Initial commit

comfy/ldm/cascade/stage_c.py

+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+from torch import nn
+import math
+from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
+# from .controlnet import ControlNetDeliverer
+
+class UpDownBlock2d(nn.Module):
+    def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert mode in ['up', 'down']
+        interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear',
+                                    align_corners=True) if enabled else nn.Identity()
+        mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device)
+        self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation])
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+        return x
+
+
+class StageC(nn.Module):
+    def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32],
+                 blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'],
+                 c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3,
+                 dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.dtype = dtype
+        self.c_r = c_r
+        self.t_conds = t_conds
+        self.c_clip_seq = c_clip_seq
+        if not isinstance(dropout, list):
+            dropout = [dropout] * len(c_hidden)
+        if not isinstance(self_attn, list):
+            self_attn = [self_attn] * len(c_hidden)
+
+        # CONDITIONING
+        self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device)
+        self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device)
+        self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device)
+        self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.embedding = nn.Sequential(
+            nn.PixelUnshuffle(patch_size),
+            operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6)
+        )
+
+        def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
+            if block_type == 'C':
+                return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'A':
+                return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'F':
+                return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'T':
+                return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
+            else:
+                raise Exception(f'Block type {block_type} not supported')
+
+        # BLOCKS
+        # -- down blocks
+        self.down_blocks = nn.ModuleList()
+        self.down_downscalers = nn.ModuleList()
+        self.down_repeat_mappers = nn.ModuleList()
+        for i in range(len(c_hidden)):
+            if i > 0:
+                self.down_downscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),
+                    UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+                ))
+            else:
+                self.down_downscalers.append(nn.Identity())
+            down_block = nn.ModuleList()
+            for _ in range(blocks[0][i]):
+                for block_type in level_config[i]:
+                    block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
+                    down_block.append(block)
+            self.down_blocks.append(down_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[0][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.down_repeat_mappers.append(block_repeat_mappers)
+
+        # -- up blocks
+        self.up_blocks = nn.ModuleList()
+        self.up_upscalers = nn.ModuleList()
+        self.up_repeat_mappers = nn.ModuleList()
+        for i in reversed(range(len(c_hidden))):
+            if i > 0:
+                self.up_upscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6),
+                    UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+                ))
+            else:
+                self.up_upscalers.append(nn.Identity())
+            up_block = nn.ModuleList()
+            for j in range(blocks[1][::-1][i]):
+                for k, block_type in enumerate(level_config[i]):
+                    c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
+                    block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
+                                      self_attn=self_attn[i])
+                    up_block.append(block)
+            self.up_blocks.append(up_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[1][::-1][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.up_repeat_mappers.append(block_repeat_mappers)
+
+        # OUTPUT
+        self.clf = nn.Sequential(
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+            operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
+            nn.PixelShuffle(patch_size),
+        )
+
+        # --- WEIGHT INIT ---
+    #     self.apply(self._init_weights)  # General init
+    #     nn.init.normal_(self.clip_txt_mapper.weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.clip_img_mapper.weight, std=0.02)  # conditionings
+    #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
+    #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
+    #
+    #     # blocks
+    #     for level_block in self.down_blocks + self.up_blocks:
+    #         for block in level_block:
+    #             if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
+    #                 block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
+    #             elif isinstance(block, TimestepBlock):
+    #                 for layer in block.modules():
+    #                     if isinstance(layer, nn.Linear):
+    #                         nn.init.constant_(layer.weight, 0)
+    #
+    # def _init_weights(self, m):
+    #     if isinstance(m, (nn.Conv2d, nn.Linear)):
+    #         torch.nn.init.xavier_uniform_(m.weight)
+    #         if m.bias is not None:
+    #             nn.init.constant_(m.bias, 0)
+
+    def gen_r_embedding(self, r, max_positions=10000):
+        r = r * max_positions
+        half_dim = self.c_r // 2
+        emb = math.log(max_positions) / (half_dim - 1)
+        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+        emb = r[:, None] * emb[None, :]
+        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+        if self.c_r % 2 == 1:  # zero pad
+            emb = nn.functional.pad(emb, (0, 1), mode='constant')
+        return emb
+
+    def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
+        clip_txt = self.clip_txt_mapper(clip_txt)
+        if len(clip_txt_pooled.shape) == 2:
+            clip_txt_pooled = clip_txt_pooled.unsqueeze(1)
+        if len(clip_img.shape) == 2:
+            clip_img = clip_img.unsqueeze(1)
+        clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1)
+        clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1)
+        clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)
+        clip = self.clip_norm(clip)
+        return clip
+
+    def _down_encode(self, x, r_embed, clip, cnet=None):
+        level_outputs = []
+        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
+        for down_block, downscaler, repmap in block_group:
+            x = downscaler(x)
+            for i in range(len(repmap) + 1):
+                for block in down_block:
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        if cnet is not None:
+                            next_cnet = cnet.pop()
+                            if next_cnet is not None:
+                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+                                                                  align_corners=True).to(x.dtype)
+                        x = block(x)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if i < len(repmap):
+                    x = repmap[i](x)
+            level_outputs.insert(0, x)
+        return level_outputs
+
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
+        x = level_outputs[0]
+        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
+        for i, (up_block, upscaler, repmap) in enumerate(block_group):
+            for j in range(len(repmap) + 1):
+                for k, block in enumerate(up_block):
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        skip = level_outputs[i] if k == 0 and i > 0 else None
+                        if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
+                            x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
+                                                                align_corners=True)
+                        if cnet is not None:
+                            next_cnet = cnet.pop()
+                            if next_cnet is not None:
+                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+                                                                  align_corners=True).to(x.dtype)
+                        x = block(x, skip)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if j < len(repmap):
+                    x = repmap[j](x)
+            x = upscaler(x)
+        return x
+
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
+        # Process the conditioning embeddings
+        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
+        for c in self.t_conds:
+            t_cond = kwargs.get(c, torch.zeros_like(r))
+            r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
+        clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img)
+
+        if control is not None:
+            cnet = control.get("input")
+        else:
+            cnet = None
+
+        # Model Blocks
+        x = self.embedding(x)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet)
+        return self.clf(x)
+
+    def update_weights_ema(self, src_model, beta=0.999):
+        for self_params, src_params in zip(self.parameters(), src_model.parameters()):
+            self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
+        for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
+            self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)

comfy/ldm/cascade/stage_c_coder.py

+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+import torch
+import torchvision
+from torch import nn
+
+import comfy.ops
+
+ops = comfy.ops.disable_weight_init
+
+# EfficientNet
+class EfficientNetEncoder(nn.Module):
+    def __init__(self, c_latent=16):
+        super().__init__()
+        self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
+        self.mapper = nn.Sequential(
+            ops.Conv2d(1280, c_latent, kernel_size=1, bias=False),
+            nn.BatchNorm2d(c_latent, affine=False),  # then normalize them to have mean 0 and std 1
+        )
+        self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]))
+        self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225]))
+
+    def forward(self, x):
+        x = x * 0.5 + 0.5
+        x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype)
+        o = self.mapper(self.backbone(x))
+        return o
+
+
+# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192
+class Previewer(nn.Module):
+    def __init__(self, c_in=16, c_hidden=512, c_out=3):
+        super().__init__()
+        self.blocks = nn.Sequential(
+            ops.Conv2d(c_in, c_hidden, kernel_size=1),  # 16 channels to 512 channels
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden),
+
+            ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden),
+
+            ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2),  # 16 -> 32
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 2),
+
+            ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 2),
+
+            ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2),  # 32 -> 64
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2),  # 64 -> 128
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            ops.Conv2d(c_hidden // 4, c_out, kernel_size=1),
+        )
+
+    def forward(self, x):
+        return (self.blocks(x) - 0.5) * 2.0
+
+class StageC_coder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.previewer = Previewer()
+        self.encoder = EfficientNetEncoder()
+
+    def encode(self, x):
+        return self.encoder(x)
+
+    def decode(self, x):
+        return self.previewer(x)

