the corresponding format described above.
+        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+        model_type: A `str`. The parameterization type of the diffusion model.
+                    "noise" or "x_start" or "v" or "score".
+        model_kwargs: A `dict`. A dict for the other inputs of the model function.
+        guidance_type: A `str`. The type of the guidance for sampling.
+                    "uncond" or "classifier" or "classifier-free".
+        condition: A pytorch tensor. The condition for the guided sampling.
+                    Only used for "classifier" or "classifier-free" guidance type.
+        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+                    Only used for "classifier-free" guidance type.
+        guidance_scale: A `float`. The scale for the guided sampling.
+        classifier_fn: A classifier function. Only used for the classifier guidance.
+        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+    Returns:
+        A noise prediction model that accepts the noised data and the continuous time as the inputs.
+    """
+
+    def get_model_input_time(t_continuous):
+        """
+        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+        For continuous-time DPMs, we just use `t_continuous`.
+        """
+        if noise_schedule.schedule == 'discrete':
+            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+        else:
+            return t_continuous
+
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, None, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "x_start":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+        elif model_type == "score":
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return -expand_dims(sigma_t, dims) * output
+
+    def cond_grad_fn(x, t_input):
+        """
+        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+        """
+        with torch.enable_grad():
+            x_in = x.detach().requires_grad_(True)
+            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+            return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+    def model_fn(x, t_continuous):
+        """
+        The noise predicition model function that is used for DPM-Solver.
+        """
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_type == "uncond":
+            return noise_pred_fn(x, t_continuous)
+        elif guidance_type == "classifier":
+            assert classifier_fn is not None
+            t_input = get_model_input_time(t_continuous)
+            cond_grad = cond_grad_fn(x, t_input)
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            noise = noise_pred_fn(x, t_continuous)
+            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+        elif guidance_type == "classifier-free":
+            if guidance_scale == 1. or unconditional_condition is None:
+                return noise_pred_fn(x, t_continuous, cond=condition)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t_continuous] * 2)
+                c_in = torch.cat([unconditional_condition, condition])
+                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+                return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+    assert model_type in ["noise", "x_start", "v"]
+    assert guidance_type in ["uncond", "classifier", "classifier-free"]
+    return model_fn
+
+
+class UniPC:
+    def __init__(
+        self,
+        model_fn,
+        noise_schedule,
+        predict_x0=True,
+        thresholding=False,
+        max_val=1.,
+        variant='bh1'
+    ):
+        """Construct a UniPC. 
+
+        We support both data_prediction and noise_prediction.
+        """
+        self.model = model_fn
+        self.noise_schedule = noise_schedule
+        self.variant = variant
+        self.predict_x0 = predict_x0
+        self.thresholding = thresholding
+        self.max_val = max_val
+        
+    def dynamic_thresholding_fn(self, x0, t=None):
+        """
+        The dynamic thresholding method. 
+        """
+        dims = x0.dim()
+        p = self.dynamic_thresholding_ratio
+        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
+        x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def noise_prediction_fn(self, x, t):
+        """
+        Return the noise prediction model.
+        """
+        return self.model(x, t)
+
+    def data_prediction_fn(self, x, t):
+        """
+        Return the data prediction model (with thresholding).
+        """
+        noise = self.noise_prediction_fn(x, t)
+        dims = x.dim()
+        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+        if self.thresholding:
+            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
+            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+            x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model_fn(self, x, t):
+        """
+        Convert the model to the noise prediction model or the data prediction model. 
+        """
+        if self.predict_x0:
+            return self.data_prediction_fn(x, t)
+        else:
+            return self.noise_prediction_fn(x, t)
+
+    def get_time_steps(self, skip_type, t_T, t_0, N, device):
+        """Compute the intermediate time steps for sampling.
+        """
+        if skip_type == 'logSNR':
+            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+            return self.noise_schedule.inverse_lambda(logSNR_steps)
+        elif skip_type == 'time_uniform':
+            return torch.linspace(t_T, t_0, N + 1).to(device)
+        elif skip_type == 'time_quadratic':
+            t_order = 2
+            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
+            return t
+        else:
+            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+        """
+        Get the order of each step for sampling by the singlestep DPM-Solver.
+        """
+        if order == 3:
+            K = steps // 3 + 1
+            if steps % 3 == 0:
+                orders = [3,] * (K - 2) + [2, 1]
+            elif steps % 3 == 1:
+                orders = [3,] * (K - 1) + [1]
+            else:
+                orders = [3,] * (K - 1) + [2]
+        elif order == 2:
+            if steps % 2 == 0:
+                K = steps // 2
+                orders = [2,] * K
+            else:
+                K = steps // 2 + 1
+                orders = [2,] * (K - 1) + [1]
+        elif order == 1:
+            K = steps
+            orders = [1,] * steps
+        else:
+            raise ValueError("'order' must be '1' or '2' or '3'.")
+        if skip_type == 'logSNR':
+            # To reproduce the results in DPM-Solver paper
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+        else:
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
+        return timesteps_outer, orders
+
+    def denoise_to_zero_fn(self, x, s):
+        """
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
+        """
+        return self.data_prediction_fn(x, s)
+
+    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
+        if len(t.shape) == 0:
+            t = t.view(-1)
+        if 'bh' in self.variant:
+            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+        else:
+            assert self.variant == 'vary_coeff'
+            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+
+    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
+        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+        ns = self.noise_schedule
+        assert order <= len(model_prev_list)
+
+        # first compute rks
+        t_prev_0 = t_prev_list[-1]
+        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+        lambda_t = ns.marginal_lambda(t)
+        model_prev_0 = model_prev_list[-1]
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        log_alpha_t = ns.marginal_log_mean_coeff(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h = lambda_t - lambda_prev_0
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            t_prev_i = t_prev_list[-(i + 1)]
+            model_prev_i = model_prev_list[-(i + 1)]
+            lambda_prev_i = ns.marginal_lambda(t_prev_i)
+            rk = (lambda_prev_i - lambda_prev_0) / h
+            rks.append(rk)
+            D1s.append((model_prev_i - model_prev_0) / rk)
+
+        rks.append(1.)
+        rks = torch.tensor(rks, device=x.device)
+
+        K = len(rks)
+        # build C matrix
+        C = []
+
+        col = torch.ones_like(rks)
+        for k in range(1, K + 1):
+            C.append(col)
+            col = col * rks / (k + 1) 
+        C = torch.stack(C, dim=1)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1) # (B, K)
+            C_inv_p = torch.linalg.inv(C[:-1, :-1])
+            A_p = C_inv_p
+
+        if use_corrector:
+            print('using corrector')
+            C_inv = torch.linalg.inv(C)
+            A_c = C_inv
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)
+        h_phi_ks = []
+        factorial_k = 1
+        h_phi_k = h_phi_1
+        for k in range(1, K + 2):
+            h_phi_ks.append(h_phi_k)
+            h_phi_k = h_phi_k / hh - 1 / factorial_k
+            factorial_k *= (k + 1)
+
+        model_t = None
+        if self.predict_x0:
+            x_t_ = (
+                sigma_t / sigma_prev_0 * x
+                - alpha_t * h_phi_1 * model_prev_0
+            )
+            # now predictor
+            x_t = x_t_
+            if len(D1s) > 0:
+                # compute the residuals for predictor
+                for k in range(K - 1):
+                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+            # now corrector
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_
+                k = 0
+                for k in range(K - 1):
+                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+        else:
+            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+            x_t_ = (
+                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
+                - (sigma_t * h_phi_1) * model_prev_0
+            )
+            # now predictor
+            x_t = x_t_
+            if len(D1s) > 0:
+                # compute the residuals for predictor
+                for k in range(K - 1):
+                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+            # now corrector
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_
+                k = 0
+                for k in range(K - 1):
+                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+        return x_t, model_t
+
+    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
+        print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
+        ns = self.noise_schedule
+        assert order <= len(model_prev_list)
+        dims = x.dim()
+
+        # first compute rks
+        t_prev_0 = t_prev_list[-1]
+        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+        lambda_t = ns.marginal_lambda(t)
+        model_prev_0 = model_prev_list[-1]
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h = lambda_t - lambda_prev_0
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            t_prev_i = t_prev_list[-(i + 1)]
+            model_prev_i = model_prev_list[-(i + 1)]
+            lambda_prev_i = ns.marginal_lambda(t_prev_i)
+            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
+            rks.append(rk)
+            D1s.append((model_prev_i - model_prev_0) / rk)
+
+        rks.append(1.)
+        rks = torch.tensor(rks, device=x.device)
+
+        R = []
+        b = []
+
+        hh = -h[0] if self.predict_x0 else h[0]
+        h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.variant == 'bh1':
+            B_h = hh
+        elif self.variant == 'bh2':
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+            
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= (i + 1)
+            h_phi_k = h_phi_k / hh - 1 / factorial_i 
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=x.device)
+
+        # now predictor
+        use_predictor = len(D1s) > 0 and x_t is None
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1) # (B, K)
+            if x_t is None:
+                # for order 2, we use a simplified version
+                if order == 2:
+                    rhos_p = torch.tensor([0.5], device=b.device)
+                else:
+                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+        else:
+            D1s = None
+
+        if use_corrector:
+            print('using corrector')
+            # for order 1, we use a simplified version
+            if order == 1:
+                rhos_c = torch.tensor([0.5], device=b.device)
+            else:
+                rhos_c = torch.linalg.solve(R, b)
+
+        model_t = None
+        if self.predict_x0:
+            x_t_ = (
+                expand_dims(sigma_t / sigma_prev_0, dims) * x
+                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
+            )
+
+            if x_t is None:
+                if use_predictor:
+                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                else:
+                    pred_res = 0
+                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
+
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                if D1s is not None:
+                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                else:
+                    corr_res = 0
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+        else:
+            x_t_ = (
+                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
+            )
+            if x_t is None:
+                if use_predictor:
+                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                else:
+                    pred_res = 0
+                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
+
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                if D1s is not None:
+                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                else:
+                    corr_res = 0
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+        return x_t, model_t
+
+
+    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+        atol=0.0078, rtol=0.05, corrector=False,
+    ):
+        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+        t_T = self.noise_schedule.T if t_start is None else t_start
+        device = x.device
+        if method == 'multistep':
+            assert steps >= order
+            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            assert timesteps.shape[0] - 1 == steps
+            with torch.no_grad():
+                vec_t = timesteps[0].expand((x.shape[0]))
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+                # Init the first `order` values by lower order multistep DPM-Solver.
+                for init_order in range(1, order):
+                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+                    if model_x is None:
+                        model_x = self.model_fn(x, vec_t)
+                    model_prev_list.append(model_x)
+                    t_prev_list.append(vec_t)
+                for step in range(order, steps + 1):
+                    vec_t = timesteps[step].expand(x.shape[0])
+                    if lower_order_final:
+                        step_order = min(order, steps + 1 - step)
+                    else:
+                        step_order = order
+                    print('this step order:', step_order)
+                    if step == steps:
+                        print('do not run corrector at the last step')
+                        use_corrector = False
+                    else:
+                        use_corrector = True
+                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+                    for i in range(order - 1):
+                        t_prev_list[i] = t_prev_list[i + 1]
+                        model_prev_list[i] = model_prev_list[i + 1]
+                    t_prev_list[-1] = vec_t
+                    # We do not need to evaluate the final model value.
+                    if step < steps:
+                        if model_x is None:
+                            model_x = self.model_fn(x, vec_t)
+                        model_prev_list[-1] = model_x
+        else:
+            raise NotImplementedError()
+        if denoise_to_zero:
+            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+        return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+    """
+    A piecewise linear function y = f(x), using xp and yp as keypoints.
+    We implement f(x) in a differentiable way (i.e. applicable for autograd).
+    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+
+    Args:
+        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+        yp: PyTorch tensor with shape [C, K].
+    Returns:
+        The function values f(x), with shape [N, C].
+    """
+    N, K = x.shape[0], xp.shape[1]
+    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+    x_idx = torch.argmin(x_indices, dim=2)
+    cand_start_idx = x_idx - 1
+    start_idx = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(1, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+    start_idx2 = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(0, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+    return cand
+
+
+def expand_dims(v, dims):
+    """
+    Expand the tensor `v` to the dim `dims`.
