diff --git a/opensora/models/diffusion/latte/configuration_latte.py b/opensora/models/diffusion/latte/configuration_latte.py
new file mode 100644
index 000000000..0aaaa9d83
--- /dev/null
+++ b/opensora/models/diffusion/latte/configuration_latte.py
@@ -0,0 +1,92 @@
+from typing import Union, Tuple
+
+from ..configuration_base import BaseConfiguration
+
+
+class LatteConfiguration(BaseConfiguration):
+
+    def __init__(
+        self,
+        input_size=32,
+        patch_size=2,
+        patch_size_t=1,
+        in_channels=4,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        num_frames=16,
+        class_dropout_prob=0.1,
+        num_classes=1000,
+        learn_sigma=True,
+        extras=1,
+        attention_mode='math',
+        attention_pe_mode=None,
+        pt_input_size: Union[int, Tuple[int, int]] = None,  # (h, w)
+        intp_vfreq: bool = True,  # vision position interpolation
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.input_size = input_size
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.in_channels = in_channels
+        self.hidden_size = hidden_size
+        self.depth = depth
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.num_frames = num_frames
+        self.class_dropout_prob = class_dropout_prob
+        self.num_classes = num_classes
+        self.learn_sigma = learn_sigma
+        self.extras = learn_sigma
+        self.attention_mode = attention_mode
+        self.attention_pe_mode = attention_pe_mode
+        self.pt_input_size = pt_input_size
+        self.intp_vfreq = intp_vfreq
+
+
+def Latte_XL_122_Config(**kwargs):
+    return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
+
+def Latte_XL_144_Config(**kwargs):
+    return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
+
+def Latte_XL_188_Config(**kwargs):
+    return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
+
+def Latte_L_122_Config(**kwargs):
+    return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
+
+def Latte_L_144_Config(**kwargs):
+    return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
+
+def Latte_L_188_Config(**kwargs):
+    return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
+
+def Latte_B_122_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=2, num_heads=12, **kwargs)
+
+def Latte_B_144_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=4, num_heads=12, **kwargs)
+
+def Latte_B_188_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=8, num_heads=12, **kwargs)
+
+def Latte_S_122_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=2, num_heads=6, **kwargs)
+
+def Latte_S_144_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=4, num_heads=6, **kwargs)
+
+def Latte_S_188_Config(**kwargs):
+    return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=8, num_heads=6, **kwargs)
+
+
+Latte_configs = {
+    "Latte-XL/122": Latte_XL_122_Config, "Latte-XL/144": Latte_XL_144_Config, "Latte-XL/188": Latte_XL_188_Config,
+    "Latte-L/122": Latte_L_122_Config, "Latte-L/144": Latte_L_144_Config, "Latte-L/188": Latte_L_188_Config,
+    "Latte-B/122": Latte_B_122_Config, "Latte-B/144": Latte_B_144_Config, "Latte-B/188": Latte_B_188_Config,
+    "Latte-S/122": Latte_S_122_Config, "Latte-S/144": Latte_S_144_Config, "Latte-S/188": Latte_S_188_Config,
+}
diff --git a/opensora/models/diffusion/latte/modeling_latte.py b/opensora/models/diffusion/latte/modeling_latte.py
new file mode 100644
index 000000000..1b9018e0b
--- /dev/null
+++ b/opensora/models/diffusion/latte/modeling_latte.py
@@ -0,0 +1,647 @@
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+import math
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+import numpy as np
+from torch.nn import functional as F
+from einops import rearrange, repeat
+from typing import Tuple, Union
+from timm.layers import to_2tuple
+from timm.models.vision_transformer import Mlp, PatchEmbed
+
+from .VisionRoPE import VisionRotaryEmbeddingFast
+
+try:
+    # needs to have https://github.com/corl-team/rebased/ installed
+    from fla.ops.triton.rebased_fast import parallel_rebased
+except:
+    REBASED_IS_AVAILABLE = False
+
+try:
+    # needs to have https://github.com/lucidrains/ring-attention-pytorch installed
+    from ring_attention_pytorch.ring_flash_attention_cuda import ring_flash_attn_cuda
+except:
+    RING_ATTENTION_IS_AVAILABLE = False
+
+from .configuration_latte import LatteConfiguration
+
+# from timm.models.layers.helpers import to_2tuple
+# from timm.models.layers.trace_utils import _assert
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+#################################################################################
+#               Attention Layers from TIMM                                      #
+#################################################################################