comfy/ldm/chroma/layers.py

+import torch
+from torch import Tensor, nn
+
+from comfy.ldm.flux.math import attention
+from comfy.ldm.flux.layers import (
+    MLPEmbedder,
+    RMSNorm,
+    QKNorm,
+    SelfAttention,
+    ModulationOut,
+)
+
+
+
+class ChromaModulationOut(ModulationOut):
+    @classmethod
+    def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:
+        return cls(
+            shift=tensor[:, offset : offset + 1, :],
+            scale=tensor[:, offset + 1 : offset + 2, :],
+            gate=tensor[:, offset + 2 : offset + 3, :],
+        )
+
+
+
+
+class Approximator(nn.Module):
+    def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
+        self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
+        self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
+
+    @property
+    def device(self):
+        # Get the device of the module (assumes all parameters are on the same device)
+        return next(self.parameters()).device
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.in_proj(x)
+
+        for layer, norms in zip(self.layers, self.norms):
+            x = x + layer(norms(x))
+
+        x = self.out_proj(x)
+
+        return x
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.flipped_img_txt = flipped_img_txt
+
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
+        (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
+
+        # prepare image for attention
+        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        attn = attention(torch.cat((txt_q, img_q), dim=2),
+                         torch.cat((txt_k, img_k), dim=2),
+                         torch.cat((txt_v, img_v), dim=2),
+                         pe=pe, mask=attn_mask)
+
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
+        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
+
+        # calculate the txt bloks
+        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
+        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
+
+        if txt.dtype == torch.float16:
+            txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
+
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float = None,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
+        mod = vec
+        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        x.addcmul_(mod.gate, output)
+        if x.dtype == torch.float16:
+            x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+        return x
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = vec
+        shift = shift.squeeze(1)
+        scale = scale.squeeze(1)
+        x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
+        x = self.linear(x)
+        return x

comfy/ldm/chroma/model.py

+#Original code can be found on: https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+from einops import rearrange, repeat
+import comfy.patcher_extension
+import comfy.ldm.common_dit
+
+from comfy.ldm.flux.layers import (
+    EmbedND,
+    timestep_embedding,
+)
+
+from .layers import (
+    DoubleStreamBlock,
+    LastLayer,
+    SingleStreamBlock,
+    Approximator,
+    ChromaModulationOut,
+)
+
+
+@dataclass
+class ChromaParams:
+    in_channels: int
+    out_channels: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list
+    theta: int
+    patch_size: int
+    qkv_bias: bool
+    in_dim: int
+    out_dim: int
+    hidden_dim: int
+    n_layers: int
+
+
+
+
+class Chroma(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        params = ChromaParams(**kwargs)
+        self.params = params
+        self.patch_size = params.patch_size
+        self.in_channels = params.in_channels
+        self.out_channels = params.out_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.in_dim = params.in_dim
+        self.out_dim = params.out_dim
+        self.hidden_dim = params.hidden_dim
+        self.n_layers = params.n_layers
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+        # set as nn identity for now, will overwrite it later.
+        self.distilled_guidance_layer = Approximator(
+                    in_dim=self.in_dim,
+                    hidden_dim=self.hidden_dim,
+                    out_dim=self.out_dim,
+                    n_layers=self.n_layers,
+                    dtype=dtype, device=device, operations=operations
+                )
+
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        if final_layer:
+            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+        self.skip_mmdit = []
+        self.skip_dit = []
+        self.lite = False
+
+    def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):
+        # This function slices up the modulations tensor which has the following layout:
+        #   single     : num_single_blocks * 3 elements
+        #   double_img : num_double_blocks * 6 elements
+        #   double_txt : num_double_blocks * 6 elements
+        #   final      : 2 elements
+        if block_type == "final":
+            return (tensor[:, -2:-1, :], tensor[:, -1:, :])
+        single_block_count = self.params.depth_single_blocks
+        double_block_count = self.params.depth
+        offset = 3 * idx
+        if block_type == "single":
+            return ChromaModulationOut.from_offset(tensor, offset)
+        # Double block modulations are 6 elements so we double 3 * idx.
+        offset *= 2
+        if block_type in {"double_img", "double_txt"}:
+            # Advance past the single block modulations.
+            offset += 3 * single_block_count
+            if block_type == "double_txt":
+                # Advance past the double block img modulations.
+                offset += 6 * double_block_count
+            return (
+                ChromaModulationOut.from_offset(tensor, offset),
+                ChromaModulationOut.from_offset(tensor, offset + 3),
+            )
+        raise ValueError("Bad block_type")
+
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        guidance: Tensor = None,
+        control = None,
+        transformer_options={},
+        attn_mask: Tensor = None,
+    ) -> Tensor:
+        patches_replace = transformer_options.get("patches_replace", {})
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+
+        # distilled vector guidance
+        mod_index_length = 344
+        distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype)
+        # guidance = guidance *
+        distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
+
+        # get all modulation index
+        modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
+        # we need to broadcast the modulation index here so each batch has all of the index
+        modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
+        # and we need to broadcast timestep and guidance along too
+        timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)
+        # then and only then we could concatenate it together
+        input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)
+
+        mod_vectors = self.distilled_guidance_layer(input_vec)
+
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.double_blocks):
+            if i not in self.skip_mmdit:
+                double_mod = (
+                    self.get_modulations(mod_vectors, "double_img", idx=i),
+                    self.get_modulations(mod_vectors, "double_txt", idx=i),
+                )
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"], out["txt"] = block(img=args["img"],
+                                                       txt=args["txt"],
+                                                       vec=args["vec"],
+                                                       pe=args["pe"],
+                                                       attn_mask=args.get("attn_mask"))
+                        return out
+
+                    out = blocks_replace[("double_block", i)]({"img": img,
+                                                               "txt": txt,
+                                                               "vec": double_mod,
+                                                               "pe": pe,
+                                                               "attn_mask": attn_mask},
+                                                              {"original_block": block_wrap})
+                    txt = out["txt"]
+                    img = out["img"]
+                else:
+                    img, txt = block(img=img,
+                                     txt=txt,
+                                     vec=double_mod,
+                                     pe=pe,
+                                     attn_mask=attn_mask)
+
+                if control is not None: # Controlnet
+                    control_i = control.get("input")
+                    if i < len(control_i):
+                        add = control_i[i]
+                        if add is not None:
+                            img += add
+
+        img = torch.cat((txt, img), 1)
+
+        for i, block in enumerate(self.single_blocks):
+            if i not in self.skip_dit:
+                single_mod = self.get_modulations(mod_vectors, "single", idx=i)
+                if ("single_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"] = block(args["img"],
+                                           vec=args["vec"],
+                                           pe=args["pe"],
+                                           attn_mask=args.get("attn_mask"))
+                        return out
+
+                    out = blocks_replace[("single_block", i)]({"img": img,
+                                                               "vec": single_mod,
+                                                               "pe": pe,
+                                                               "attn_mask": attn_mask},
+                                                              {"original_block": block_wrap})
+                    img = out["img"]
+                else:
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
+
+                if control is not None: # Controlnet
+                    control_o = control.get("output")
+                    if i < len(control_o):
+                        add = control_o[i]
+                        if add is not None:
+                            img[:, txt.shape[1] :, ...] += add
+
+        img = img[:, txt.shape[1] :, ...]
+        final_mod = self.get_modulations(mod_vectors, "final")
+        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        return img
+
+    def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs)
+
+    def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
+
+        h_len = ((h + (self.patch_size // 2)) // self.patch_size)
+        w_len = ((w + (self.patch_size // 2)) // self.patch_size)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]

comfy/ldm/common_dit.py

+import torch
+import comfy.rmsnorm
+
+
+def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
+    if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()):
+        padding_mode = "reflect"
+
+    pad = ()
+    for i in range(img.ndim - 2):
+        pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad
+
+    return torch.nn.functional.pad(img, pad, mode=padding_mode)
+
+
+rms_norm = comfy.rmsnorm.rms_norm