+
+    Args:
+        `v`: a PyTorch tensor with shape [N].
+        `dim`: a `int`.
+    Returns:
+        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+    """
+    return v[(...,) + (None,)*(dims - 1)]
\ No newline at end of file
diff --git a/lvdm/models/utils_diffusion.py b/lvdm/models/utils_diffusion.py
new file mode 100644
index 0000000..403b7b3
--- /dev/null
+++ b/lvdm/models/utils_diffusion.py
@@ -0,0 +1,158 @@
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops import repeat
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False, dtype=None):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=dtype) / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        embedding = repeat(timesteps, 'b -> b d', d=dim)
+    return embedding.to(dtype)
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+    if schedule == "linear":
+        betas = (
+                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+        )
+
+    elif schedule == "cosine":
+        timesteps = (
+                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+        )
+        alphas = timesteps / (1 + cosine_s) * np.pi / 2
+        alphas = torch.cos(alphas).pow(2)
+        alphas = alphas / alphas[0]
+        betas = 1 - alphas[1:] / alphas[:-1]
+        betas = np.clip(betas, a_min=0, a_max=0.999)
+
+    elif schedule == "sqrt_linear":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+    elif schedule == "sqrt":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+    else:
+        raise ValueError(f"schedule '{schedule}' unknown.")
+    return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+    if ddim_discr_method == 'uniform':
+        c = num_ddpm_timesteps // num_ddim_timesteps
+        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+        steps_out = ddim_timesteps + 1
+    elif ddim_discr_method == 'uniform_trailing':
+        c = num_ddpm_timesteps / num_ddim_timesteps
+        ddim_timesteps = np.flip(np.round(np.arange(num_ddpm_timesteps, 0, -c))).astype(np.int64)
+        steps_out = ddim_timesteps - 1
+    elif ddim_discr_method == 'quad':
+        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+        steps_out = ddim_timesteps + 1
+    else:
+        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+    # add one to get the final alpha values right (the ones from first scale to data during sampling)
+    # steps_out = ddim_timesteps + 1
+    if verbose:
+        print(f'Selected timesteps for ddim sampler: {steps_out}')
+    return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+    # select alphas for computing the variance schedule
+    # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}')
+    alphas = alphacums[ddim_timesteps]
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+    if verbose:
+        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        print(f'For the chosen value of eta, which is {eta}, '
+              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+    return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+def rescale_zero_terminal_snr(betas):
+    """
+    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+
+    Args:
+        betas (`numpy.ndarray`):
+            the betas that the scheduler is being initialized with.
+
+    Returns:
+        `numpy.ndarray`: rescaled betas with zero terminal SNR
+    """
+    # Convert betas to alphas_bar_sqrt
+    alphas = 1.0 - betas
+    alphas_cumprod = np.cumprod(alphas, axis=0)
+    alphas_bar_sqrt = np.sqrt(alphas_cumprod)
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].copy()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].copy()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
+    alphas = np.concatenate([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+
+    return betas
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
\ No newline at end of file
diff --git a/lvdm/modules/attention.py b/lvdm/modules/attention.py
new file mode 100644
index 0000000..7537aa5
--- /dev/null
+++ b/lvdm/modules/attention.py
@@ -0,0 +1,676 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from functools import partial
+from ..common import (
+    checkpoint,
+    exists,
+    default,
+)
+from ..basics import zero_module
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+from comfy import model_management
+from comfy.ldm.modules.attention import optimized_attention, optimized_attention_masked
+
+if model_management.xformers_enabled():
+    import xformers
+    import xformers.ops
+    XFORMERS_IS_AVAILBLE = True
+else:
+    XFORMERS_IS_AVAILBLE = False
+
+class RelativePosition(nn.Module):
+    """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
+
+    def __init__(self, num_units, max_relative_position):
+        super().__init__()
+        self.num_units = num_units
+        self.max_relative_position = max_relative_position
+        self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
+        nn.init.xavier_uniform_(self.embeddings_table)
+
+    def forward(self, length_q, length_k):
+        device = self.embeddings_table.device
+        range_vec_q = torch.arange(length_q, device=device)
+        range_vec_k = torch.arange(length_k, device=device)
+        distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
+        distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
+        final_mat = distance_mat_clipped + self.max_relative_position
+        final_mat = final_mat.long()
+        embeddings = self.embeddings_table[final_mat]
+        return embeddings
+
+
+# TODO Add native Comfy optimized attention.
+class CrossAttention(nn.Module):
+
+    def __init__(
+                self, 
+                query_dim, 
+                context_dim=None, 
+                heads=8, 
+                dim_head=64, 
+                dropout=0., 
+                relative_position=False, 
+                temporal_length=None, 
+                video_length=None, 
+                image_cross_attention=False, 
+                image_cross_attention_scale=1.0, 
+                image_cross_attention_scale_learnable=False, 
+                text_context_len=77,
+                device=None,
+                dtype=None,
+                operations=ops
+            ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+        self.scale = dim_head**-0.5
+        self.heads = heads
+        self.dim_head = dim_head
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) 
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, device=device, dtype=dtype), 
+            nn.Dropout(dropout)
+        )
+        
+        self.relative_position = relative_position
+        if self.relative_position:
+            assert(temporal_length is not None)
+            self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
+            self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
+        else:
+            ## only used for spatial attention, while NOT for temporal attention
+            if XFORMERS_IS_AVAILBLE and temporal_length is None:
+                self.forward = self.efficient_forward
+            else:
+                self.forward = self.comfy_efficient_forward
+
+        self.video_length = video_length
+        self.image_cross_attention = image_cross_attention
+        self.image_cross_attention_scale = image_cross_attention_scale
+        self.text_context_len = text_context_len
+        self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
+        if self.image_cross_attention:
+            self.to_k_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+            self.to_v_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+            if image_cross_attention_scale_learnable:
+                self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
+
+    def comfy_efficient_forward(self, x, context=None, mask=None):
+        spatial_self_attn = (context is None)
+        k_ip, v_ip, out_ip = None, None, None
+
+        h = self.heads
+        q = self.to_q(x)
+        context = default(context, x)
+
+        if self.image_cross_attention and not spatial_self_attn:
+            context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+            k_ip = self.to_k_ip(context_image)
+            v_ip = self.to_v_ip(context_image)
+        else:
+            if not spatial_self_attn:
+                context = context[:,:self.text_context_len,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+
+        out = optimized_attention(q, k, v, h)
+
+        if exists(mask):
+            ## feasible for causal attention mask only
+            out = optimized_attention_masked(q, k, v, h)
+
+        ## for image cross-attention
+        if k_ip is not None:
+            q = rearrange(q, 'b n (h d) -> (b h) n d', h=h)
+            k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
+            sim_ip =  torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
+            del k_ip
+            sim_ip = sim_ip.softmax(dim=-1)
+            out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
+            out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
+
+        if out_ip is not None:
+            if self.image_cross_attention_scale_learnable:
+                out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
+            else:
+                out = out + self.image_cross_attention_scale * out_ip
+
+        return self.to_out(out)
+            
+    def forward(self, x, context=None, mask=None):
+        spatial_self_attn = (context is None)
+        k_ip, v_ip, out_ip = None, None, None
+
+        h = self.heads
+        q = self.to_q(x)
+        context = default(context, x)
+
+        if self.image_cross_attention and not spatial_self_attn:
+            context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+            k_ip = self.to_k_ip(context_image)
+            v_ip = self.to_v_ip(context_image)
+        else:
+
+            # Assumed Spatial Attention (b c h w)
+            if not spatial_self_attn:
+                context = context[:,:self.text_context_len,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+        sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
+        if self.relative_position:
+            len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
+            k2 = self.relative_position_k(len_q, len_k)
+            sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check 
+            sim += sim2
+        del k
+
+        if exists(mask):
+            ## feasible for causal attention mask only
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b i j -> (b h) i j', h=h)
+            sim.masked_fill_(~(mask>0.5), max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = torch.einsum('b i j, b j d -> b i d', sim, v)
+        if self.relative_position:
+            v2 = self.relative_position_v(len_q, len_v)
+            out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check
+            out += out2
+        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+
+
+        ## for image cross-attention
+        if k_ip is not None:
+            k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
+            sim_ip =  torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
+            del k_ip
+            sim_ip = sim_ip.softmax(dim=-1)
+            out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
+            out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
+
+
+        if out_ip is not None:
+            if self.image_cross_attention_scale_learnable:
+                out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
+            else:
+                out = out + self.image_cross_attention_scale * out_ip
+        
+        return self.to_out(out)
+    
+    def efficient_forward(self, x, context=None, mask=None):
+        spatial_self_attn = (context is None)
+        k_ip, v_ip, out_ip = None, None, None
+
+        q = self.to_q(x)
+        context = default(context, x)
+
+        if self.image_cross_attention and not spatial_self_attn:
+            context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+            k_ip = self.to_k_ip(context_image)
+            v_ip = self.to_v_ip(context_image)
+        else:
+            if not spatial_self_attn:
+                context = context[:,:self.text_context_len,:]
+            k = self.to_k(context)
+            v = self.to_v(context)
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+        # actually compute the attention, what we cannot get enough of
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
+        
+        ## for image cross-attention
+        if k_ip is not None:
+            k_ip, v_ip = map(
+                lambda t: t.unsqueeze(3)
+                .reshape(b, t.shape[1], self.heads, self.dim_head)
+                .permute(0, 2, 1, 3)
+                .reshape(b * self.heads, t.shape[1], self.dim_head)
+                .contiguous(),
+                (k_ip, v_ip),
+            )
+            out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None)
+            out_ip = (
+                out_ip.unsqueeze(0)
+                .reshape(b, self.heads, out.shape[1], self.dim_head)
+                .permute(0, 2, 1, 3)
+                .reshape(b, out.shape[1], self.heads * self.dim_head)
+            )
+
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        if out_ip is not None:
+            if self.image_cross_attention_scale_learnable:
+                out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
+            else:
+                out = out + self.image_cross_attention_scale * out_ip
+           
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+
+    def __init__(
+        self, 
+        dim, 
+        n_heads, 
+        d_head, 
+        dropout=0., 
+        context_dim=None, 
+        gated_ff=True, 
+        checkpoint=True,
+        disable_self_attn=False, 
+        attention_cls=None, 
+        video_length=None, 
+        image_cross_attention=False, 
+        image_cross_attention_scale=1.0, 
+        image_cross_attention_scale_learnable=False, 
+        text_context_len=77,
+        device=None,
+        dtype=None,
+        operations=ops
+    ):
+        super().__init__()
+        attn_cls = CrossAttention if attention_cls is None else attention_cls
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+            context_dim=None, device=device, dtype=dtype if self.disable_self_attn else None)
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, device=device, dtype=dtype)
+        self.attn2 = attn_cls(
+            query_dim=dim, 
+            context_dim=context_dim, 
+            heads=n_heads, 
+            dim_head=d_head, 
+            dropout=dropout, 
+            video_length=video_length, 
+            image_cross_attention=image_cross_attention, 
+            image_cross_attention_scale=image_cross_attention_scale, 
+            image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
+            text_context_len=text_context_len,
+            device=device,
+            dtype=dtype
+        )
+        self.image_cross_attention = image_cross_attention
+
+        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.norm3 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None, mask=None, **kwargs):
+        ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
+        input_tuple = (x,)      ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
+        if context is not None:
+            input_tuple = (x, context)
+        if mask is not None:
+            forward_mask = partial(self._forward, mask=mask)
+            return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
+        return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
+
+
+    def _forward(self, x, context=None, mask=None):
+        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
+        x = self.attn2(self.norm2(x), context=context, mask=mask) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data in spatial axis.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+
+    def __init__(
+        self, 
+        in_channels, 
+        n_heads, 
+        d_head, 
+        depth=1, 
+        dropout=0., 
+        context_dim=None,
+        use_checkpoint=True, 
+        disable_self_attn=False, 
+        use_linear=False, 
+        video_length=None,
+        image_cross_attention=False, 
+        image_cross_attention_scale_learnable=False,
+        device=None,
+        dtype=None,
+        operations=ops
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
+        if not use_linear:
+            self.proj_in = opeations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
+        else:
+            self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
+
+        attention_cls = None
+        self.transformer_blocks = nn.ModuleList([
+            BasicTransformerBlock(
+                inner_dim,
+                n_heads,
+                d_head,
+                dropout=dropout,
+                context_dim=context_dim,
+                disable_self_attn=disable_self_attn,
+                checkpoint=use_checkpoint,
+                attention_cls=attention_cls,
+                video_length=video_length,
+                image_cross_attention=image_cross_attention,
+                image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
+                device=device,
+                dtype=dtype
+                ) for d in range(depth)
+        ])
+        if not use_linear:
+            self.proj_out = zero_module(operations.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype))
+        else:
+            self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None, **kwargs):
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            x = block(x, context=context, **kwargs)
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in
+    
+    
+class TemporalTransformer(nn.Module):
+    """
+    Transformer block for image-like data in temporal axis.