+
+class Attention(nn.Module):
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 attn_drop=0.,
+                 proj_drop=0.,
+                 use_lora=False,
+                 attention_mode='math',
+                 eps=1e-12,
+                 causal=True,
+                 ring_bucket_size=1024,
+                 attention_pe_mode=None,
+                 hw: Union[int, Tuple[int, int]] = 16,  # (h, w)
+                 pt_hw: Union[int, Tuple[int, int]] = 16,  # (h, w)
+                 intp_vfreq: bool = True,  # vision position interpolation
+                 ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+        self.attention_mode = attention_mode
+        self.attention_pe_mode = attention_pe_mode
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.eps = eps
+        self.causal = causal
+        self.ring_bucket_size = ring_bucket_size
+
+        if self.attention_pe_mode == '2d_rope':
+            half_head_dim = dim // num_heads // 2
+            self.hw = to_2tuple(hw)
+            self.rope = VisionRotaryEmbeddingFast(
+                dim=half_head_dim,
+                pt_hw=to_2tuple(pt_hw),
+                ft_hw=self.hw if intp_vfreq else None,
+            )
+
+    def forward(self, x, attn_mask):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
+        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple) b h n c
+        if attn_mask is not None:
+            attn_mask = attn_mask.repeat(1, self.num_heads, 1, 1).to(q.dtype)
+
+        if self.attention_pe_mode == '2d_rope':
+            q_t = q.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
+            ro_q_t = self.rope(q_t)
+            q = ro_q_t.view(B, self.num_heads, N, C // self.num_heads)
+
+            k_t = k.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
+            ro_k_t = self.rope(k_t)
+            k = ro_k_t.view(B, self.num_heads, N, C // self.num_heads)
+
+        if self.attention_mode == 'xformers': # cause loss nan while using with amp
+            with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True):
+                x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
+                                                   dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C) # require pytorch 2.0
+
+        elif self.attention_mode == 'flash':
+            # cause loss nan while using with amp
+            # Optionally use the context manager to ensure one of the fused kerenels is run
+            with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False):
+                x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
+                                                   dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C) # require pytorch 2.0
+
+        elif self.attention_mode == 'math':
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            if attn_mask is not None:
+                attn_bias = self.make_attn_bias(attn_mask)
+                attn = attn + attn_bias
+            attn = attn.softmax(dim=-1)
+            if torch.any(torch.isnan(attn)):
+                print('torch.any(torch.isnan(attn))')
+                attn = attn.masked_fill(torch.isnan(attn), float(0.))
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+
+        elif self.attention_mode == 'rebased':
+            x = parallel_rebased(q, k, v, self.eps, True, True).reshape(B, N, C)
+
+        elif self.attention_mode == 'ring':
+            x = ring_flash_attn_cuda(q, k, v, causal=self.causal, bucket_size=self.ring_bucket_size).reshape(B, N, C)
+
+        else:
+            raise NotImplemented
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def make_attn_bias(self, attn_mask):
+        attn_bias = torch.where(attn_mask == 0, -1e8 if attn_mask.dtype == torch.float32 else 1e-4, attn_mask)
+        attn_bias = torch.where(attn_mask == 1, 0., attn_bias)
+        return attn_bias
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: 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, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, 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)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class LabelEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+#################################################################################
+#                                 Core Latte Model                                #
+#################################################################################
+
+class TransformerBlock(nn.Module):
+    """
+    A Latte tansformer block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+        )
+
+    def forward(self, x, c, attn_bias):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), attn_bias)
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of Latte.