comfy/ldm/cosmos/blocks.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional
+import logging
+
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+from torch import nn
+
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def get_normalization(name: str, channels: int, weight_args={}, operations=None):
+    if name == "I":
+        return nn.Identity()
+    elif name == "R":
+        return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args)
+    else:
+        raise ValueError(f"Normalization {name} not found")
+
+
+class BaseAttentionOp(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+
+class Attention(nn.Module):
+    """
+    Generalized attention impl.
+
+    Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided.
+    If `context_dim` is None, self-attention is assumed.
+
+    Parameters:
+        query_dim (int): Dimension of each query vector.
+        context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed.
+        heads (int, optional): Number of attention heads. Defaults to 8.
+        dim_head (int, optional): Dimension of each head. Defaults to 64.
+        dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0.
+        attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default.
+        qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False.
+        out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False.
+        qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections.
+                                  Defaults to "SSI".
+        qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections.
+                                        Defaults to 'per_head'. Only support 'per_head'.
+
+    Examples:
+        >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1)
+        >>> query = torch.randn(10, 128)  # Batch size of 10
+        >>> context = torch.randn(10, 256)  # Batch size of 10
+        >>> output = attn(query, context)  # Perform the attention operation
+
+    Note:
+        https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        attn_op: Optional[BaseAttentionOp] = None,
+        qkv_bias: bool = False,
+        out_bias: bool = False,
+        qkv_norm: str = "SSI",
+        qkv_norm_mode: str = "per_head",
+        backend: str = "transformer_engine",
+        qkv_format: str = "bshd",
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+
+        self.is_selfattn = context_dim is None  # self attention
+
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+
+        self.heads = heads
+        self.dim_head = dim_head
+        self.qkv_norm_mode = qkv_norm_mode
+        self.qkv_format = qkv_format
+
+        if self.qkv_norm_mode == "per_head":
+            norm_dim = dim_head
+        else:
+            raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'")
+
+        self.backend = backend
+
+        self.to_q = nn.Sequential(
+            operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations),
+        )
+        self.to_k = nn.Sequential(
+            operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations),
+        )
+        self.to_v = nn.Sequential(
+            operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations),
+        )
+
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args),
+            nn.Dropout(dropout),
+        )
+
+    def cal_qkv(
+        self, x, context=None, mask=None, rope_emb=None, **kwargs
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        del kwargs
+
+
+        """
+        self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers.
+        Before 07/24/2024, these modules normalize across all heads.
+        After 07/24/2024, to support tensor parallelism and follow the common practice in the community,
+        we support to normalize per head.
+        To keep the checkpoint copatibility with the previous code,
+        we keep the nn.Sequential but call the projection and the normalization layers separately.
+        We use a flag `self.qkv_norm_mode` to control the normalization behavior.
+        The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head.
+        """
+        if self.qkv_norm_mode == "per_head":
+            q = self.to_q[0](x)
+            context = x if context is None else context
+            k = self.to_k[0](context)
+            v = self.to_v[0](context)
+            q, k, v = map(
+                lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head),
+                (q, k, v),
+            )
+        else:
+            raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'")
+
+        q = self.to_q[1](q)
+        k = self.to_k[1](k)
+        v = self.to_v[1](v)
+        if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
+            # apply_rotary_pos_emb inlined
+            q_shape = q.shape
+            q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
+            q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1]
+            q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype)
+
+            # apply_rotary_pos_emb inlined
+            k_shape = k.shape
+            k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
+            k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1]
+            k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype)
+        return q, k, v
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        rope_emb=None,
+        **kwargs,
+    ):
+        """
+        Args:
+            x (Tensor): The query tensor of shape [B, Mq, K]
+            context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
+        """
+        q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
+        del q, k, v
+        out = rearrange(out, " b n s c -> s b (n c)")
+        return self.to_out(out)
+
+
+class FeedForward(nn.Module):
+    """
+    Transformer FFN with optional gating
+
+    Parameters:
+        d_model (int): Dimensionality of input features.
+        d_ff (int): Dimensionality of the hidden layer.
+        dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1.
+        activation (callable, optional): The activation function applied after the first linear layer.
+                                         Defaults to nn.ReLU().
+        is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer.
+                                   Defaults to False.
+        bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True.
+
+    Example:
+        >>> ff = FeedForward(d_model=512, d_ff=2048)
+        >>> x = torch.randn(64, 10, 512)  # Example input tensor
+        >>> output = ff(x)
+        >>> print(output.shape)  # Expected shape: (64, 10, 512)
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        d_ff: int,
+        dropout: float = 0.1,
+        activation=nn.ReLU(),
+        is_gated: bool = False,
+        bias: bool = False,
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+
+        self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args)
+        self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args)
+
+        self.dropout = nn.Dropout(dropout)
+        self.activation = activation
+        self.is_gated = is_gated
+        if is_gated:
+            self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args)
+
+    def forward(self, x: torch.Tensor):
+        g = self.activation(self.layer1(x))
+        if self.is_gated:
+            x = g * self.linear_gate(x)
+        else:
+            x = g
+        assert self.dropout.p == 0.0, "we skip dropout"
+        return self.layer2(x)
+
+
+class GPT2FeedForward(FeedForward):
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None):
+        super().__init__(
+            d_model=d_model,
+            d_ff=d_ff,
+            dropout=dropout,
+            activation=nn.GELU(),
+            is_gated=False,
+            bias=bias,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def forward(self, x: torch.Tensor):
+        assert self.dropout.p == 0.0, "we skip dropout"
+
+        x = self.layer1(x)
+        x = self.activation(x)
+        x = self.layer2(x)
+
+        return x
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels):
+        super().__init__()
+        self.num_channels = num_channels
+
+    def forward(self, timesteps):
+        half_dim = self.num_channels // 2
+        exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)
+        exponent = exponent / (half_dim - 0.0)
+
+        emb = torch.exp(exponent)
+        emb = timesteps[:, None].float() * emb[None, :]
+
+        sin_emb = torch.sin(emb)
+        cos_emb = torch.cos(emb)
+        emb = torch.cat([cos_emb, sin_emb], dim=-1)
+
+        return emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None):
+        super().__init__()
+        logging.debug(
+            f"Using AdaLN LoRA Flag:  {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility."
+        )
+        self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args)
+        self.activation = nn.SiLU()
+        self.use_adaln_lora = use_adaln_lora
+        if use_adaln_lora:
+            self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args)
+        else:
+            self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        emb = self.linear_1(sample)
+        emb = self.activation(emb)
+        emb = self.linear_2(emb)
+
+        if self.use_adaln_lora:
+            adaln_lora_B_3D = emb
+            emb_B_D = sample
+        else:
+            emb_B_D = emb
+            adaln_lora_B_3D = None
+
+        return emb_B_D, adaln_lora_B_3D
+
+
+class FourierFeatures(nn.Module):
+    """
+    Implements a layer that generates Fourier features from input tensors, based on randomly sampled
+    frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems.
+
+    [B] -> [B, D]
+
+    Parameters:
+        num_channels (int): The number of Fourier features to generate.
+        bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1.
+        normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize
+                                    the variance of the features. Defaults to False.
+
+    Example:
+        >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True)
+        >>> x = torch.randn(10, 256)  # Example input tensor
+        >>> output = layer(x)
+        >>> print(output.shape)  # Expected shape: (10, 256)
+    """
+
+    def __init__(self, num_channels, bandwidth=1, normalize=False):
+        super().__init__()
+        self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True)
+        self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True)
+        self.gain = np.sqrt(2) if normalize else 1
+
+    def forward(self, x, gain: float = 1.0):
+        """
+        Apply the Fourier feature transformation to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+            gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1.
+
+        Returns:
+            torch.Tensor: The transformed tensor, with Fourier features applied.
+        """
+        in_dtype = x.dtype
+        x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32))
+        x = x.cos().mul(self.gain * gain).to(in_dtype)
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """
+    PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,
+    depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,
+    making it suitable for video and image processing tasks. It supports dividing the input into patches
+    and embedding each patch into a vector of size `out_channels`.
+
+    Parameters:
+    - spatial_patch_size (int): The size of each spatial patch.
+    - temporal_patch_size (int): The size of each temporal patch.
+    - in_channels (int): Number of input channels. Default: 3.
+    - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.
+    - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.
+    """
+
+    def __init__(
+        self,
+        spatial_patch_size,
+        temporal_patch_size,
+        in_channels=3,
+        out_channels=768,
+        bias=True,
+        weight_args={},
+        operations=None,
+    ):
+        super().__init__()
+        self.spatial_patch_size = spatial_patch_size
+        self.temporal_patch_size = temporal_patch_size
+
+        self.proj = nn.Sequential(
+            Rearrange(