+    First, reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    """
+    def __init__(
+        self, 
+        in_channels, 
+        n_heads, 
+        d_head, 
+        depth=1, 
+        dropout=0., 
+        context_dim=None,
+        use_checkpoint=True, 
+        use_linear=False, 
+        only_self_att=True, 
+        causal_attention=False, 
+        causal_block_size=1,
+        relative_position=False, 
+        temporal_length=None,
+        device=None,
+        dtype=None,
+        operations=ops
+    ):
+        super().__init__()
+        self.only_self_att = only_self_att
+        self.relative_position = relative_position
+        self.causal_attention = causal_attention
+        self.causal_block_size = causal_block_size
+
+        if only_self_att:
+            context_dim = None
+            
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
+        self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
+        if not use_linear:
+            self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
+        else:
+            self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
+
+        if relative_position:
+            assert(temporal_length is not None)
+            attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length, device=device, dtype=dtype)
+        else:
+            attention_cls = partial(CrossAttention, temporal_length=temporal_length, device=device, dtype=dtype)
+        if self.causal_attention:
+            assert(temporal_length is not None)
+            self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
+
+        if self.only_self_att:
+            context_dim = None
+        self.transformer_blocks = nn.ModuleList([
+            BasicTransformerBlock(
+                inner_dim,
+                n_heads,
+                d_head,
+                dropout=dropout,
+                context_dim=context_dim,
+                attention_cls=attention_cls,
+                checkpoint=use_checkpoint,
+                device=device,
+                dtype=dtype
+            ) for d in range(depth)
+        ])
+        if not use_linear:
+            self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0).to(device, dtype))
+        else:
+            self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None):
+        b, c, t, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous()
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, 'bhw c t -> bhw t c').contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+
+        temp_mask = None
+        if self.causal_attention:
+            # slice the from mask map
+            temp_mask = self.mask[:,:t,:t].to(x.device)
+
+        if temp_mask is not None:
+            mask = temp_mask.to(x.device)
+            mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w)
+        else:
+            mask = None
+
+        if self.only_self_att:
+            ## note: if no context is given, cross-attention defaults to self-attention
+            for i, block in enumerate(self.transformer_blocks):
+                x = block(x, mask=mask)
+            x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
+        else:
+            x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
+            context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous()
+            for i, block in enumerate(self.transformer_blocks):
+                # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
+                for j in range(b):
+                    context_j = repeat(
+                        context[j],
+                        't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous()
+                    ## note: causal mask will not applied in cross-attention case
+                    x[j] = block(x[j], context=context_j)
+        
+        if self.use_linear:
+            x = self.proj_out(x)
+            x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous()
+            x = self.proj_out(x)
+            x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous()
+
+        return x + x_in
+    
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out * 2, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., device=None, dtype=None, operations=ops):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            operations.Linear(dim, inner_dim, device=device, dtype=dtype),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            operations.Linear(inner_dim, dim_out, device=device, dtype=dtype)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = operations.Conv2d(dim, hidden_dim * 3, 1, bias = False, device=device, dtype=dtype)
+        self.to_out = operations.Conv2d(hidden_dim, dim, 1, device=device, dtype=dtype)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        k = k.softmax(dim=-1)  
+        context = torch.einsum('bhdn,bhen->bhde', k, v)
+        out = torch.einsum('bhde,bhdn->bhen', context, q)
+        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        return self.to_out(out)
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = operations.GroupNorm(
+            num_groups=32, 
+            num_channels=in_channels, 
+            eps=1e-6, 
+            affine=True, 
+            device=device, 
+            dtype=dtype
+        )
+        self.q = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.k = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.v = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.proj_out = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = rearrange(q, 'b c h w -> b (h w) c')
+        k = rearrange(k, 'b c h w -> b c (h w)')
+        w_ = torch.einsum('bij,bjk->bik', q, k)
+
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, 'b c h w -> b c (h w)')
+        w_ = rearrange(w_, 'b i j -> b j i')
+        h_ = torch.einsum('bij,bjk->bik', v, w_)
+        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+        h_ = self.proj_out(h_)
+
+        return x+h_
diff --git a/lvdm/modules/encoders/condition.py b/lvdm/modules/encoders/condition.py
new file mode 100644
index 0000000..610322b
--- /dev/null
+++ b/lvdm/modules/encoders/condition.py
@@ -0,0 +1,389 @@
+import torch
+import torch.nn as nn
+import kornia
+import open_clip
+from torch.utils.checkpoint import checkpoint
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+from ..common import autocast
+from utils.utils import count_params
+
+
+class AbstractEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+    def encode(self, x):
+        return x
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+        self.n_classes = n_classes
+        self.ucg_rate = ucg_rate
+
+    def forward(self, batch, key=None, disable_dropout=False):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        if self.ucg_rate > 0. and not disable_dropout:
+            mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+            c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
+            c = c.long()
+        c = self.embedding(c)
+        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}
+        return uc
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+    """Uses the T5 transformer encoder for text"""
+
+    def __init__(self, version="google/t5-v1_1-large", 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  # TODO: typical value?
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        # self.train = disabled_train
+        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)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """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):  # 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
+        if layer == "hidden":
+            assert layer_idx is not None
+            assert 0 <= abs(layer_idx) <= 12
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        # self.train = disabled_train
+        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)
+        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]
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class ClipImageEmbedder(nn.Module):
+    def __init__(
+            self,
+            model,
+            jit=False,
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+            antialias=True,
+            ucg_rate=0.
+    ):
+        super().__init__()
+        from clip import load as load_clip
+        self.model, _ = load_clip(name=model, device=device, jit=jit)
+
+        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
+
+    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.) / 2.
+        # re-normalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x, no_dropout=False):
+        # x is assumed to be in range [-1,1]
+        out = self.model.encode_image(self.preprocess(x))
+        out = out.to(x.dtype)
+        if self.ucg_rate > 0. and not no_dropout:
+            out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
+        return out
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+    """
+    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"):
+        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) ## all clip models use 77 as context length
+        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(AbstractEncoder):
+    """
+    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, layer="pooled", antialias=True, ucg_rate=0.):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
+                                                            pretrained=version, )
+        del model.transformer
+        self.model = model
+        # self.mapper = torch.nn.Linear(1280, 1024)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "penultimate":
+            raise NotImplementedError()
+            self.layer_idx = 1
+
+        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
+
+    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.) / 2.
+        # 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.model.parameters():
+            param.requires_grad = False
+    
+    @autocast
+    def forward(self, image, no_dropout=False):
+        z = self.encode_with_vision_transformer(image)
+        if self.ucg_rate > 0. and not no_dropout:
+            z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
+        return z
+
+    def encode_with_vision_transformer(self, img):
+        img = self.preprocess(img)
+        x = self.model.visual(img)
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
+    """
+    Uses the OpenCLIP vision transformer encoder for images
+    """
+
+    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda",
+                 freeze=True, layer="pooled", antialias=True):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
+                                                            pretrained=version, )
+        del model.transformer
+        self.model = model
+        self.device = device
+
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "penultimate":
+            raise NotImplementedError()
+            self.layer_idx = 1
+
+        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)
+
+
+    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.) / 2.
+        # 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.model.parameters():
+            param.requires_grad = False
+
+    def forward(self, image, no_dropout=False): 
+        ## image: b c h w
+        z = self.encode_with_vision_transformer(image)
+        return z
+
+    def encode_with_vision_transformer(self, x):
+        x = self.preprocess(x)
+
+        # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
+        if self.model.visual.input_patchnorm:
+            # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
+            x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1])
+            x = x.permute(0, 2, 4, 1, 3, 5)
+            x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1)
+            x = self.model.visual.patchnorm_pre_ln(x)
+            x = self.model.visual.conv1(x)
+        else:
+            x = self.model.visual.conv1(x)  # shape = [*, width, grid, grid]
+            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+
+        # class embeddings and positional embeddings
+        x = torch.cat(
+            [self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.model.visual.positional_embedding.to(x.dtype)
+
+        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
+        x = self.model.visual.patch_dropout(x)
+        x = self.model.visual.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.model.visual.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+
+        return x
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+    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]
diff --git a/lvdm/modules/encoders/resampler.py b/lvdm/modules/encoders/resampler.py
new file mode 100644
index 0000000..d9abd23
--- /dev/null
+++ b/lvdm/modules/encoders/resampler.py
@@ -0,0 +1,145 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py
+import math
+import torch
+import torch.nn as nn
+
+
+class ImageProjModel(nn.Module):
+    """Projection Model"""
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+        super().__init__()        
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = nn.LayerNorm(cross_attention_dim)
+        
+    def forward(self, image_embeds):
+        #embeds = image_embeds
+        embeds = image_embeds.type(list(self.proj.parameters())[0].dtype)
+        clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+    
+    
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+        
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+        
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+        
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        video_length=None, # using frame-wise version or not
+    ):
+        super().__init__()
+        ## queries for a single frame / image
+        self.num_queries = num_queries 
+        self.video_length = video_length
+
+        ## <num_queries> queries for each frame
+        if video_length is not None: 
+            num_queries = num_queries * video_length
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        self.proj_in = nn.Linear(embedding_dim, dim)
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+        
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        latents = self.latents.repeat(x.size(0), 1, 1) ## B (T L) C
+        x = self.proj_in(x)
+        
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+            
+        latents = self.proj_out(latents)
+        latents = self.norm_out(latents) # B L C or B (T L) C
+
+        return latents
\ No newline at end of file
diff --git a/lvdm/modules/networks/ae_modules.py b/lvdm/modules/networks/ae_modules.py
new file mode 100644
index 0000000..35b6817
--- /dev/null
+++ b/lvdm/modules/networks/ae_modules.py
@@ -0,0 +1,1026 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import numpy as np
+import torch.nn as nn
+from einops import rearrange
+from utils.utils import instantiate_from_config
+from ...modules.attention import LinearAttention
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def nonlinearity(x):
+    # swish
+    return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32, device=None, dtype=None, operations=ops):
+    return operations.GroupNorm(
+        num_groups=num_groups, 
+        num_channels=in_channels, 
+        eps=1e-6, 
+        affine=True,
+        device=device,
+        dtype=dtype
+    )
+
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+    def __init__(self, in_channels, device=None, dtype=None):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels, device=device, dtype=dtype)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels, device=device, dtype=dtype)
+        self.q = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.k = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.v = operations.Conv2d(
+                                in_channels,
+                                in_channels,
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                device=device,
+                                dtype=dtype
+                            )
+        self.proj_out = operations.Conv2d(
+                                    in_channels,
+                                    in_channels,
+                                    kernel_size=1,
+                                    stride=1,
+                                    padding=0,
+                                    device=device,
+                                    dtype=dtype
+                                )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = q.reshape(b,c,h*w) # bcl
+        q = q.permute(0,2,1)   # bcl -> blc l=hw
+        k = k.reshape(b,c,h*w) # bcl
+        
+        w_ = torch.bmm(q,k)    # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b,c,h*w)
+        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b,c,h,w)
+
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+def make_attn(in_channels, attn_type="vanilla", device=None, dtype=None):
+    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
+    #print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    if attn_type == "vanilla":
+        return AttnBlock(in_channels, device=device, dtype=dtype)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        return LinAttnBlock(in_channels, device=device, dtype=dtype)
+ 
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.with_conv = with_conv
+        self.in_channels = in_channels
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = operations.Conv2d(
+                                    in_channels,
+                                    in_channels,
+                                    kernel_size=3,
+                                    stride=2,
+                                    padding=0,
+                                    device=device,
+                                    dtype=dtype
+                                )
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0,1,0,1)
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.with_conv = with_conv
+        self.in_channels = in_channels
+        if self.with_conv:
+            self.conv = operations.Conv2d(
+                                    in_channels,
+                                    in_channels,
+                                    kernel_size=3,
+                                    stride=1,
+                                    padding=1,
+                                    device=device,
+                                    dtype=dtype
+                                )
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0,1,0,0))
+    return emb
+
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self, 
+        *, 
+        in_channels, 
+        out_channels=None, 
+        conv_shortcut=False,
+        dropout, 
+        temb_channels=512,
+        device=None,
+        dtype=None,
+        operations=ops