+    """
+    def __init__(self, hidden_size, patch_size_t, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size_t * patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class Latte(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(self, config: LatteConfiguration):
+        super().__init__()
+
+        input_size = config.input_size
+        patch_size = config.patch_size
+        patch_size_t = config.patch_size_t
+        in_channels = config.in_channels
+        hidden_size = config.hidden_size
+        depth = config.depth
+        num_heads = config.num_heads
+        mlp_ratio = config.mlp_ratio
+        num_frames = config.num_frames
+        class_dropout_prob = config.class_dropout_prob
+        num_classes = config.num_classes
+        learn_sigma = config.learn_sigma
+        extras = config.learn_sigma
+        attention_mode = config.attention_mode
+        attention_pe_mode = config.attention_pe_mode
+        pt_input_size = config.pt_input_size
+        intp_vfreq = config.intp_vfreq
+
+        self.config = config
+
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.num_heads = num_heads
+        self.extras = extras
+        self.num_frames = num_frames
+        self.hidden_size = hidden_size
+        self.gradient_checkpointing = False
+
+        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+
+        if self.extras == 2:
+            self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
+        if self.extras == 78: # timestep + text_embedding
+            self.text_embedding_projection = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(77 * 768, hidden_size, bias=True)
+        )
+
+        num_patches = self.x_embedder.num_patches
+        # Will use fixed sin-cos embedding:
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
+        self.temp_embed = nn.Parameter(torch.zeros(1, num_frames, hidden_size), requires_grad=False)
+        self.hidden_size = hidden_size
+
+        if pt_input_size is None:
+            pt_input_size = input_size
+        self.blocks = nn.ModuleList([
+            TransformerBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode,
+                             attention_pe_mode=attention_pe_mode, hw=input_size, pt_hw=pt_input_size,
+                             intp_vfreq=intp_vfreq
+                             ) for _ in range(depth)
+        ])
+
+        self.final_layer = FinalLayer(hidden_size, patch_size_t, patch_size, self.out_channels)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding:
+        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
+        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        temp_embed = get_1d_sincos_temp_embed(self.temp_embed.shape[-1], self.temp_embed.shape[-2])
+        self.temp_embed.data.copy_(torch.from_numpy(temp_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        if self.extras == 2:
+            # Initialize label embedding table:
+            nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in Latte blocks:
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        h = w = int(x.shape[1] ** 0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
+        return imgs
+
+    def ckpt_wrapper(self, module):
+        def ckpt_forward(*inputs):
+            outputs = module(*inputs)
+            return outputs
+        return ckpt_forward
+
+    def make_mask(self, attention_mask):
+        attention_mask = attention_mask.flatten(1).unsqueeze(-1)  # bs t h w -> bs thw 1
+        attention_mask = attention_mask @ attention_mask.transpose(1, 2)  # bs thw 1 @ bs 1 thw = bs thw thw
+        attention_mask = attention_mask.unsqueeze(1)
+        return attention_mask
+
+    # @torch.cuda.amp.autocast()
+    # @torch.compile
+    def forward(self, 
+                x, 
+                t, 
+                y=None, 
+                text_embedding=None,
+                attention_mask=None):
+        """
+        Forward pass of Latte.