+                "b c (t r) (h m) (w n) -> b t h w (c r m n)",
+                r=temporal_patch_size,
+                m=spatial_patch_size,
+                n=spatial_patch_size,
+            ),
+            operations.Linear(
+                in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args
+            ),
+        )
+        self.out = nn.Identity()
+
+    def forward(self, x):
+        """
+        Forward pass of the PatchEmbed module.
+
+        Parameters:
+        - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where
+            B is the batch size,
+            C is the number of channels,
+            T is the temporal dimension,
+            H is the height, and
+            W is the width of the input.
+
+        Returns:
+        - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.
+        """
+        assert x.dim() == 5
+        _, _, T, H, W = x.shape
+        assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0
+        assert T % self.temporal_patch_size == 0
+        x = self.proj(x)
+        return self.out(x)
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of video DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        spatial_patch_size,
+        temporal_patch_size,
+        out_channels,
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args)
+        self.linear = operations.Linear(
+            hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args
+        )
+        self.hidden_size = hidden_size
+        self.n_adaln_chunks = 2
+        self.use_adaln_lora = use_adaln_lora
+        if use_adaln_lora:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args),
+                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args),
+            )
+        else:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args)
+            )
+
+    def forward(
+        self,
+        x_BT_HW_D,
+        emb_B_D,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ):
+        if self.use_adaln_lora:
+            assert adaln_lora_B_3D is not None
+            shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk(
+                2, dim=1
+            )
+        else:
+            shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1)
+
+        B = emb_B_D.shape[0]
+        T = x_BT_HW_D.shape[0] // B
+        shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T)
+        x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D)
+
+        x_BT_HW_D = self.linear(x_BT_HW_D)
+        return x_BT_HW_D
+
+
+class VideoAttn(nn.Module):
+    """
+    Implements video attention with optional cross-attention capabilities.
+
+    This module processes video features while maintaining their spatio-temporal structure. It can perform
+    self-attention within the video features or cross-attention with external context features.
+
+    Parameters:
+        x_dim (int): Dimension of input feature vectors
+        context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention
+        num_heads (int): Number of attention heads
+        bias (bool): Whether to include bias in attention projections. Default: False
+        qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head"
+        x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD"
+
+    Input shape:
+        - x: (T, H, W, B, D) video features
+        - context (optional): (M, B, D) context features for cross-attention
+        where:
+            T: temporal dimension
+            H: height
+            W: width
+            B: batch size
+            D: feature dimension
+            M: context sequence length
+    """
+
+    def __init__(
+        self,
+        x_dim: int,
+        context_dim: Optional[int],
+        num_heads: int,
+        bias: bool = False,
+        qkv_norm_mode: str = "per_head",
+        x_format: str = "BTHWD",
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.x_format = x_format
+
+        self.attn = Attention(
+            x_dim,
+            context_dim,
+            num_heads,
+            x_dim // num_heads,
+            qkv_bias=bias,
+            qkv_norm="RRI",
+            out_bias=bias,
+            qkv_norm_mode=qkv_norm_mode,
+            qkv_format="sbhd",
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass for video attention.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data.
+            context (Tensor): Context tensor of shape (B, M, D) or (M, B, D),
+            where M is the sequence length of the context.
+            crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms.
+            rope_emb_L_1_1_D (Optional[Tensor]):
+            Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.
+
+        Returns:
+            Tensor: The output tensor with applied attention, maintaining the input shape.
+        """
+
+        x_T_H_W_B_D = x
+        context_M_B_D = context
+        T, H, W, B, D = x_T_H_W_B_D.shape
+        x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d")
+        x_THW_B_D = self.attn(
+            x_THW_B_D,
+            context_M_B_D,
+            crossattn_mask,
+            rope_emb=rope_emb_L_1_1_D,
+        )
+        x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
+        return x_T_H_W_B_D
+
+
+def adaln_norm_state(norm_state, x, scale, shift):
+    normalized = norm_state(x)
+    return normalized * (1 + scale) + shift
+
+
+class DITBuildingBlock(nn.Module):
+    """
+    A building block for the DiT (Diffusion Transformer) architecture that supports different types of
+    attention and MLP operations with adaptive layer normalization.
+
+    Parameters:
+        block_type (str): Type of block - one of:
+            - "cross_attn"/"ca": Cross-attention
+            - "full_attn"/"fa": Full self-attention
+            - "mlp"/"ff": MLP/feedforward block
+        x_dim (int): Dimension of input features
+        context_dim (Optional[int]): Dimension of context features for cross-attention
+        num_heads (int): Number of attention heads
+        mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0
+        bias (bool): Whether to use bias in layers. Default: False
+        mlp_dropout (float): Dropout rate for MLP. Default: 0.0
+        qkv_norm_mode (str): QKV normalization mode. Default: "per_head"
+        x_format (str): Input tensor format. Default: "BTHWD"
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256
+    """
+
+    def __init__(
+        self,
+        block_type: str,
+        x_dim: int,
+        context_dim: Optional[int],
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        bias: bool = False,
+        mlp_dropout: float = 0.0,
+        qkv_norm_mode: str = "per_head",
+        x_format: str = "BTHWD",
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None
+    ) -> None:
+        block_type = block_type.lower()
+
+        super().__init__()
+        self.x_format = x_format
+        if block_type in ["cross_attn", "ca"]:
+            self.block = VideoAttn(
+                x_dim,
+                context_dim,
+                num_heads,
+                bias=bias,
+                qkv_norm_mode=qkv_norm_mode,
+                x_format=self.x_format,
+                weight_args=weight_args,
+                operations=operations,
+            )
+        elif block_type in ["full_attn", "fa"]:
+            self.block = VideoAttn(
+                x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations
+            )
+        elif block_type in ["mlp", "ff"]:
+            self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations)
+        else:
+            raise ValueError(f"Unknown block type: {block_type}")
+
+        self.block_type = block_type
+        self.use_adaln_lora = use_adaln_lora
+
+        self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6)
+        self.n_adaln_chunks = 3
+        if use_adaln_lora:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args),
+                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args),
+            )
+        else:
+            self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass for dynamically configured blocks with adaptive normalization.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D).
+            emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation.
+            crossattn_emb (Tensor): Tensor for cross-attention blocks.
+            crossattn_mask (Optional[Tensor]): Optional mask for cross-attention.
+            rope_emb_L_1_1_D (Optional[Tensor]):
+            Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.
+
+        Returns:
+            Tensor: The output tensor after processing through the configured block and adaptive normalization.
+        """
+        if self.use_adaln_lora:
+            shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk(
+                self.n_adaln_chunks, dim=1
+            )
+        else:
+            shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1)
+
+        shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = (
+            shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+            scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+            gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+        )
+
+        if self.block_type in ["mlp", "ff"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+            )
+        elif self.block_type in ["full_attn", "fa"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+                context=None,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+            )
+        elif self.block_type in ["cross_attn", "ca"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+                context=crossattn_emb,
+                crossattn_mask=crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+            )
+        else:
+            raise ValueError(f"Unknown block type: {self.block_type}")
+
+        return x
+
+
+class GeneralDITTransformerBlock(nn.Module):
+    """
+    A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer.
+    Each block in the sequence is specified by a block configuration string.
+
+    Parameters:
+        x_dim (int): Dimension of input features
+        context_dim (int): Dimension of context features for cross-attention blocks
+        num_heads (int): Number of attention heads
+        block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention,
+                          full-attention, then MLP)
+        mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0
+        x_format (str): Input tensor format. Default: "BTHWD"
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256
+
+    The block_config string uses "-" to separate block types:
+        - "ca"/"cross_attn": Cross-attention block
+        - "fa"/"full_attn": Full self-attention block
+        - "mlp"/"ff": MLP/feedforward block
+
+    Example:
+        block_config = "ca-fa-mlp" creates a sequence of:
+        1. Cross-attention block
+        2. Full self-attention block
+        3. MLP block
+    """
+
+    def __init__(
+        self,
+        x_dim: int,
+        context_dim: int,
+        num_heads: int,
+        block_config: str,
+        mlp_ratio: float = 4.0,
+        x_format: str = "BTHWD",
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None
+    ):
+        super().__init__()
+        self.blocks = nn.ModuleList()
+        self.x_format = x_format
+        for block_type in block_config.split("-"):
+            self.blocks.append(
+                DITBuildingBlock(
+                    block_type,
+                    x_dim,
+                    context_dim,
+                    num_heads,
+                    mlp_ratio,
+                    x_format=self.x_format,
+                    use_adaln_lora=use_adaln_lora,
+                    adaln_lora_dim=adaln_lora_dim,
+                    weight_args=weight_args,
+                    operations=operations,
+                )
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        for block in self.blocks:
+            x = block(
+                x,
+                emb_B_D,
+                crossattn_emb,
+                crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                adaln_lora_B_3D=adaln_lora_B_3D,
+            )
+        return x

comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py

+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The model definition for 3D layers
+
+Adapted from: https://github.com/lucidrains/magvit2-pytorch/blob/
+9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889
+
+[MIT License Copyright (c) 2023 Phil Wang]
+https://github.com/lucidrains/magvit2-pytorch/blob/
+9f49074179c912736e617d61b32be367eb5f993a/LICENSE
+"""
+import math
+from typing import Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import logging
+
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+
+from .patching import (
+    Patcher,
+    Patcher3D,
+    UnPatcher,
+    UnPatcher3D,
+)
+from .utils import (
+    CausalNormalize,
+    batch2space,
+    batch2time,
+    cast_tuple,
+    is_odd,
+    nonlinearity,
+    replication_pad,
+    space2batch,
+    time2batch,
+)
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+_LEGACY_NUM_GROUPS = 32
+
+
+class CausalConv3d(nn.Module):
+    def __init__(
+        self,
+        chan_in: int = 1,
+        chan_out: int = 1,
+        kernel_size: Union[int, Tuple[int, int, int]] = 3,
+        pad_mode: str = "constant",
+        **kwargs,
+    ):
+        super().__init__()
+        kernel_size = cast_tuple(kernel_size, 3)
+
+        time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
+
+        assert is_odd(height_kernel_size) and is_odd(width_kernel_size)
+
+        dilation = kwargs.pop("dilation", 1)
+        stride = kwargs.pop("stride", 1)
+        time_stride = kwargs.pop("time_stride", 1)
+        time_dilation = kwargs.pop("time_dilation", 1)
+        padding = kwargs.pop("padding", 1)
+
+        self.pad_mode = pad_mode
+        time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride)
+        self.time_pad = time_pad
+
+        self.spatial_pad = (padding, padding, padding, padding)
+
+        stride = (time_stride, stride, stride)
+        dilation = (time_dilation, dilation, dilation)
+        self.conv3d = ops.Conv3d(
+            chan_in,
+            chan_out,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            **kwargs,
+        )
+
+    def _replication_pad(self, x: torch.Tensor) -> torch.Tensor:
+        x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1)
+        x = torch.cat([x_prev, x], dim=2)
+        padding = self.spatial_pad + (0, 0)
+        return F.pad(x, padding, mode=self.pad_mode, value=0.0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self._replication_pad(x)
+        return self.conv3d(x)
+
+
+class CausalUpsample3d(nn.Module):
+    def __init__(self, in_channels: int) -> None:
+        super().__init__()
+        self.conv = CausalConv3d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4)
+        time_factor = 1.0 + 1.0 * (x.shape[2] > 1)
+        if isinstance(time_factor, torch.Tensor):
+            time_factor = time_factor.item()
+        x = x.repeat_interleave(int(time_factor), dim=2)
+        # TODO(freda): Check if this causes temporal inconsistency.
+        # Shoule reverse the order of the following two ops,
+        # better perf and better temporal smoothness.
+        x = self.conv(x)
+        return x[..., int(time_factor - 1) :, :, :]
+
+
+class CausalDownsample3d(nn.Module):
+    def __init__(self, in_channels: int) -> None:
+        super().__init__()
+        self.conv = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=3,
+            stride=2,
+            time_stride=2,
+            padding=0,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad = (0, 1, 0, 1, 0, 0)
+        x = F.pad(x, pad, mode="constant", value=0)
+        x = replication_pad(x)
+        x = self.conv(x)
+        return x
+
+
+class CausalHybridUpsample3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        spatial_up: bool = True,
+        temporal_up: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.spatial_up = spatial_up
+        self.temporal_up = temporal_up
+        if not self.spatial_up and not self.temporal_up:
+            return
+
+        self.conv1 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=(3, 1, 1),
+            stride=1,
+            time_stride=1,
+            padding=0,
+        )
+        self.conv2 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=(1, 3, 3),
+            stride=1,
+            time_stride=1,
+            padding=1,
+        )
+        self.conv3 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            time_stride=1,
+            padding=0,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not self.spatial_up and not self.temporal_up:
+            return x
+
+        # hybrid upsample temporally.
+        if self.temporal_up:
+            time_factor = 1.0 + 1.0 * (x.shape[2] > 1)
+            if isinstance(time_factor, torch.Tensor):
+                time_factor = time_factor.item()
+            x = x.repeat_interleave(int(time_factor), dim=2)
+            x = x[..., int(time_factor - 1) :, :, :]
+            x = self.conv1(x) + x
+
+        # hybrid upsample spatially.
+        if self.spatial_up:
+            x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4)
+            x = self.conv2(x) + x
+
+        # final 1x1x1 conv.
+        x = self.conv3(x)
+        return x
+
+
+class CausalHybridDownsample3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        spatial_down: bool = True,
+        temporal_down: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.spatial_down = spatial_down
+        self.temporal_down = temporal_down
+        if not self.spatial_down and not self.temporal_down:
+            return
+
+        self.conv1 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=(1, 3, 3),
+            stride=2,
+            time_stride=1,
+            padding=0,
+        )
+        self.conv2 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=(3, 1, 1),
+            stride=1,
+            time_stride=2,
+            padding=0,
+        )
+        self.conv3 = CausalConv3d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            time_stride=1,
+            padding=0,
+        )
+
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not self.spatial_down and not self.temporal_down:
+            return x
+
+        # hybrid downsample spatially.
+        if self.spatial_down:
+            pad = (0, 1, 0, 1, 0, 0)
+            x = F.pad(x, pad, mode="constant", value=0)
+            x1 = self.conv1(x)
+            x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+            x = x1 + x2
+
+        # hybrid downsample temporally.
+        if self.temporal_down:
+            x = replication_pad(x)
+            x1 = self.conv2(x)
+            x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1))
+            x = x1 + x2
+
+        # final 1x1x1 conv.
+        x = self.conv3(x)
+        return x
+
+
+class CausalResnetBlock3d(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        out_channels: int = None,
+        dropout: float,
+        num_groups: int,
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+
+        self.norm1 = CausalNormalize(in_channels, num_groups=num_groups)
+        self.conv1 = CausalConv3d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.norm2 = CausalNormalize(out_channels, num_groups=num_groups)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = CausalConv3d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.nin_shortcut = (
+            CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+        x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class CausalResnetBlockFactorized3d(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        out_channels: int = None,
+        dropout: float,
+        num_groups: int,
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+
+        self.norm1 = CausalNormalize(in_channels, num_groups=1)
+        self.conv1 = nn.Sequential(
+            CausalConv3d(
+                in_channels,
+                out_channels,
+                kernel_size=(1, 3, 3),
+                stride=1,
+                padding=1,
+            ),
+            CausalConv3d(
+                out_channels,
+                out_channels,
+                kernel_size=(3, 1, 1),
+                stride=1,
+                padding=0,
+            ),
+        )
+        self.norm2 = CausalNormalize(out_channels, num_groups=num_groups)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = nn.Sequential(
+            CausalConv3d(
+                out_channels,
+                out_channels,
+                kernel_size=(1, 3, 3),
+                stride=1,
+                padding=1,
+            ),
+            CausalConv3d(
+                out_channels,
+                out_channels,
+                kernel_size=(3, 1, 1),
+                stride=1,
+                padding=0,
+            ),
+        )
+        self.nin_shortcut = (
+            CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+        x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class CausalAttnBlock(nn.Module):
+    def __init__(self, in_channels: int, num_groups: int) -> None:
+        super().__init__()
+
+        self.norm = CausalNormalize(in_channels, num_groups=num_groups)
+        self.q = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+        self.optimized_attention = vae_attention()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        q, batch_size = time2batch(q)
+        k, batch_size = time2batch(k)
+        v, batch_size = time2batch(v)
+
+        b, c, h, w = q.shape
+        h_ = self.optimized_attention(q, k, v)
+
+        h_ = batch2time(h_, batch_size)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class CausalTemporalAttnBlock(nn.Module):
+    def __init__(self, in_channels: int, num_groups: int) -> None:
+        super().__init__()
+
+        self.norm = CausalNormalize(in_channels, num_groups=num_groups)
+        self.q = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = CausalConv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        q, batch_size, height = space2batch(q)
+        k, _, _ = space2batch(k)
+        v, _, _ = space2batch(v)
+
+        bhw, c, t = q.shape
+        q = q.permute(0, 2, 1)  # (bhw, t, c)
+        k = k.permute(0, 2, 1)  # (bhw, t, c)
+        v = v.permute(0, 2, 1)  # (bhw, t, c)
+
+        w_ = torch.bmm(q, k.permute(0, 2, 1))  # (bhw, t, t)
+        w_ = w_ * (int(c) ** (-0.5))
+
+        # Apply causal mask
+        mask = torch.tril(torch.ones_like(w_))
+        w_ = w_.masked_fill(mask == 0, float("-inf"))
+        w_ = F.softmax(w_, dim=2)
+
+        # attend to values
+        h_ = torch.bmm(w_, v)  # (bhw, t, c)
+        h_ = h_.permute(0, 2, 1).reshape(bhw, c, t)  # (bhw, c, t)
+
+        h_ = batch2space(h_, batch_size, height)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class EncoderBase(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        channels: int,
+        channels_mult: list[int],
+        num_res_blocks: int,
+        attn_resolutions: list[int],
+        dropout: float,
+        resolution: int,
+        z_channels: int,
+        **ignore_kwargs,
+    ) -> None:
+        super().__init__()
+        self.num_resolutions = len(channels_mult)
+        self.num_res_blocks = num_res_blocks
+
+        # Patcher.
+        patch_size = ignore_kwargs.get("patch_size", 1)
+        self.patcher = Patcher(
+            patch_size, ignore_kwargs.get("patch_method", "rearrange")
+        )
+        in_channels = in_channels * patch_size * patch_size
+
+        # downsampling
+        self.conv_in = CausalConv3d(
+            in_channels, channels, kernel_size=3, stride=1, padding=1
+        )
+
+        # num of groups for GroupNorm, num_groups=1 for LayerNorm.
+        num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS)
+        curr_res = resolution // patch_size
+        in_ch_mult = (1,) + tuple(channels_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 = channels * in_ch_mult[i_level]
+            block_out = channels * channels_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(
+                    CausalResnetBlock3d(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        dropout=dropout,
+                        num_groups=num_groups,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(CausalAttnBlock(block_in, num_groups=num_groups))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = CausalDownsample3d(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = CausalResnetBlock3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=num_groups,
+        )
+        self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups)
+        self.mid.block_2 = CausalResnetBlock3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=num_groups,
+        )
+
+        # end
+        self.norm_out = CausalNormalize(block_in, num_groups=num_groups)
+        self.conv_out = CausalConv3d(
+            block_in, z_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def patcher3d(self, x: torch.Tensor) -> torch.Tensor:
+        x, batch_size = time2batch(x)