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels, device=device, dtype=dtype)
+        self.conv1 = operations.Conv2d(
+                                    in_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1,
+                                     device=device,
+                                     dtype=dtype
+                                )
+        if temb_channels > 0:
+            self.temb_proj = operations.Linear(
+                                            temb_channels,
+                                            out_channels,
+                                            device=device,
+                                            dtype=dtype
+                                        )
+        self.norm2 = Normalize(out_channels, device=device, dtype=dtype)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = operations.Conv2d(
+                                    out_channels,
+                                    out_channels,
+                                    kernel_size=3,
+                                    stride=1,
+                                    padding=1,
+                                    device=device,
+                                    dtype=dtype
+                                )
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = operations.Conv2d(
+                                                    in_channels,
+                                                    out_channels,
+                                                    kernel_size=3,
+                                                    stride=1,
+                                                    padding=1,
+                                                    device=device,
+                                                    dtype=device
+                                                )
+            else:
+                self.nin_shortcut = operations.Conv2d(
+                                                    in_channels,
+                                                    out_channels,
+                                                    kernel_size=1,
+                                                    stride=1,
+                                                    padding=0,
+                                                    device=device,
+                                                    dtype=dtype
+                                                )
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x+h
+
+class Model(nn.Module):
+    def __init__(
+        self, 
+        *, 
+        ch, 
+        out_ch, 
+        ch_mult=(1,2,4,8), 
+        num_res_blocks,
+        attn_resolutions, 
+        dropout=0.0, 
+        resamp_with_conv=True, 
+        in_channels,
+        resolution, 
+        use_timestep=True, 
+        use_linear_attn=False, 
+        attn_type="vanilla",
+        device=None,
+        dtype=None,
+        operations=ops
+    ):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = self.ch*4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList([
+                operations.Linear(
+                                self.ch,
+                                self.temb_ch,
+                                device=device,
+                                dtype=dtype
+                            ),
+                operations.Linear(
+                                self.temb_ch,
+                                self.temb_ch,
+                                device=device,
+                                dtype=dtype
+                            ),
+            ])
+
+        # downsampling
+        self.conv_in = operations.Conv2d(
+                                    in_channels,
+                                    self.ch,
+                                    kernel_size=3,
+                                    stride=1,
+                                    padding=1,
+                                    device=device,
+                                    dtype=dtype
+                                )
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout,
+                                         device=device,
+                                         dtype=dtype))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv, device=device, dtype=dtype)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            skip_in = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch*in_ch_mult[i_level]
+                block.append(ResnetBlock(in_channels=block_in+skip_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout,
+                                         device=device,
+                                         dtype=dtype))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv, device=device, dtype=dtype)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in, device=device, dtype=device)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, x, t=None, context=None):
+        #assert x.shape[2] == x.shape[3] == self.resolution
+        if context is not None:
+            # assume aligned context, cat along channel axis
+            x = torch.cat((x, context), dim=1)
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+    def get_last_layer(self):
+        return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self, 
+        *, 
+        ch, 
+        out_ch, 
+        ch_mult=(1,2,4,8), 
+        num_res_blocks,
+        attn_resolutions, 
+        dropout=0.0, 
+        resamp_with_conv=True, 
+        in_channels,
+        resolution, 
+        z_channels, 
+        double_z=True, 
+        use_linear_attn=False, 
+        attn_type="vanilla",
+        device=None,
+        dtype=None,
+        operations=ops,
+        **ignore_kwargs
+    ):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = operations.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1,
+                                       device=device,
+                                       dtype=dtype)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout,
+                                         device=device,
+                                         dtype=dtype))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv, device=device, dtype=dtype)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+
+        # end
+        self.norm_out = Normalize(block_in, device=device, dtype=dtype)
+        self.conv_out = operations.Conv2d(block_in,
+                                        2*z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, x):
+        # timestep embedding
+        temb = None
+
+        # print(f'encoder-input={x.shape}')
+        # downsampling
+        hs = [self.conv_in(x)]
+        # print(f'encoder-conv in feat={hs[0].shape}')
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                # print(f'encoder-down feat={h.shape}')
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                # print(f'encoder-downsample (input)={hs[-1].shape}')
+                hs.append(self.down[i_level].downsample(hs[-1]))
+                # print(f'encoder-downsample (output)={hs[-1].shape}')
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        # print(f'encoder-mid1 feat={h.shape}')
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+        # print(f'encoder-mid2 feat={h.shape}')
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        # print(f'end feat={h.shape}')
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self, 
+        *, 
+        ch, 
+        out_ch, 
+        ch_mult=(1,2,4,8), 
+        num_res_blocks,
+        attn_resolutions, 
+        dropout=0.0, 
+        resamp_with_conv=True, 
+        in_channels,
+        resolution, 
+        z_channels, 
+        give_pre_end=False, 
+        tanh_out=False, 
+        use_linear_attn=False,
+        attn_type="vanilla", 
+        device=None,
+        dtype=None,
+        operations=ops,
+        **ignorekwargs
+    ):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,)+tuple(ch_mult)
+        block_in = ch*ch_mult[self.num_resolutions-1]
+        curr_res = resolution // 2**(self.num_resolutions-1)
+        self.z_shape = (1,z_channels,curr_res,curr_res)
+        print("AE working on z of shape {} = {} dimensions.".format(
+            self.z_shape, np.prod(self.z_shape)))
+
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(z_channels,
+                                       block_in,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1,
+                                       device=device,
+                                       dtype=dtype)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout,
+                                       device=device,
+                                       dtype=dtype)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout,
+                                         device=device,
+                                         dtype=dtype))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type, device=device, dtype=dtype))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv, device=device, dtype=dtype)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in, device=device, dtype=dtype)
+        self.conv_out = operations.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, z):
+        #assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # print(f'decoder-input={z.shape}')
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+        # print(f'decoder-conv in feat={h.shape}')
+
+        # middle
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+        # print(f'decoder-mid feat={h.shape}')
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](h, temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+                # print(f'decoder-up feat={h.shape}')
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+                # print(f'decoder-upsample feat={h.shape}')
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        # print(f'decoder-conv_out feat={h.shape}')
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h
+
+
+class SimpleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, device=None, dtype=None, operations=ops, *args, **kwargs):
+        super().__init__()
+        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+                                     ResnetBlock(in_channels=in_channels,
+                                                 out_channels=2 * in_channels,
+                                                 temb_channels=0, dropout=0.0,
+                                                 device=device,
+                                                 dtype=dtype),
+                                     ResnetBlock(in_channels=2 * in_channels,
+                                                out_channels=4 * in_channels,
+                                                temb_channels=0, dropout=0.0,
+                                                device=device,
+                                                dtype=dtype),
+                                     ResnetBlock(in_channels=4 * in_channels,
+                                                out_channels=2 * in_channels,
+                                                temb_channels=0, dropout=0.0,
+                                                device=device,
+                                                dtype=dtype),
+                                     operations.Conv2d(2*in_channels, in_channels, 1),
+                                     Upsample(in_channels, with_conv=True, device=device, dtype=dtype)])
+        # end
+        self.norm_out = Normalize(in_channels, device=device, dtype=dtype)
+        self.conv_out = torch.nn.Conv2d(in_channels,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, x):
+        for i, layer in enumerate(self.model):
+            if i in [1,2,3]:
+                x = layer(x, None)
+            else:
+                x = layer(x)
+
+        h = self.norm_out(x)
+        h = nonlinearity(h)
+        x = self.conv_out(h)
+        return x
+
+
+class UpsampleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+                 ch_mult=(2,2), dropout=0.0, device=None, dtype=None, operations=ops):
+        super().__init__()
+        # upsampling
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        block_in = in_channels
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.res_blocks = nn.ModuleList()
+        self.upsample_blocks = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            res_block = []
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                res_block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout,
+                                         device=device,
+                                         dtype=dtype))
+                block_in = block_out
+            self.res_blocks.append(nn.ModuleList(res_block))
+            if i_level != self.num_resolutions - 1:
+                self.upsample_blocks.append(Upsample(block_in, True, device=device, dtype=dtype))
+                curr_res = curr_res * 2
+
+        # end
+        self.norm_out = Normalize(block_in, device=device, dtype=dtype)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, x):
+        # upsampling
+        h = x
+        for k, i_level in enumerate(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.res_blocks[i_level][i_block](h, None)
+            if i_level != self.num_resolutions - 1:
+                h = self.upsample_blocks[k](h)
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class LatentRescaler(nn.Module):
+    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2, device=None, dtype=None, operations=ops):
+        super().__init__()
+        # residual block, interpolate, residual block
+        self.factor = factor
+        self.conv_in = operations.Conv2d(in_channels,
+                                 mid_channels,
+                                 kernel_size=3,
+                                 stride=1,
+                                 padding=1,
+                                 device=device,
+                                 dtype=dtype)
+        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0,
+                                                     device=device,
+                                                     dtype=dtype) for _ in range(depth)])
+        self.attn = AttnBlock(mid_channels, device=device, dtype=dtype)
+        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0,
+                                                     device=device,
+                                                     dtype=dtype) for _ in range(depth)])
+
+        self.conv_out = operations.Conv2d(mid_channels,
+                                  out_channels,
+                                  kernel_size=1,
+                                  device=device,
+                                  dtype=dtype
+                                  )
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        for block in self.res_block1:
+            x = block(x, None)
+        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+        x = self.attn(x)
+        for block in self.res_block2:
+            x = block(x, None)
+        x = self.conv_out(x)
+        return x
+
+
+class MergedRescaleEncoder(nn.Module):
+    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
+                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1, device=None, dtype=None, operations=ops):
+        super().__init__()
+        intermediate_chn = ch * ch_mult[-1]
+        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
+                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+                               out_ch=None, device=device, dtype=dtype)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth, 
+                                       device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.rescaler(x)
+        return x
+
+
+class MergedRescaleDecoder(nn.Module):
+    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1,
+                 device=None, dtype=None, operations=ops):
+        super().__init__()
+        tmp_chn = z_channels*ch_mult[-1]
+        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+                               ch_mult=ch_mult, resolution=resolution, ch=ch, device=device, operations=ops)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+                                       out_channels=tmp_chn, depth=rescale_module_depth, device=device, operations=ops)
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Upsampler(nn.Module):
+    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2, device=None, dtype=None, operations=ops):
+        super().__init__()
+        assert out_size >= in_size
+        num_blocks = int(np.log2(out_size//in_size))+1
+        factor_up = 1.+ (out_size % in_size)
+        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+                                       out_channels=in_channels, device=device, dtype=dtype)
+        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+                               attn_resolutions=[], in_channels=None, ch=in_channels, device=device, dtype=dtype,
+                               ch_mult=[ch_mult for _ in range(num_blocks)])
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Resize(nn.Module):
+    def __init__(self, in_channels=None, learned=False, mode="bilinear", device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.with_conv = learned
+        self.mode = mode
+        if self.with_conv:
+            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+            raise NotImplementedError()
+            assert in_channels is not None
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = operations.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=4,
+                                        stride=2,
+                                        padding=1,
+                                        device=device,
+                                        dtype=dtype)
+
+    def forward(self, x, scale_factor=1.0):
+        if scale_factor==1.0:
+            return x
+        else:
+            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+        return x
+
+class FirstStagePostProcessor(nn.Module):
+
+    def __init__(self, ch_mult:list, in_channels,
+                 pretrained_model:nn.Module=None,
+                 reshape=False,
+                 n_channels=None,
+                 dropout=0.,
+                 pretrained_config=None,
+                 device=None,
+                 dtype=None,
+                 operations=ops):
+        super().__init__()
+        if pretrained_config is None:
+            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+            self.pretrained_model = pretrained_model
+        else:
+            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+            self.instantiate_pretrained(pretrained_config)
+
+        self.do_reshape = reshape
+
+        if n_channels is None:
+            n_channels = self.pretrained_model.encoder.ch
+