+        x: (N, F, C, H, W) tensor of video inputs
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        attention_mask: (N, F, H, W)
+        """
+        attention_mask_temproal, attention_mask_spatial = None, None
+        if attention_mask is not None:
+            attention_mask_spatial = rearrange(attention_mask, 'b t h w -> (b t) h w')
+            attention_mask_spatial = self.make_mask(attention_mask_spatial)
+
+            attention_mask_temproal = rearrange(attention_mask, 'b t h w -> (b h w) t')
+            attention_mask_temproal = self.make_mask(attention_mask_temproal)
+
+        batches, frames, channels, high, weight = x.shape
+
+        x = rearrange(x, 'b f c h w -> (b f) c h w')
+
+        x = self.x_embedder(x) + self.pos_embed
+        t = self.t_embedder(t)
+
+
+        timestep_spatial = repeat(t, 'n d -> (n c) d', c=self.temp_embed.shape[1])
+        timestep_temp = repeat(t, 'n d -> (n c) d', c=self.pos_embed.shape[1])
+
+        if self.extras == 2:
+            y = self.y_embedder(y, self.training)
+            y_spatial = repeat(y, 'n d -> (n c) d', c=self.temp_embed.shape[1])
+            y_temp = repeat(y, 'n d -> (n c) d', c=self.pos_embed.shape[1])
+        elif self.extras == 78:
+            text_embedding = self.text_embedding_projection(text_embedding.reshape(batches, -1))
+            text_embedding_spatial = repeat(text_embedding, 'n d -> (n c) d', c=self.temp_embed.shape[1])
+            text_embedding_temp = repeat(text_embedding, 'n d -> (n c) d', c=self.pos_embed.shape[1])
+
+        for i in range(0, len(self.blocks), 2):
+            spatial_block, temp_block = self.blocks[i:i+2]
+            if self.extras == 2:
+                c = timestep_spatial + y_spatial
+            elif self.extras == 78:
+                c = timestep_spatial + text_embedding_spatial
+            else:
+                c = timestep_spatial
+            if self.gradient_checkpointing and self.training:
+                x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(spatial_block), x, c, attention_mask_spatial)
+            else:
+                x = spatial_block(x, c, attention_mask_spatial)
+
+            x = rearrange(x, '(b f) t d -> (b t) f d', b=batches)
+            # Add Time Embedding
+            if i == 0:
+                x = x + self.temp_embed
+
+            if self.extras == 2:
+                c = timestep_temp + y_temp
+            elif self.extras == 78:
+                c = timestep_temp + text_embedding_temp
+            else:
+                c = timestep_temp
+
+            if self.gradient_checkpointing and self.training:
+                x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(temp_block), x, c, attention_mask_temproal)
+            else:
+                x = temp_block(x, c, attention_mask_temproal)
+            x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
+
+        if self.extras == 2:
+            c = timestep_spatial + y_spatial
+        else:
+            c = timestep_spatial
+        x = self.final_layer(x, c)               
+        x = self.unpatchify(x)                  
+        x = rearrange(x, '(b f) c h w -> b f c h w', b=batches)
+        return x
+
+    def forward_with_cfg(self, x, t, y=None, cfg_scale=7.0, text_embedding=None, attention_mask=None):
+        """
+        Forward pass of Latte, but also batches the unconditional forward pass for classifier-free guidance.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[: len(x) // 2]
+        combined = torch.cat([half, half], dim=0)
+        model_out = self.forward(combined, t, y=y, text_embedding=text_embedding, attention_mask=attention_mask)
+        # For exact reproducibility reasons, we apply classifier-free guidance on only
+        # three channels by default. The standard approach to cfg applies it to all channels.
+        # This can be done by uncommenting the following line and commenting-out the line following that.