+        x = self.patcher(x)
+        x = batch2time(x, batch_size)
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patcher3d(x)
+
+        # 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])
+                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]))
+            else:
+                # temporal downsample (last level)
+                time_factor = 1 + 1 * (hs[-1].shape[2] > 1)
+                if isinstance(time_factor, torch.Tensor):
+                    time_factor = time_factor.item()
+                hs[-1] = replication_pad(hs[-1])
+                hs.append(
+                    F.avg_pool3d(
+                        hs[-1],
+                        kernel_size=[time_factor, 1, 1],
+                        stride=[2, 1, 1],
+                    )
+                )
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class DecoderBase(nn.Module):
+    def __init__(
+        self,
+        out_channels: int,
+        channels: int,
+        channels_mult: list[int],
+        num_res_blocks: int,
+        attn_resolutions: list[int],
+        dropout: float,
+        resolution: int,
+        z_channels: int,
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        self.num_resolutions = len(channels_mult)
+        self.num_res_blocks = num_res_blocks
+
+        # UnPatcher.
+        patch_size = ignore_kwargs.get("patch_size", 1)
+        self.unpatcher = UnPatcher(
+            patch_size, ignore_kwargs.get("patch_method", "rearrange")
+        )
+        out_ch = out_channels * patch_size * patch_size
+
+        block_in = channels * channels_mult[self.num_resolutions - 1]
+        curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+        logging.debug(
+            "Working with z of shape {} = {} dimensions.".format(
+                self.z_shape, np.prod(self.z_shape)
+            )
+        )
+
+        # z to block_in
+        self.conv_in = CausalConv3d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # num of groups for GroupNorm, num_groups=1 for LayerNorm.
+        num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = CausalResnetBlock3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=num_groups,
+        )
+        self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups)
+        self.mid.block_2 = CausalResnetBlock3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=num_groups,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = channels * channels_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(
+                    CausalResnetBlock3d(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        dropout=dropout,
+                        num_groups=num_groups,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(CausalAttnBlock(block_in, num_groups=num_groups))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = CausalUpsample3d(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = CausalNormalize(block_in, num_groups=num_groups)
+        self.conv_out = CausalConv3d(
+            block_in, out_ch, kernel_size=3, stride=1, padding=1
+        )
+
+    def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor:
+        x, batch_size = time2batch(x)
+        x = self.unpatcher(x)
+        x = batch2time(x, batch_size)
+
+        return x
+
+    def forward(self, z):
+        h = self.conv_in(z)
+
+        # middle block.
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # decoder blocks.
+        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)
+                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)
+            else:
+                # temporal upsample (last level)
+                time_factor = 1.0 + 1.0 * (h.shape[2] > 1)
+                if isinstance(time_factor, torch.Tensor):
+                    time_factor = time_factor.item()
+                h = h.repeat_interleave(int(time_factor), dim=2)
+                h = h[..., int(time_factor - 1) :, :, :]
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        h = self.unpatcher3d(h)
+        return h
+
+
+class EncoderFactorized(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        channels: int,
+        channels_mult: list[int],
+        num_res_blocks: int,
+        attn_resolutions: list[int],
+        dropout: float,
+        resolution: int,
+        z_channels: int,
+        spatial_compression: int = 8,
+        temporal_compression: int = 8,
+        **ignore_kwargs,
+    ) -> None:
+        super().__init__()
+        self.num_resolutions = len(channels_mult)
+        self.num_res_blocks = num_res_blocks
+
+        # Patcher.
+        patch_size = ignore_kwargs.get("patch_size", 1)
+        self.patcher3d = Patcher3D(
+            patch_size, ignore_kwargs.get("patch_method", "haar")
+        )
+        in_channels = in_channels * patch_size * patch_size * patch_size
+
+        # calculate the number of downsample operations
+        self.num_spatial_downs = int(math.log2(spatial_compression)) - int(
+            math.log2(patch_size)
+        )
+        assert (
+            self.num_spatial_downs <= self.num_resolutions
+        ), f"Spatially downsample {self.num_resolutions} times at most"
+
+        self.num_temporal_downs = int(math.log2(temporal_compression)) - int(
+            math.log2(patch_size)
+        )
+        assert (
+            self.num_temporal_downs <= self.num_resolutions
+        ), f"Temporally downsample {self.num_resolutions} times at most"
+
+        # downsampling
+        self.conv_in = nn.Sequential(
+            CausalConv3d(
+                in_channels,
+                channels,
+                kernel_size=(1, 3, 3),
+                stride=1,
+                padding=1,
+            ),
+            CausalConv3d(
+                channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0
+            ),
+        )
+
+        curr_res = resolution // patch_size
+        in_ch_mult = (1,) + tuple(channels_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 = channels * in_ch_mult[i_level]
+            block_out = channels * channels_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(
+                    CausalResnetBlockFactorized3d(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        dropout=dropout,
+                        num_groups=1,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(
+                        nn.Sequential(
+                            CausalAttnBlock(block_in, num_groups=1),
+                            CausalTemporalAttnBlock(block_in, num_groups=1),
+                        )
+                    )
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                spatial_down = i_level < self.num_spatial_downs
+                temporal_down = i_level < self.num_temporal_downs
+                down.downsample = CausalHybridDownsample3d(
+                    block_in,
+                    spatial_down=spatial_down,
+                    temporal_down=temporal_down,
+                )
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = CausalResnetBlockFactorized3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=1,
+        )
+        self.mid.attn_1 = nn.Sequential(
+            CausalAttnBlock(block_in, num_groups=1),
+            CausalTemporalAttnBlock(block_in, num_groups=1),
+        )
+        self.mid.block_2 = CausalResnetBlockFactorized3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=1,
+        )
+
+        # end
+        self.norm_out = CausalNormalize(block_in, num_groups=1)
+        self.conv_out = nn.Sequential(
+            CausalConv3d(
+                block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1
+            ),
+            CausalConv3d(
+                z_channels,
+                z_channels,
+                kernel_size=(3, 1, 1),
+                stride=1,
+                padding=0,
+            ),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patcher3d(x)
+
+        # downsampling
+        h = 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](h)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+            if i_level != self.num_resolutions - 1:
+                h = self.down[i_level].downsample(h)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class DecoderFactorized(nn.Module):
+    def __init__(
+        self,
+        out_channels: int,
+        channels: int,
+        channels_mult: list[int],
+        num_res_blocks: int,
+        attn_resolutions: list[int],
+        dropout: float,
+        resolution: int,
+        z_channels: int,
+        spatial_compression: int = 8,
+        temporal_compression: int = 8,
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        self.num_resolutions = len(channels_mult)
+        self.num_res_blocks = num_res_blocks
+
+        # UnPatcher.
+        patch_size = ignore_kwargs.get("patch_size", 1)
+        self.unpatcher3d = UnPatcher3D(
+            patch_size, ignore_kwargs.get("patch_method", "haar")
+        )
+        out_ch = out_channels * patch_size * patch_size * patch_size
+
+        # calculate the number of upsample operations
+        self.num_spatial_ups = int(math.log2(spatial_compression)) - int(
+            math.log2(patch_size)
+        )
+        assert (
+            self.num_spatial_ups <= self.num_resolutions
+        ), f"Spatially upsample {self.num_resolutions} times at most"
+        self.num_temporal_ups = int(math.log2(temporal_compression)) - int(
+            math.log2(patch_size)
+        )
+        assert (
+            self.num_temporal_ups <= self.num_resolutions
+        ), f"Temporally upsample {self.num_resolutions} times at most"
+
+        block_in = channels * channels_mult[self.num_resolutions - 1]
+        curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+        logging.debug(
+            "Working with z of shape {} = {} dimensions.".format(
+                self.z_shape, np.prod(self.z_shape)
+            )
+        )
+
+        # z to block_in
+        self.conv_in = nn.Sequential(
+            CausalConv3d(
+                z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1
+            ),
+            CausalConv3d(
+                block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0
+            ),
+        )
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = CausalResnetBlockFactorized3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=1,
+        )
+        self.mid.attn_1 = nn.Sequential(
+            CausalAttnBlock(block_in, num_groups=1),
+            CausalTemporalAttnBlock(block_in, num_groups=1),
+        )
+        self.mid.block_2 = CausalResnetBlockFactorized3d(
+            in_channels=block_in,
+            out_channels=block_in,
+            dropout=dropout,
+            num_groups=1,
+        )
+
+        legacy_mode = ignore_kwargs.get("legacy_mode", False)
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = channels * channels_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(
+                    CausalResnetBlockFactorized3d(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        dropout=dropout,
+                        num_groups=1,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(
+                        nn.Sequential(
+                            CausalAttnBlock(block_in, num_groups=1),
+                            CausalTemporalAttnBlock(block_in, num_groups=1),
+                        )
+                    )
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                # The layer index for temporal/spatial downsampling performed
+                # in the encoder should correspond to the layer index in
+                # reverse order where upsampling is performed in the decoder.
+                # If you've a pre-trained model, you can simply finetune.
+                i_level_reverse = self.num_resolutions - i_level - 1
+                if legacy_mode:
+                    temporal_up = i_level_reverse < self.num_temporal_ups
+                else:
+                    temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1
+                spatial_up = temporal_up or (
+                    i_level_reverse < self.num_spatial_ups
+                    and self.num_spatial_ups > self.num_temporal_ups
+                )
+                up.upsample = CausalHybridUpsample3d(
+                    block_in, spatial_up=spatial_up, temporal_up=temporal_up
+                )
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = CausalNormalize(block_in, num_groups=1)
+        self.conv_out = nn.Sequential(
+            CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1),
+            CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0),
+        )
+
+    def forward(self, z):
+        h = self.conv_in(z)
+
+        # middle block.
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # decoder blocks.
+        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)
+                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)
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        h = self.unpatcher3d(h)
+        return h