+        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2, device=device, dtype=dtype)
+        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
+                            stride=1,padding=1, device=device, dtype=dtype)
+
+        blocks = []
+        downs = []
+        ch_in = n_channels
+        for m in ch_mult:
+            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout, device=device, dtype=dtype))
+            ch_in = m * n_channels
+            downs.append(Downsample(ch_in, with_conv=False, device=device, dtype=dtype))
+
+        self.model = nn.ModuleList(blocks)
+        self.downsampler = nn.ModuleList(downs)
+
+
+    def instantiate_pretrained(self, config):
+        model = instantiate_from_config(config)
+        self.pretrained_model = model.eval()
+        # self.pretrained_model.train = False
+        for param in self.pretrained_model.parameters():
+            param.requires_grad = False
+
+
+    @torch.no_grad()
+    def encode_with_pretrained(self,x):
+        c = self.pretrained_model.encode(x)
+        if isinstance(c, DiagonalGaussianDistribution):
+            c = c.mode()
+        return  c
+
+    def forward(self,x):
+        z_fs = self.encode_with_pretrained(x)
+        z = self.proj_norm(z_fs)
+        z = self.proj(z)
+        z = nonlinearity(z)
+
+        for submodel, downmodel in zip(self.model,self.downsampler):
+            z = submodel(z,temb=None)
+            z = downmodel(z)
+
+        if self.do_reshape:
+            z = rearrange(z,'b c h w -> b (h w) c')
+        return z
\ No newline at end of file
diff --git a/lvdm/modules/networks/openaimodel3d.py b/lvdm/modules/networks/openaimodel3d.py
new file mode 100644
index 0000000..874e21e
--- /dev/null
+++ b/lvdm/modules/networks/openaimodel3d.py
@@ -0,0 +1,750 @@
+from functools import partial
+from abc import abstractmethod
+import torch
+import torch.nn as nn
+from einops import rearrange
+import torch.nn.functional as F
+from ...models.utils_diffusion import timestep_embedding
+from ...common import checkpoint
+from ...basics import (
+    zero_module,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    normalization
+)
+from ...modules.attention import SpatialTransformer, TemporalTransformer
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
+def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
+    for layer in ts:
+        if isinstance(layer, TimestepBlock):
+            x = layer(x, emb)
+        elif isinstance(layer, SpatialVideoTransformer):
+            x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
+            if "transformer_index" in transformer_options:
+                transformer_options["transformer_index"] += 1
+        elif isinstance(layer, TemporalTransformer):
+            x = layer(x, context, transformer_options)
+            if "transformer_index" in transformer_options:
+                transformer_options["transformer_index"] += 1
+        elif isinstance(layer, Upsample):
+            x = layer(x, output_shape=output_shape)
+        else:
+            x = layer(x)
+    return x
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None, batch_size=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb, batch_size=batch_size)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            elif isinstance(layer, TemporalTransformer):
+                x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
+                x = layer(x, context)
+                x = rearrange(x, 'b c f h w -> (b f) c h w')
+            else:
+                x = layer(x)
+        return x
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = operations.conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest')
+        else:
+            x = F.interpolate(x, scale_factor=2, mode='nearest')
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    :param use_temporal_conv: if True, use the temporal convolution.
+    :param use_image_dataset: if True, the temporal parameters will not be optimized.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        use_conv=False,
+        up=False,
+        down=False,
+        kernel_size=3,
+        use_temporal_conv=False,
+        tempspatial_aware=False,
+        dtype=None,
+        device=None,
+        operations=ops
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+        self.use_temporal_conv = use_temporal_conv
+
+        if isinstance(kernel_size, list):
+            padding =[k // 2 for k in kernel_size]
+        else:
+            padding = kernel_size // 2
+
+        # operations used in normalization function
+        self.in_layers = nn.Sequential(
+            normalization(channels, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+            self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+            self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+                dtype=dtype,
+                device=device
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels, dtype=dtype, device=device),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(operations.Conv2d(self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
+        else:
+            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
+
+        if self.use_temporal_conv:
+            self.temopral_conv = TemporalConvBlock(
+                self.out_channels,
+                self.out_channels,
+                dropout=0.1,
+                spatial_aware=tempspatial_aware,
+                dtype=dtype,
+                device=device
+            )
+
+    def forward(self, x, emb, batch_size=None):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        input_tuple = (x, emb)
+        if batch_size:
+            forward_batchsize = partial(self._forward, batch_size=batch_size)
+            return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint)
+        return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint)
+
+    def _forward(self, x, emb, batch_size=None):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        h = self.skip_connection(x) + h
+
+        if self.use_temporal_conv and batch_size:
+            h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size)
+            h = self.temopral_conv(h)
+            h = rearrange(h, 'b c t h w -> (b t) c h w')
+        return h
+
+class TemporalConvBlock(nn.Module):
+    """
+    Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py
+    """
+    def __init__(
+        self, 
+        in_channels, 
+        out_channels=None, 
+        dropout=0.0, 
+        spatial_aware=False, 
+        dtype=None, 
+        device=None, 
+        operations=ops
+    ):
+        super(TemporalConvBlock, self).__init__()
+        if out_channels is None:
+            out_channels = in_channels
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1)
+        th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0)
+        tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3)
+        tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1)
+
+        # conv layers
+        self.conv1 = nn.Sequential(
+            operations.GroupNorm(32, in_channels, device=device, dtype=dtype), nn.SiLU(),
+            operations.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape, device=device, dtype=dtype))
+        self.conv2 = nn.Sequential(
+            operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
+            operations.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape, device=device, dtype=dtype))
+        self.conv3 = nn.Sequential(
+            operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
+            operations.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape, device=device, dtype=dtype))
+        self.conv4 = nn.Sequential(
+            operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
+            operations.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape, device=device, dtype=dtype))
+
+        # zero out the last layer params,so the conv block is identity
+        nn.init.zeros_(self.conv4[-1].weight)
+        nn.init.zeros_(self.conv4[-1].bias)
+
+    def forward(self, x):
+        identity = x
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        return identity + x
+
+def context_processor(context, t, img_emb=None, temporal_size=16, concat_only=False, disable_concat=False):
+    if disable_concat:
+        return context
+        
+    ## repeat t times for context [(b t) 77 768] & time embedding
+    ## check if we use per-frame image conditioning
+
+    if img_emb is not None:
+        context = torch.cat([context, img_emb.to(context.device, context.dtype)], dim=1)
+        
+    if concat_only:
+        return context
+
+    b, l_context, _ = context.shape
+    if l_context == 77 + t * temporal_size:
+        context_text, context_img = context[:,:77,:], context[:,77:,:]
+        context_text = context_text.repeat_interleave(repeats=t, dim=0)
+        context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t)
+        context = torch.cat([context_text, context_img], dim=1)
+    else:
+        context = context.repeat_interleave(repeats=t, dim=0)
+
+    return context
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: in_channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 model_channels,
+                 out_channels,
+                 num_res_blocks,
+                 attention_resolutions,
+                 dropout=0.0,
+                 channel_mult=(1, 2, 4, 8),
+                 conv_resample=True,
+                 dims=2,
+                 context_dim=None,
+                 use_scale_shift_norm=False,
+                 resblock_updown=False,
+                 num_heads=-1,
+                 num_head_channels=-1,
+                 transformer_depth=1,
+                 use_linear=False,
+                 use_checkpoint=False,
+                 temporal_conv=False,
+                 tempspatial_aware=False,
+                 temporal_attention=True,
+                 use_relative_position=True,
+                 use_causal_attention=False,
+                 temporal_length=None,
+                 use_fp16=False,
+                 addition_attention=False,
+                 temporal_selfatt_only=True,
+                 image_cross_attention=False,
+                 image_cross_attention_scale_learnable=False,
+                 default_fs=4,
+                 fs_condition=False,
+                 device=None,
+                 dtype=torch.float16,
+                 operations=ops
+                ):
+        super(UNetModel, self).__init__()
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.temporal_attention = temporal_attention
+        time_embed_dim = model_channels * 4
+        self.use_checkpoint = use_checkpoint
+        temporal_self_att_only = True
+        self.addition_attention = addition_attention
+        self.temporal_length = temporal_length
+        self.image_cross_attention = image_cross_attention
+        self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
+        self.default_fs = default_fs
+        self.fs_condition = fs_condition
+        self.device = device
+        #self.dtype = dtype
+        self.dtype = torch.float32
+
+        ## Time embedding blocks
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim, device=device, dtype=self.dtype),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim, device=device, dtype=self.dtype),
+        )
+        if fs_condition:
+            self.fps_embedding = nn.Sequential(
+                linear(model_channels, time_embed_dim, device=device, dtype=self.dtype),
+                nn.SiLU(),
+                linear(time_embed_dim, time_embed_dim, device=device, dtype=self.dtype),
+            )
+            nn.init.zeros_(self.fps_embedding[-1].weight)
+            nn.init.zeros_(self.fps_embedding[-1].bias)
+        ## Input Block
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    operations.conv_nd(
+                        dims, 
+                        in_channels, 
+                        model_channels, 
+                        3, 
+                        padding=1, 
+                        device=device, 
+                        dtype=self.dtype
+                    ))
+            ]
+        )
+        if self.addition_attention:
+            self.init_attn=TimestepEmbedSequential(
+                TemporalTransformer(
+                    model_channels,
+                    n_heads=8,
+                    d_head=num_head_channels,
+                    depth=transformer_depth,
+                    context_dim=context_dim,
+                    use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, 
+                    causal_attention=False, relative_position=use_relative_position, 
+                    temporal_length=temporal_length,
+                    device=device,
+                    dtype=self.dtype
+                    ))
+
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(ch, time_embed_dim, dropout,
+                        out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+                        use_temporal_conv=temporal_conv,
+                        device=device,
+                        dtype=self.dtype
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    layers.append(
+                        SpatialTransformer(ch, num_heads, dim_head, 
+                            depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                            use_checkpoint=use_checkpoint, disable_self_attn=False, 
+                            video_length=temporal_length, image_cross_attention=self.image_cross_attention,
+                            image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, 
+                            device=device,
+                            dtype=self.dtype                     
+                        )
+                    )
+                    if self.temporal_attention:
+                        layers.append(
+                            TemporalTransformer(ch, num_heads, dim_head,
+                                depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                                use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, 
+                                causal_attention=use_causal_attention, relative_position=use_relative_position, 
+                                temporal_length=temporal_length,
+                                device=device,
+                                dtype=self.dtype
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(ch, time_embed_dim, dropout, 
+                            out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                            device=device,
+                            dtype=self.dtype
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, 
+                            conv_resample, 
+                            dims=dims, 
+                            out_channels=out_ch,
+                            device=device,
+                            dtype=self.dtype
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        layers = [
+            ResBlock(ch, time_embed_dim, dropout,
+                dims=dims, use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+                use_temporal_conv=temporal_conv,
+                device=device,
+                dtype=self.dtype
+            ),
+            SpatialTransformer(ch, num_heads, dim_head, 
+                depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, 
+                image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
+                device=device,
+                dtype=self.dtype
+            )
+        ]
+        if self.temporal_attention:
+            layers.append(
+                TemporalTransformer(ch, num_heads, dim_head,
+                    depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                    use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, 
+                    causal_attention=use_causal_attention, relative_position=use_relative_position, 
+                    temporal_length=temporal_length,
+                    device=device,
+                    dtype=self.dtype
+                )
+            )
+        layers.append(
+            ResBlock(ch, time_embed_dim, dropout,
+                dims=dims, use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, 
+                use_temporal_conv=temporal_conv,
+                device=device,
+                dtype=self.dtype
+                )
+        )
+
+        ## Middle Block
+        self.middle_block = TimestepEmbedSequential(*layers)
+
+        ## Output Block
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(ch + ich, time_embed_dim, dropout,
+                        out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
+                        use_temporal_conv=temporal_conv,
+                        device=device,
+                        dtype=self.dtype
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    layers.append(
+                        SpatialTransformer(ch, num_heads, dim_head, 
+                            depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                            use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length,
+                            image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
+                            device=device,
+                            dtype=self.dtype
+                        )
+                    )
+                    if self.temporal_attention:
+                        layers.append(
+                            TemporalTransformer(ch, num_heads, dim_head,
+                                depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
+                                use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, 
+                                causal_attention=use_causal_attention, relative_position=use_relative_position, 
+                                temporal_length=temporal_length,
+                                device=device,
+                                dtype=self.dtype
+                            )
+                        )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(ch, time_embed_dim, dropout,
+                            out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                            device=device,
+                            dtype=self.dtype
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+
+        self.out = nn.Sequential(
+            normalization(ch, device=device, dtype=self.dtype),
+            nn.SiLU(),
+            zero_module(
+                operations.conv_nd(
+                    dims, 
+                    model_channels, 
+                    out_channels, 
+                    3, 
+                    padding=1, 
+                    device=device, 
+                    dtype=self.dtype
+                )
+            ),
+        )
+
+    # TODO Add Transformer options to leverage the usage of patches.