+        eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        # eps, rest = model_out[:, :3], model_out[:, 3:]
+        # eps, rest = model_out[:, :, :4, ...], model_out[:, :, 4:, ...]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
+        eps = torch.cat([half_eps, half_eps], dim=0) 
+        return torch.cat([eps, rest], dim=2)
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+def get_1d_sincos_temp_embed(embed_dim, length):
+    pos = torch.arange(0, length).unsqueeze(1)
+    return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) 
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) 
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega 
+
+    pos = pos.reshape(-1)  
+    out = np.einsum('m,d->md', pos, omega) 
+
+    emb_sin = np.sin(out) 
+    emb_cos = np.cos(out) 
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1) 
+    return emb
+
+
+#################################################################################
+#                                   Latte Configs                                  #
+#################################################################################
+
+from .configuration_latte import (
+    Latte_XL_122_Config, Latte_XL_144_Config, Latte_XL_188_Config,
+    Latte_L_122_Config, Latte_L_144_Config, Latte_L_188_Config,
+    Latte_B_122_Config, Latte_B_144_Config, Latte_B_188_Config,
+    Latte_S_122_Config, Latte_S_144_Config, Latte_S_188_Config,
+)
+
+def Latte_XL_122(**kwargs):
+    return Latte(Latte_XL_122_Config(**kwargs))
+
+def Latte_XL_144(**kwargs):
+    return Latte(Latte_XL_144_Config(**kwargs))
+
+def Latte_XL_188(**kwargs):
+    return Latte(Latte_XL_188_Config(**kwargs))
+
+def Latte_L_122(**kwargs):
+    return Latte(Latte_L_122_Config(**kwargs))
+
+def Latte_L_144(**kwargs):
+    return Latte(Latte_L_144_Config(**kwargs))
+
+def Latte_L_188(**kwargs):
+    return Latte(Latte_L_188_Config(**kwargs))
+
+def Latte_B_122(**kwargs):
+    return Latte(Latte_B_122_Config(**kwargs))
+
+def Latte_B_144(**kwargs):
+    return Latte(Latte_B_144_Config(**kwargs))
+
+def Latte_B_188(**kwargs):
+    return Latte(Latte_B_188_Config(**kwargs))
+
+def Latte_S_122(**kwargs):
+    return Latte(Latte_S_122_Config(**kwargs))
+
+def Latte_S_144(**kwargs):
+    return Latte(Latte_S_144_Config(**kwargs))
+
+def Latte_S_188(**kwargs):
+    return Latte(Latte_S_188_Config(**kwargs))
+
+
+Latte_models = {
+    "Latte-XL/122": Latte_XL_122, "Latte-XL/144": Latte_XL_144, "Latte-XL/188": Latte_XL_188,
+    "Latte-L/122": Latte_L_122, "Latte-L/144": Latte_L_144, "Latte-L/188": Latte_L_188,
+    "Latte-B/122": Latte_B_122, "Latte-B/144": Latte_B_144, "Latte-B/188": Latte_B_188,
+    "Latte-S/122": Latte_S_122, "Latte-S/144": Latte_S_144, "Latte-S/188": Latte_S_188,
+}
+
+
+Latte_models = {
+    'Latte-XL/122': Latte_XL_122,  'Latte-XL/144': Latte_XL_144,  'Latte-XL/188': Latte_XL_188,
+    'Latte-L/122':  Latte_L_122,   'Latte-L/144':  Latte_L_144,   'Latte-L/188':  Latte_L_188,
+    'Latte-B/122':  Latte_B_122,   'Latte-B/144':  Latte_B_144,   'Latte-B/188':  Latte_B_188,
+    'Latte-S/122':  Latte_S_122,   'Latte-S/144':  Latte_S_144,   'Latte-S/188':  Latte_S_188,
+}
+
+if __name__ == '__main__':
+
+    import torch
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    img = torch.randn(3, 16, 4, 32, 32).to(device)
+    t = torch.tensor([1, 2, 3]).to(device)
+    y = torch.tensor([1, 2, 3]).to(device)
+    network = Latte_XL_122().to(device)
+    from thop import profile 
+    flops, params = profile(network, inputs=(img, t))
+    print('FLOPs = ' + str(flops/1000**3) + 'G')
+    print('Params = ' + str(params/1000**2) + 'M')
+    # y_embeder = LabelEmbedder(num_classes=101, hidden_size=768, dropout_prob=0.5).to(device)
+    # lora.mark_only_lora_as_trainable(network)
+    # out = y_embeder(y, True)
+    # out = network(img, t, y)
+    # print(out.shape)