comfy/ldm/cosmos/cosmos_tokenizer/patching.py

+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The patcher and unpatcher implementation for 2D and 3D data.
+
+The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions.
+One on the rows and one on the columns.
+For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2.
+We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component.
+For H component, we can use a 1D convolution with kernel [1, -1] and stride 2.
+Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all
+   as we need to support downsampling for more than 2x.
+For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be.
+   [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64]
+"""
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+
+_WAVELETS = {
+    "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]),
+    "rearrange": torch.tensor([1.0, 1.0]),
+}
+_PERSISTENT = False
+
+
+class Patcher(torch.nn.Module):
+    """A module to convert image tensors into patches using torch operations.
+
+    The main difference from `class Patching` is that this module implements
+    all operations using torch, rather than python or numpy, for efficiency purpose.
+
+    It's bit-wise identical to the Patching module outputs, with the added
+    benefit of being torch.jit scriptable.
+    """
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__()
+        self.patch_size = patch_size
+        self.patch_method = patch_method
+        self.register_buffer(
+            "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT
+        )
+        self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))
+        self.register_buffer(
+            "_arange",
+            torch.arange(_WAVELETS[patch_method].shape[0]),
+            persistent=_PERSISTENT,
+        )
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, x):
+        if self.patch_method == "haar":
+            return self._haar(x)
+        elif self.patch_method == "rearrange":
+            return self._arrange(x)
+        else:
+            raise ValueError("Unknown patch method: " + self.patch_method)
+
+    def _dwt(self, x, mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        n = h.shape[0]
+        g = x.shape[1]
+        hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype)
+        xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2))
+        xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2))
+        xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1))
+        xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1))
+        xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1))
+        xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1))
+
+        out = torch.cat([xll, xlh, xhl, xhh], dim=1)
+        if rescale:
+            out = out / 2
+        return out
+
+    def _haar(self, x):
+        for _ in self.range:
+            x = self._dwt(x, rescale=True)
+        return x
+
+    def _arrange(self, x):
+        x = rearrange(
+            x,
+            "b c (h p1) (w p2) -> b (c p1 p2) h w",
+            p1=self.patch_size,
+            p2=self.patch_size,
+        ).contiguous()
+        return x
+
+
+class Patcher3D(Patcher):
+    """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos."""
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__(patch_method=patch_method, patch_size=patch_size)
+        self.register_buffer(
+            "patch_size_buffer",
+            patch_size * torch.ones([1], dtype=torch.int32),
+            persistent=_PERSISTENT,
+        )
+
+    def _dwt(self, x, wavelet, mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        n = h.shape[0]
+        g = x.shape[1]
+        hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        # Handles temporal axis.
+        x = F.pad(
+            x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode
+        ).to(dtype)
+        xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+        xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+
+        # Handles spatial axes.
+        xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+
+        xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+
+        out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1)
+        if rescale:
+            out = out / (2 * torch.sqrt(torch.tensor(2.0)))
+        return out
+
+    def _haar(self, x):
+        xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)
+        x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)
+        for _ in self.range:
+            x = self._dwt(x, "haar", rescale=True)
+        return x
+
+    def _arrange(self, x):
+        xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)
+        x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)
+        x = rearrange(
+            x,
+            "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w",
+            p1=self.patch_size,
+            p2=self.patch_size,
+            p3=self.patch_size,
+        ).contiguous()
+        return x
+
+
+class UnPatcher(torch.nn.Module):
+    """A module to convert patches into image tensorsusing torch operations.
+
+    The main difference from `class Unpatching` is that this module implements
+    all operations using torch, rather than python or numpy, for efficiency purpose.
+
+    It's bit-wise identical to the Unpatching module outputs, with the added
+    benefit of being torch.jit scriptable.
+    """
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__()
+        self.patch_size = patch_size
+        self.patch_method = patch_method
+        self.register_buffer(
+            "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT
+        )
+        self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))
+        self.register_buffer(
+            "_arange",
+            torch.arange(_WAVELETS[patch_method].shape[0]),
+            persistent=_PERSISTENT,
+        )
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, x):
+        if self.patch_method == "haar":
+            return self._ihaar(x)
+        elif self.patch_method == "rearrange":
+            return self._iarrange(x)
+        else:
+            raise ValueError("Unknown patch method: " + self.patch_method)
+
+    def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+        n = h.shape[0]
+
+        g = x.shape[1] // 4
+        hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1)
+
+        # Inverse transform.
+        yl = torch.nn.functional.conv_transpose2d(
+            xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yl += torch.nn.functional.conv_transpose2d(
+            xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yh = torch.nn.functional.conv_transpose2d(
+            xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yh += torch.nn.functional.conv_transpose2d(
+            xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        y = torch.nn.functional.conv_transpose2d(
+            yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+        )
+        y += torch.nn.functional.conv_transpose2d(
+            yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+        )
+
+        if rescale:
+            y = y * 2
+        return y
+
+    def _ihaar(self, x):
+        for _ in self.range:
+            x = self._idwt(x, "haar", rescale=True)
+        return x
+
+    def _iarrange(self, x):
+        x = rearrange(
+            x,
+            "b (c p1 p2) h w -> b c (h p1) (w p2)",
+            p1=self.patch_size,
+            p2=self.patch_size,
+        )
+        return x
+
+
+class UnPatcher3D(UnPatcher):
+    """A 3D inverse discrete wavelet transform for video wavelet decompositions."""
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__(patch_method=patch_method, patch_size=patch_size)
+
+    def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        g = x.shape[1] // 8  # split into 8 spatio-temporal filtered tesnors.
+        hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hl = hl.to(dtype=dtype)
+        hh = hh.to(dtype=dtype)
+
+        xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1)
+        del x
+
+        # Height height transposed convolutions.
+        xll = F.conv_transpose3d(
+            xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlll
+
+        xll += F.conv_transpose3d(
+            xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xllh
+
+        xlh = F.conv_transpose3d(
+            xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlhl
+
+        xlh += F.conv_transpose3d(
+            xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlhh
+
+        xhl = F.conv_transpose3d(
+            xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhll
+
+        xhl += F.conv_transpose3d(
+            xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhlh
+
+        xhh = F.conv_transpose3d(
+            xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhhl
+
+        xhh += F.conv_transpose3d(
+            xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhhh
+
+        # Handles width transposed convolutions.
+        xl = F.conv_transpose3d(
+            xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xll
+
+        xl += F.conv_transpose3d(
+            xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xlh
+
+        xh = F.conv_transpose3d(
+            xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xhl
+
+        xh += F.conv_transpose3d(
+            xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xhh
+
+        # Handles time axis transposed convolutions.
+        x = F.conv_transpose3d(
+            xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+        )
+        del xl
+
+        x += F.conv_transpose3d(
+            xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+        )
+
+        if rescale:
+            x = x * (2 * torch.sqrt(torch.tensor(2.0)))
+        return x
+
+    def _ihaar(self, x):
+        for _ in self.range:
+            x = self._idwt(x, "haar", rescale=True)
+        x = x[:, :, self.patch_size - 1 :, ...]
+        return x
+
+    def _iarrange(self, x):
+        x = rearrange(
+            x,
+            "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)",
+            p1=self.patch_size,
+            p2=self.patch_size,
+            p3=self.patch_size,
+        )
+        x = x[:, :, self.patch_size - 1 :, ...]
+        return x