+    def forward(
+            self, 
+            x, 
+            timesteps, 
+            context=None, 
+            context_in=None,
+            cc_concat=None,
+            num_video_frames=16,
+            features_adapter=None, 
+            fs=None, 
+            img_emb=None,
+            transformer_options={}, 
+            **kwargs
+        ):
+        
+        if any([fs is None, img_emb is None, cc_concat is None]):
+            raise ValueError("One or more of the required inputs for UNet Forward is None.")
+        
+        transformer_options['original_shape'] = list(x.shape)
+        transformer_options['transformer_index'] = 0
+        transformer_patches = transformer_options.get("patches", {})
+
+        # In ComfyUI, the frames are always with the batch, so we deconstruct it here.
+        # This is mandatory as this is a video based model.
+        # We usually denote "f" as frames, but will use "t" (time) to be consistent with DynamiCrafter.
+        b,_,t,_,_ = x.shape
+        
+        context = context_in
+        cc_concat = cc_concat.to(x.device, x.dtype)
+        x = torch.cat([x, cc_concat], dim=1)
+
+        fs = fs.to(x.device, x.dtype)
+
+        timestep = timesteps
+        context = context_processor(context, num_video_frames, img_emb=img_emb)
+
+        t_emb = timestep_embedding(timestep, self.model_channels, repeat_only=False, dtype=self.dtype)
+        emb = self.time_embed(t_emb)
+        emb = emb.repeat_interleave(repeats=t, dim=0)
+        
+        ## always in shape (b t) c h w, except for temporal layer
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+
+        ## combine emb
+        if self.fs_condition:
+            if fs is None:
+                fs = torch.tensor(
+                    [self.default_fs] * b, dtype=torch.long, device=x.device)
+            fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False, dtype=self.dtype).type(x.dtype)
+
+            fs_embed = self.fps_embedding(fs_emb)
+            fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
+
+            emb = emb + fs_embed
+
+        h = x.type(self.dtype)
+        adapter_idx = 0
+        hs = []
+
+        for id, module in enumerate(self.input_blocks):
+            h = module(h, emb, context=context, batch_size=b)
+            if id ==0 and self.addition_attention:
+                h = self.init_attn(h, emb, context=context, batch_size=b)
+            ## plug-in adapter features
+            if ((id+1)%3 == 0) and features_adapter is not None:
+                h = h + features_adapter[adapter_idx]
+                adapter_idx += 1
+            hs.append(h)
+        if features_adapter is not None:
+            assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
+
+        h = self.middle_block(h, emb, context=context, batch_size=b)
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context=context, batch_size=b)
+        h = h.type(x.dtype)
+        h = self.out(h)
+        
+        # We output with the tensor unfolded framewise, then reshape them to batched using ComfyUI nodes.
+        h = rearrange(h, '(b t) c h w -> b c t h w', t=num_video_frames)
+        
+        return h
\ No newline at end of file
diff --git a/lvdm/modules/x_transformer.py b/lvdm/modules/x_transformer.py
new file mode 100644
index 0000000..5321012
--- /dev/null
+++ b/lvdm/modules/x_transformer.py
@@ -0,0 +1,639 @@
+"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
+from functools import partial
+from inspect import isfunction
+from collections import namedtuple
+from einops import rearrange, repeat
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+# constants
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+    'pre_softmax_attn',
+    'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+    'hiddens',
+    'attn_intermediates'
+])
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.emb = nn.Embedding(max_seq_len, dim)
+        self.init_()
+
+    def init_(self):
+        nn.init.normal_(self.emb.weight, std=0.02)
+
+    def forward(self, x):
+        n = torch.arange(x.shape[1], device=x.device)
+        return self.emb(n)[None, :, :]
+
+
+class FixedPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, x, seq_dim=1, offset=0):
+        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+        return emb[None, :, :]
+
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def always(val):
+    def inner(*args, **kwargs):
+        return val
+    return inner
+
+
+def not_equals(val):
+    def inner(x):
+        return x != val
+    return inner
+
+
+def equals(val):
+    def inner(x):
+        return x == val
+    return inner
+
+
+def max_neg_value(tensor):
+    return -torch.finfo(tensor.dtype).max
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(), dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+
+# classes
+class Scale(nn.Module):
+    def __init__(self, value, fn):
+        super().__init__()
+        self.value = value
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.value, *rest)
+
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.g, *rest)
+
+
+class ScaleNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class Residual(nn.Module):
+    def forward(self, x, residual):
+        return x + residual
+
+
+class GRUGating(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gru = nn.GRUCell(dim, dim)
+
+    def forward(self, x, residual):
+        gated_output = self.gru(
+            rearrange(x, 'b n d -> (b n) d'),
+            rearrange(residual, 'b n d -> (b n) d')
+        )
+
+        return gated_output.reshape_as(x)
+
+
+# feedforward
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+# attention.
+class Attention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_head=DEFAULT_DIM_HEAD,
+            heads=8,
+            causal=False,
+            mask=None,
+            talking_heads=False,
+            sparse_topk=None,
+            use_entmax15=False,
+            num_mem_kv=0,
+            dropout=0.,
+            on_attn=False
+    ):
+        super().__init__()
+        if use_entmax15:
+            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        self.causal = causal
+        self.mask = mask
+
+        inner_dim = dim_head * heads
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(dim, inner_dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+        # talking heads
+        self.talking_heads = talking_heads
+        if talking_heads:
+            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+        # explicit topk sparse attention
+        self.sparse_topk = sparse_topk
+
+        # entmax
+        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+        self.attn_fn = F.softmax
+
+        # add memory key / values
+        self.num_mem_kv = num_mem_kv
+        if num_mem_kv > 0:
+            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+        # attention on attention
+        self.attn_on_attn = on_attn
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            rel_pos=None,
+            sinusoidal_emb=None,
+            prev_attn=None,
+            mem=None
+    ):
+        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+        kv_input = default(context, x)
+
+        q_input = x
+        k_input = kv_input
+        v_input = kv_input
+
+        if exists(mem):
+            k_input = torch.cat((mem, k_input), dim=-2)
+            v_input = torch.cat((mem, v_input), dim=-2)
+
+        if exists(sinusoidal_emb):
+            # in shortformer, the query would start at a position offset depending on the past cached memory
+            offset = k_input.shape[-2] - q_input.shape[-2]
+            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+            k_input = k_input + sinusoidal_emb(k_input)
+
+        q = self.to_q(q_input)
+        k = self.to_k(k_input)
+        v = self.to_v(v_input)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+
+        input_mask = None
+        if any(map(exists, (mask, context_mask))):
+            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+            k_mask = q_mask if not exists(context) else context_mask
+            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+            q_mask = rearrange(q_mask, 'b i -> b () i ()')
+            k_mask = rearrange(k_mask, 'b j -> b () () j')
+            input_mask = q_mask * k_mask
+
+        if self.num_mem_kv > 0:
+            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+            k = torch.cat((mem_k, k), dim=-2)
+            v = torch.cat((mem_v, v), dim=-2)
+            if exists(input_mask):
+                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+        mask_value = max_neg_value(dots)
+
+        if exists(prev_attn):
+            dots = dots + prev_attn
+
+        pre_softmax_attn = dots
+
+        if talking_heads:
+            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+        if exists(rel_pos):
+            dots = rel_pos(dots)
+
+        if exists(input_mask):
+            dots.masked_fill_(~input_mask, mask_value)
+            del input_mask
+
+        if self.causal:
+            i, j = dots.shape[-2:]
+            r = torch.arange(i, device=device)
+            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+            mask = F.pad(mask, (j - i, 0), value=False)
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+            top, _ = dots.topk(self.sparse_topk, dim=-1)
+            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+            mask = dots < vk
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        attn = self.attn_fn(dots, dim=-1)
+        post_softmax_attn = attn
+
+        attn = self.dropout(attn)
+
+        if talking_heads:
+            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+
+        intermediates = Intermediates(
+            pre_softmax_attn=pre_softmax_attn,
+            post_softmax_attn=post_softmax_attn
+        )
+
+        return self.to_out(out), intermediates
+
+
+class AttentionLayers(nn.Module):
+    def __init__(
+            self,
+            dim,
+            depth,
+            heads=8,
+            causal=False,
+            cross_attend=False,
+            only_cross=False,
+            use_scalenorm=False,
+            use_rmsnorm=False,
+            use_rezero=False,
+            rel_pos_num_buckets=32,
+            rel_pos_max_distance=128,
+            position_infused_attn=False,
+            custom_layers=None,
+            sandwich_coef=None,
+            par_ratio=None,
+            residual_attn=False,
+            cross_residual_attn=False,
+            macaron=False,
+            pre_norm=True,
+            gate_residual=False,
+            **kwargs
+    ):
+        super().__init__()
+        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+        self.dim = dim
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        self.has_pos_emb = position_infused_attn
+        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+        self.rotary_pos_emb = always(None)
+
+        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+        self.rel_pos = None
+
+        self.pre_norm = pre_norm
+
+        self.residual_attn = residual_attn
+        self.cross_residual_attn = cross_residual_attn
+
+        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+        norm_class = RMSNorm if use_rmsnorm else norm_class
+        norm_fn = partial(norm_class, dim)
+
+        norm_fn = nn.Identity if use_rezero else norm_fn
+        branch_fn = Rezero if use_rezero else None
+
+        if cross_attend and not only_cross:
+            default_block = ('a', 'c', 'f')
+        elif cross_attend and only_cross:
+            default_block = ('c', 'f')
+        else:
+            default_block = ('a', 'f')
+
+        if macaron:
+            default_block = ('f',) + default_block
+
+        if exists(custom_layers):
+            layer_types = custom_layers
+        elif exists(par_ratio):
+            par_depth = depth * len(default_block)
+            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+            default_block = tuple(filter(not_equals('f'), default_block))
+            par_attn = par_depth // par_ratio
+            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
+            par_width = (depth_cut + depth_cut // par_attn) // par_attn
+            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+            par_block = default_block + ('f',) * (par_width - len(default_block))
+            par_head = par_block * par_attn
+            layer_types = par_head + ('f',) * (par_depth - len(par_head))
+        elif exists(sandwich_coef):
+            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+        else:
+            layer_types = default_block * depth
+
+        self.layer_types = layer_types
+        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+        for layer_type in self.layer_types:
+            if layer_type == 'a':
+                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+            elif layer_type == 'c':
+                layer = Attention(dim, heads=heads, **attn_kwargs)
+            elif layer_type == 'f':
+                layer = FeedForward(dim, **ff_kwargs)
+                layer = layer if not macaron else Scale(0.5, layer)
+            else:
+                raise Exception(f'invalid layer type {layer_type}')
+
+            if isinstance(layer, Attention) and exists(branch_fn):
+                layer = branch_fn(layer)
+
+            if gate_residual:
+                residual_fn = GRUGating(dim)
+            else:
+                residual_fn = Residual()
+
+            self.layers.append(nn.ModuleList([
+                norm_fn(),
+                layer,
+                residual_fn
+            ]))
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            mems=None,
+            return_hiddens=False
+    ):
+        hiddens = []
+        intermediates = []
+        prev_attn = None
+        prev_cross_attn = None
+
+        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+
+        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+            is_last = ind == (len(self.layers) - 1)
+
+            if layer_type == 'a':
+                hiddens.append(x)
+                layer_mem = mems.pop(0)
+
+            residual = x
+
+            if self.pre_norm:
+                x = norm(x)
+
+            if layer_type == 'a':
+                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
+                                   prev_attn=prev_attn, mem=layer_mem)
+            elif layer_type == 'c':
+                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+            elif layer_type == 'f':
+                out = block(x)
+
+            x = residual_fn(out, residual)
+
+            if layer_type in ('a', 'c'):
+                intermediates.append(inter)
+
+            if layer_type == 'a' and self.residual_attn:
+                prev_attn = inter.pre_softmax_attn
+            elif layer_type == 'c' and self.cross_residual_attn:
+                prev_cross_attn = inter.pre_softmax_attn
+
+            if not self.pre_norm and not is_last:
+                x = norm(x)
+
+        if return_hiddens:
+            intermediates = LayerIntermediates(
+                hiddens=hiddens,
+                attn_intermediates=intermediates
+            )
+
+            return x, intermediates
+
+        return x
+
+
+class Encoder(AttentionLayers):
+    def __init__(self, **kwargs):
+        assert 'causal' not in kwargs, 'cannot set causality on encoder'
+        super().__init__(causal=False, **kwargs)
+
+
+
+class TransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            num_tokens,
+            max_seq_len,
+            attn_layers,
+            emb_dim=None,
+            max_mem_len=0.,
+            emb_dropout=0.,
+            num_memory_tokens=None,
+            tie_embedding=False,
+            use_pos_emb=True
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+        dim = attn_layers.dim
+        emb_dim = default(emb_dim, dim)
+
+        self.max_seq_len = max_seq_len
+        self.max_mem_len = max_mem_len
+        self.num_tokens = num_tokens
+
+        self.token_emb = nn.Embedding(num_tokens, emb_dim)
+        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.emb_dropout = nn.Dropout(emb_dropout)
+
+        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+
+        self.init_()
+
+        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+        # memory tokens (like [cls]) from Memory Transformers paper
+        num_memory_tokens = default(num_memory_tokens, 0)
+        self.num_memory_tokens = num_memory_tokens
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+            # let funnel encoder know number of memory tokens, if specified
+            if hasattr(attn_layers, 'num_memory_tokens'):
+                attn_layers.num_memory_tokens = num_memory_tokens
+
+    def init_(self):
+        nn.init.normal_(self.token_emb.weight, std=0.02)
+
+    def forward(
+            self,
+            x,
+            return_embeddings=False,
+            mask=None,
+            return_mems=False,
+            return_attn=False,
+            mems=None,
+            **kwargs
+    ):
+        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+        x = self.token_emb(x)
+        x += self.pos_emb(x)
+        x = self.emb_dropout(x)
+
+        x = self.project_emb(x)
+
+        if num_mem > 0:
+            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+            x = torch.cat((mem, x), dim=1)
+
+            # auto-handle masking after appending memory tokens
+            if exists(mask):
+                mask = F.pad(mask, (num_mem, 0), value=True)
+
+        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x = self.norm(x)
+
+        mem, x = x[:, :num_mem], x[:, num_mem:]
+
+        out = self.to_logits(x) if not return_embeddings else x
+
+        if return_mems:
+            hiddens = intermediates.hiddens
+            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
+            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+            return out, new_mems
+
+        if return_attn:
+            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            return out, attn_maps
+
+        return out
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..3fff8ef
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,370 @@
+import os
+import torch
+import comfy
+import yaml
+import folder_paths
+
+from einops import rearrange
+from comfy import model_base, model_management, model_detection, latent_formats, model_sampling
+from .lvdm.modules.networks.openaimodel3d import UNetModel as DynamiCrafterUNetModel
+
+from .utils.model_utils import DynamiCrafterBase, DYNAMICRAFTER_CONFIG, \
+    load_image_proj_dict, load_dynamicrafter_dict, get_image_proj_model, load_vae_dict
+
+from .utils.utils import get_models_directory
+
+MODEL_DIR= "dynamicrafter_models"
+MODEL_DIR_PATH = os.path.join(folder_paths.models_dir, MODEL_DIR)
+
+class DynamiCrafterProcessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "clip_vision": ("CLIP_VISION", ),
+                "vae": ("VAE", ),
+                "image_proj_model": ("IMAGE_PROJ_MODEL", ),
+                "images": ("IMAGE", ),
+                "use_interpolate": ("BOOLEAN", {"default": False}),
+                "fps": ("INT", {"default": 15, "min": 1, "max": 30, "step": 1}, ),
+                "frames": ("INT", {"default": 16})
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "LATENT", "LATENT", )
+    RETURN_NAMES = ("model", "empty_latent", "latent_img", )
+
+    FUNCTION = "process_image_conditioning"
+
+    def __init__(self):
+        self.model_patcher = None
+
+    # There is probably a better way to do this, but with the apply_model callback, this seems necessary.
+    # The model gets wrapped around a CFG Denoiser class, and handles the conditioning parts there.
+    # We cannot access it, so we must find the conditioning according to how ComfyUI handles it.
+    def get_conditioning_pair(self, c_crossattn, use_cfg: bool):
+        if not use_cfg:
+            return c_crossattn
+
+        conditioning_group = []
+
+        for i in range(c_crossattn.shape[0]):
+            # Get the positive and negative conditioning.
+            positive_idx = i + 1
+            negative_idx = i
+
+            if positive_idx >= c_crossattn.shape[0]:
+                break
+
+            if not torch.equal(c_crossattn[[positive_idx]], c_crossattn[[negative_idx]]):
+                conditioning_group = [
+                    c_crossattn[[positive_idx]], 
+                    c_crossattn[[negative_idx]]
+                ]
+                break
+
+        if len(conditioning_group) == 0:
+            raise ValueError("Could not get the appropriate conditioning group.")
+
+        return torch.cat(conditioning_group)
+
+    # apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}
+    def _forward(self, *args):
+        transformer_options = self.model_patcher.model_options['transformer_options']
+        conditioning = transformer_options['conditioning']
+      
+        apply_model = args[0]
+
+        # forward_dict
+        fd = args[1]
+        
+        x, t, model_in_kwargs, _ =  fd['input'], fd['timestep'], fd['c'], fd['cond_or_uncond']
+        
+        c_crossattn = model_in_kwargs.pop("c_crossattn")
+        c_concat = conditioning['c_concat']
+        num_video_frames = conditioning['num_video_frames']
+        fs = conditioning['fs']
+
+        
+        original_num_frames = num_video_frames
+
+        # Better way to determine if we're using CFG
+        # The cond batch will always be num_frames >= 2 since we're doing video,
+        # so we need get this condition differently here. 
+        if x.shape[0] > num_video_frames:
+            num_video_frames *= 2
+            batch_size = 2
+            use_cfg = True
+        else:
+            use_cfg = False
+            batch_size = 1
+        
+        if use_cfg:
+            c_concat = torch.cat([c_concat] * 2)
+
+        self.validate_forwardable_latent(x, c_concat, num_video_frames, use_cfg)
+
+        x_in, c_concat = map(lambda xc: rearrange(xc, '(b t) c h w -> b c t h w', b=batch_size), (x, c_concat))
+        
+        # We always assume video, so there will always be batched conditionings.
+        c_crossattn = self.get_conditioning_pair(c_crossattn, use_cfg)
+        c_crossattn = c_crossattn[:2] if use_cfg else c_crossattn[:1]
+        context_in = c_crossattn
+
+        img_embs = conditioning['image_emb']
+
+        if use_cfg:
+            img_emb_uncond = conditioning['image_emb_uncond']
+            img_embs = torch.cat([img_embs, img_emb_uncond])
+
+        fs = torch.cat([fs] * x_in.shape[0])
+        
+        outs = []
+        for i in range(batch_size):
+            x_out = apply_model(
+                    x_in[[i]], 
+                    t=torch.cat([t[:1]]),
+                    context_in=context_in[[i]],
+                    c_crossattn=c_crossattn, 
+                    cc_concat=c_concat[[i]], # "cc" is to handle naming conflict with apply_model wrapper. 
+                    # We want to handle this in the UNet forward.
+                    num_video_frames=num_video_frames // 2 if batch_size > 1 else num_video_frames, 
+                    img_emb=img_embs[[i]],
+                    fs=fs[[i]],
+                    **model_in_kwargs
+                )
+            outs.append(x_out)
+               
+        x_out = torch.cat(list(reversed(outs)))
+        x_out = rearrange(x_out, 'b c t h w -> (b t) c h w')
+
+        return x_out
+
+    def assign_forward_args(
+        self, 
+        model, 
+        c_concat, 
+        image_emb, 
+        image_emb_uncond, 
+        fs, 
+        frames,
+    ):
+        model.model_options['transformer_options']['conditioning'] = {
+            "c_concat": model.model.process_latent_in(c_concat),
+            "image_emb": image_emb,
+            'image_emb_uncond': image_emb_uncond,
+            "fs": fs,
+            "num_video_frames": frames,
+        }
+
+    def validate_forwardable_latent(self, latent, c_concat, num_video_frames, use_cfg):
+        check_no_cfg = latent.shape[0] != num_video_frames
+        check_with_cfg = latent.shape[0] != (num_video_frames * 2)
+
+        latent_batch_size = latent.shape[0] if not use_cfg else latent.shape[0] // 2
+        num_frames = num_video_frames if not use_cfg else num_video_frames // 2
+
+        if all([check_no_cfg, check_with_cfg]):
+            raise ValueError(
+                "Please make sure your latent inputs match the number of frames in the DynamiCrafter Processor."
+                f"Got a latent batch size of ({latent_batch_size}) with number of frames being ({num_frames})."
+            )
+        
+        latent_h, latent_w = latent.shape[-2:]
+        c_concat_h, c_concat_w = c_concat.shape[-2:]
+
+        if not all([latent_h == c_concat_h, latent_w == c_concat_w]):
+            raise ValueError(
+                "Please make sure that your input latent and image frames are the same height and width.",
+                f"Image Size: {c_concat_w * 8}, {c_concat_h * 8}, Latent Size: {latent_h * 8}, {latent_w * 8}"
+            )
+
+    def process_image_conditioning(
+        self, 
+        model, 
+        clip_vision, 
+        vae, 
+        image_proj_model, 
+        images, 
+        use_interpolate,
+        fps: int,
+        frames: int
+    ):
+        self.model_patcher = model
+        encoded_latent = vae.encode(images[:, :, :, :3])
+
+        encoded_image = clip_vision.encode_image(images[:1])['last_hidden_state']
+        image_emb = image_proj_model(encoded_image)
+
+        encoded_image_uncond  = clip_vision.encode_image(torch.zeros_like(images)[:1])['last_hidden_state']
+        image_emb_uncond = image_proj_model(encoded_image_uncond)
+
+        c_concat = encoded_latent
+        fs = torch.tensor([fps], dtype=torch.long, device=model_management.intermediate_device())
+        
+        model.set_model_unet_function_wrapper(self._forward)
+
+        used_interpolate_processing = False
+
+        if use_interpolate and frames > 16:
+            raise ValueError(
+                "When using interpolation mode, the maximum amount of frames are 16."
+                "If you're doing long video generation, consider using the last frame\
+                     from the first generation for the next one (autoregressive)."
+            )
+        if encoded_latent.shape[0] == 1:
+            c_concat = torch.cat([c_concat] * frames, dim=0)[:frames]
+        
+            if use_interpolate:
+                mask = torch.zeros_like(c_concat)
+                mask[:1] = c_concat[:1]
+                c_concat = mask
+
+                used_interpolate_processing = True
+        else:
+            if use_interpolate and c_concat.shape[0] in [2, 3]:
+                input_frame_count = c_concat.shape[0]
+
+                # We're just padding to the same type an size of the concat
+                masked_frames = torch.zeros_like(torch.cat([c_concat[:1]] * frames))[:frames]
+
+                # Start frame
+                masked_frames[:1] = c_concat[:1]
+
+                end_frame_idx = -1
+
+                # TODO
+                speed = 1.0
+                if speed < 1.0:
+                    possible_speeds = list(torch.linspace(0, 1.0, c_concat.shape[0]))
+                    speed_from_frames = enumerate(possible_speeds)
+                    speed_idx = min(speed_from_frames, key=lambda n: n[1] - speed)[0]
+                    end_frame_idx = speed_idx
+
+                # End frame
+                masked_frames[-1:] = c_concat[[end_frame_idx]]
+                
+                # Possible middle frame, but not working at the moment.
+                if input_frame_count == 3:
+                    middle_idx = masked_frames.shape[0] // 2
+                    middle_idx_frame = c_concat.shape[0] // 2
+                    masked_frames[[middle_idx]] = c_concat[[middle_idx_frame]]
+
+                c_concat = masked_frames
+                used_interpolate_processing = True
+
+                print(f"Using interpolation mode with {input_frame_count} frames.")
+
+            if c_concat.shape[0] < frames and not used_interpolate_processing:
+                print(
+                    "Multiple images found, but interpolation mode is unset. Using the first frame as condition.",
+                )
+                c_concat = torch.cat([c_concat[:1]] * frames)
+
+        c_concat = c_concat[:frames]
+        
+        if encoded_latent.shape[0] == 1:
+            encoded_latent = torch.cat([encoded_latent] * frames)[:frames]
+
+        if encoded_latent.shape[0] < frames and encoded_latent.shape[0] != 1:
+            encoded_latent = torch.cat(
+                [encoded_latent] + [encoded_latent[-1:]] * abs(encoded_latent.shape[0] - frames)
+            )[:frames]
+        
+        # We could store this as a state in this Node Class Instance, but to prevent any weird edge cases,
+        # this should always be passed through the 'stateless' way, and let ComfyUI handle the transformer_options state.
+        self.assign_forward_args(model, c_concat, image_emb, image_emb_uncond, fs, frames)
+        
+        return (model, {"samples": torch.zeros_like(c_concat)}, {"samples": encoded_latent},)
+
+class DynamiCrafterLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_path": (get_models_directory(os.listdir(MODEL_DIR_PATH)), ),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "IMAGE_PROJ_MODEL", )
+    RETURN_NAMES = ("model", "image_proj_model", )
+    FUNCTION = "load_dynamicrafter"
+    
+    def load_model_sicts(self, model_path: str):
+        model_state_dict = comfy.utils.load_torch_file(model_path)
+        dynamicrafter_dict = load_dynamicrafter_dict(model_state_dict)
+        image_proj_dict = load_image_proj_dict(model_state_dict)
+
+        return dynamicrafter_dict, image_proj_dict
+
+    def get_prediction_type(self, is_eps: bool, model_config):
+        if not is_eps and "image_cross_attention_scale_learnable" in model_config.unet_config.keys():
+                model_config.unet_config["image_cross_attention_scale_learnable"] = False
+
+        return model_base.ModelType.EPS if is_eps else model_base.ModelType.V_PREDICTION
+
+    def handle_model_management(self, dynamicrafter_dict: dict, model_config):
+        parameters = comfy.utils.calculate_parameters(dynamicrafter_dict, "model.diffusion_model.")
+        load_device = model_management.get_torch_device()
+        unet_dtype = model_management.unet_dtype(
+            model_params=parameters, 
+            supported_dtypes=model_config.supported_inference_dtypes
+        )
+        manual_cast_dtype = model_management.unet_manual_cast(
+            unet_dtype, 
+            load_device, 
+            model_config.supported_inference_dtypes
+        )
+        model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+        inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
+        offload_device = model_management.unet_offload_device()
+
+        return load_device, inital_load_device
+
+    def check_leftover_keys(self, state_dict: dict):
+        left_over = state_dict.keys()
+        if len(left_over) > 0:
+            print("left over keys:", left_over)
+
+    def load_dynamicrafter(self, model_path):
+        model_path = os.path.join(MODEL_DIR_PATH, model_path)
+        
+        if os.path.exists(model_path):
+            dynamicrafter_dict, image_proj_dict = self.load_model_sicts(model_path)
+            model_config = DynamiCrafterBase(DYNAMICRAFTER_CONFIG)
+
+            dynamicrafter_dict, is_eps = model_config.process_dict_version(state_dict=dynamicrafter_dict)
+
+            MODEL_TYPE = self.get_prediction_type(is_eps, model_config)
+            load_device, inital_load_device = self.handle_model_management(dynamicrafter_dict, model_config)
+
+            model = model_base.BaseModel(
+                model_config, 
+                model_type=MODEL_TYPE, 
+                device=inital_load_device, 
+                unet_model=DynamiCrafterUNetModel
+            )
+
+            image_proj_model = get_image_proj_model(image_proj_dict)
+            model.load_model_weights(dynamicrafter_dict, "model.diffusion_model.")
+            self.check_leftover_keys(dynamicrafter_dict)
+
+            model_patcher = comfy.model_patcher.ModelPatcher(
+                model, 
+                load_device=load_device, 
+                offload_device=model_management.unet_offload_device(), 
+                current_device=inital_load_device
+            )
+    
+        return (model_patcher, image_proj_model, )
+
+NODE_CLASS_MAPPINGS = {
+    "DynamiCrafterLoader": DynamiCrafterLoader,
+    "DynamiCrafterProcessor": DynamiCrafterProcessor,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DynamiCrafterLoader": "Load a DynamiCrafter Checkpoint",    
+    "DynamiCrafterProcessor": "Apply DynamiCrafter",
+}
\ No newline at end of file
diff --git a/utils/model_utils.py b/utils/model_utils.py
new file mode 100644
index 0000000..adc1be4
--- /dev/null
+++ b/utils/model_utils.py
@@ -0,0 +1,142 @@
+
+import torch
+
+from collections import OrderedDict
+
+from comfy import model_base
+from comfy import utils
+
+from comfy import sd2_clip
+
+from comfy import supported_models_base
+from comfy import latent_formats
+
+from ..lvdm.modules.encoders.resampler import Resampler
+
+DYNAMICRAFTER_CONFIG = {
+    'in_channels': 8,
+    'out_channels': 4,
+    'model_channels': 320,
+    'attention_resolutions': [4, 2, 1],
+    'num_res_blocks': 2,
+    'channel_mult': [1, 2, 4, 4],
+    'num_head_channels': 64,
+    'transformer_depth': 1,
+    'context_dim': 1024,
+    'use_linear': True,
+    'use_checkpoint': False,
+    'temporal_conv': True,
+    'temporal_attention': True,
+    'temporal_selfatt_only': True,
+    'use_relative_position': False,
+    'use_causal_attention': False,
+    'temporal_length': 16,
+    'addition_attention': True,
+    'image_cross_attention': True,
+    'image_cross_attention_scale_learnable': True,
+    'default_fs': 3,
+    'fs_condition': True
+}
+
+IMAGE_PROJ_CONFIG = {
+    "dim": 1024,
+    "depth": 4,
+    "dim_head": 64,
+    "heads": 12,
+    "num_queries": 16,
+    "embedding_dim": 1280,
+    "output_dim": 1024,
+    "ff_mult": 4,
+    "video_length": 16
+}
+
+def process_list_or_str(target_key_or_keys, k):
+    if isinstance(target_key_or_keys, list):
+        return any([list_k in k for list_k in target_key_or_keys])
+    else:
+        return target_key_or_keys in k
+
+def simple_state_dict_loader(state_dict: dict, target_key: str, target_dict: dict = None):
+    out_dict = {}
+    
+    if target_dict is None:
+        for k, v in state_dict.items():
+            if process_list_or_str(target_key, k):
+                out_dict[k] = v
+    else:
+        for k, v in target_dict.items():
+            out_dict[k] = state_dict[k]
+            
+    return out_dict
+
+def load_image_proj_dict(state_dict: dict):
+    return simple_state_dict_loader(state_dict, 'image_proj')
+
+def load_dynamicrafter_dict(state_dict: dict):
+    return simple_state_dict_loader(state_dict, 'model.diffusion_model')
+
+def load_vae_dict(state_dict: dict):
+    return simple_state_dict_loader(state_dict, 'first_stage_model')
+
+def get_base_model(state_dict: dict, version_checker=False):
+
+    is_256_model = False
+
+    for k in state_dict.keys():
+         if "framestride_embed" in k:
+            is_256_model = True
+            break
+
+def get_image_proj_model(state_dict: dict):
+
+    state_dict = {k.replace('image_proj_model.', ''): v for k, v in state_dict.items()}
+    #target_dict = Resampler().state_dict()
+
+    ImageProjModel = Resampler(**IMAGE_PROJ_CONFIG)
+    ImageProjModel.load_state_dict(state_dict)
+
+    print("Image Projection Model loaded successfully")
+    #del target_dict
+    return ImageProjModel
+
+class DynamiCrafterBase(supported_models_base.BASE):
+    unet_config = {}
+    unet_extra_config = {}
+
+    latent_format = latent_formats.SD15
+
+    def process_clip_state_dict(self, state_dict):
+        replace_prefix = {}
+        replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
+        replace_prefix["cond_stage_model.model."] = "clip_h."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
+        return state_dict
+
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {}
+        replace_prefix["clip_h"] = "cond_stage_model.model"
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
+        state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
+        return state_dict
+
+    def clip_target(self):
+        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
+
+    def process_dict_version(self, state_dict: dict):
+        processed_dict = OrderedDict()
+        is_eps = False
+        
+        for k in list(state_dict.keys()):
+            if "framestride_embed" in k:
+                new_key = k.replace("framestride_embed", "fps_embedding")
+                processed_dict[new_key] = state_dict[k]
+                is_eps = True
+                continue
+            
+            processed_dict[k] = state_dict[k]
+
+        return processed_dict, is_eps
+
+
+
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000..d6e3a66
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,82 @@
+import importlib
+import numpy as np
+import cv2
+import torch
+import torch.distributed as dist
+
+MODEL_EXTS = ['ckpt', 'safetensors', 'bin']
+
+def get_models_directory(directory: list):
+    files_list = list(filter(lambda f: f.split(".")[-1] in MODEL_EXTS, directory))
+    return files_list
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+    return total_params
+
+
+def check_istarget(name, para_list):
+    """ 
+    name: full name of source para
+    para_list: partial name of target para 
+    """
+    istarget=False
+    for para in para_list:
+        if para in name:
+            return True
+    return istarget
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == '__is_first_stage__':
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def load_npz_from_dir(data_dir):
+    data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)]
+    data = np.concatenate(data, axis=0)
+    return data
+
+
+def load_npz_from_paths(data_paths):
+    data = [np.load(data_path)['arr_0'] for data_path in data_paths]
+    data = np.concatenate(data, axis=0)
+    return data   
+
+
+def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None):
+    h, w = image.shape[:2]
+    if resize_short_edge is not None:
+        k = resize_short_edge / min(h, w)
+    else:
+        k = max_resolution / (h * w)
+        k = k**0.5
+    h = int(np.round(h * k / 64)) * 64
+    w = int(np.round(w * k / 64)) * 64
+    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
+    return image
+
+
+def setup_dist(args):
+    if dist.is_initialized():
+        return
+    torch.cuda.set_device(args.local_rank)
+    torch.distributed.init_process_group(
+        'nccl',
+        init_method='env://'
+    )
\ No newline at end of file