comfy/ldm/cosmos/cosmos_tokenizer/utils.py

+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shared utilities for the networks module."""
+
+from typing import Any
+
+import torch
+from einops import rearrange
+
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
+    batch_size = x.shape[0]
+    return rearrange(x, "b c t h w -> (b t) c h w"), batch_size
+
+
+def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor:
+    return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size)
+
+
+def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
+    batch_size, height = x.shape[0], x.shape[-2]
+    return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height
+
+
+def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor:
+    return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height)
+
+
+def cast_tuple(t: Any, length: int = 1) -> Any:
+    return t if isinstance(t, tuple) else ((t,) * length)
+
+
+def replication_pad(x):
+    return torch.cat([x[:, :, :1, ...], x], dim=2)
+
+
+def divisible_by(num: int, den: int) -> bool:
+    return (num % den) == 0
+
+
+def is_odd(n: int) -> bool:
+    return not divisible_by(n, 2)
+
+
+def nonlinearity(x):
+    # x * sigmoid(x)
+    return torch.nn.functional.silu(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return ops.GroupNorm(
+        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class CausalNormalize(torch.nn.Module):
+    def __init__(self, in_channels, num_groups=1):
+        super().__init__()
+        self.norm = ops.GroupNorm(
+            num_groups=num_groups,
+            num_channels=in_channels,
+            eps=1e-6,
+            affine=True,
+        )
+        self.num_groups = num_groups
+
+    def forward(self, x):
+        # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose.
+        # All new models should use num_groups=1, otherwise causality is not guaranteed.
+        if self.num_groups == 1:
+            x, batch_size = time2batch(x)
+            return batch2time(self.norm(x), batch_size)
+        return self.norm(x)
+
+
+def exists(v):
+    return v is not None
+
+
+def default(*args):
+    for arg in args:
+        if exists(arg):
+            return arg
+    return None
+
+
+def round_ste(z: torch.Tensor) -> torch.Tensor:
+    """Round with straight through gradients."""
+    zhat = z.round()
+    return z + (zhat - z).detach()
+
+
+def log(t, eps=1e-5):
+    return t.clamp(min=eps).log()
+
+
+def entropy(prob):
+    return (-prob * log(prob)).sum(dim=-1)

comfy/ldm/cosmos/model.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
+"""
+
+from typing import Optional, Tuple
+
+import torch
+from einops import rearrange
+from torch import nn
+from torchvision import transforms
+
+from enum import Enum
+import logging
+
+import comfy.patcher_extension
+
+from .blocks import (
+    FinalLayer,
+    GeneralDITTransformerBlock,
+    PatchEmbed,
+    TimestepEmbedding,
+    Timesteps,
+)
+
+from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb
+
+
+class DataType(Enum):
+    IMAGE = "image"
+    VIDEO = "video"
+
+
+class GeneralDIT(nn.Module):
+    """
+    A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
+
+    Args:
+        max_img_h (int): Maximum height of the input images.
+        max_img_w (int): Maximum width of the input images.
+        max_frames (int): Maximum number of frames in the video sequence.
+        in_channels (int): Number of input channels (e.g., RGB channels for color images).
+        out_channels (int): Number of output channels.
+        patch_spatial (tuple): Spatial resolution of patches for input processing.
+        patch_temporal (int): Temporal resolution of patches for input processing.
+        concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.
+        block_config (str): Configuration of the transformer block. See Notes for supported block types.
+        model_channels (int): Base number of channels used throughout the model.
+        num_blocks (int): Number of transformer blocks.
+        num_heads (int): Number of heads in the multi-head attention layers.
+        mlp_ratio (float): Expansion ratio for MLP blocks.
+        block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD').
+        crossattn_emb_channels (int): Number of embedding channels for cross-attention.
+        use_cross_attn_mask (bool): Whether to use mask in cross-attention.
+        pos_emb_cls (str): Type of positional embeddings.
+        pos_emb_learnable (bool): Whether positional embeddings are learnable.
+        pos_emb_interpolation (str): Method for interpolating positional embeddings.
+        affline_emb_norm (bool): Whether to normalize affine embeddings.
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA.
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA.
+        rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.
+        rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.
+        rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.
+        extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.
+        extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings.
+        extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.
+        extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.
+        extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.
+
+    Notes:
+        Supported block types in block_config:
+        * cross_attn, ca: Cross attention
+        * full_attn: Full attention on all flattened tokens
+        * mlp, ff: Feed forward block
+    """
+
+    def __init__(
+        self,
+        max_img_h: int,
+        max_img_w: int,
+        max_frames: int,
+        in_channels: int,
+        out_channels: int,
+        patch_spatial: tuple,
+        patch_temporal: int,
+        concat_padding_mask: bool = True,
+        # attention settings
+        block_config: str = "FA-CA-MLP",
+        model_channels: int = 768,
+        num_blocks: int = 10,
+        num_heads: int = 16,
+        mlp_ratio: float = 4.0,
+        block_x_format: str = "BTHWD",
+        # cross attention settings
+        crossattn_emb_channels: int = 1024,
+        use_cross_attn_mask: bool = False,
+        # positional embedding settings
+        pos_emb_cls: str = "sincos",
+        pos_emb_learnable: bool = False,
+        pos_emb_interpolation: str = "crop",
+        affline_emb_norm: bool = False,  # whether or not to normalize the affine embedding
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        rope_h_extrapolation_ratio: float = 1.0,
+        rope_w_extrapolation_ratio: float = 1.0,
+        rope_t_extrapolation_ratio: float = 1.0,
+        extra_per_block_abs_pos_emb: bool = False,
+        extra_per_block_abs_pos_emb_type: str = "sincos",
+        extra_h_extrapolation_ratio: float = 1.0,
+        extra_w_extrapolation_ratio: float = 1.0,
+        extra_t_extrapolation_ratio: float = 1.0,
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.max_img_h = max_img_h
+        self.max_img_w = max_img_w
+        self.max_frames = max_frames
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.patch_spatial = patch_spatial
+        self.patch_temporal = patch_temporal
+        self.num_heads = num_heads
+        self.num_blocks = num_blocks
+        self.model_channels = model_channels
+        self.use_cross_attn_mask = use_cross_attn_mask
+        self.concat_padding_mask = concat_padding_mask
+        # positional embedding settings
+        self.pos_emb_cls = pos_emb_cls
+        self.pos_emb_learnable = pos_emb_learnable
+        self.pos_emb_interpolation = pos_emb_interpolation
+        self.affline_emb_norm = affline_emb_norm
+        self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio
+        self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio
+        self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio
+        self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb
+        self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower()
+        self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio
+        self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio
+        self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio
+        self.dtype = dtype
+        weight_args = {"device": device, "dtype": dtype}
+
+        in_channels = in_channels + 1 if concat_padding_mask else in_channels
+        self.x_embedder = PatchEmbed(
+            spatial_patch_size=patch_spatial,
+            temporal_patch_size=patch_temporal,
+            in_channels=in_channels,
+            out_channels=model_channels,
+            bias=False,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+        self.build_pos_embed(device=device, dtype=dtype)
+        self.block_x_format = block_x_format
+        self.use_adaln_lora = use_adaln_lora
+        self.adaln_lora_dim = adaln_lora_dim
+        self.t_embedder = nn.ModuleList(
+            [Timesteps(model_channels),
+             TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),]
+        )
+
+        self.blocks = nn.ModuleDict()
+
+        for idx in range(num_blocks):
+            self.blocks[f"block{idx}"] = GeneralDITTransformerBlock(
+                x_dim=model_channels,
+                context_dim=crossattn_emb_channels,
+                num_heads=num_heads,
+                block_config=block_config,
+                mlp_ratio=mlp_ratio,
+                x_format=self.block_x_format,
+                use_adaln_lora=use_adaln_lora,
+                adaln_lora_dim=adaln_lora_dim,
+                weight_args=weight_args,
+                operations=operations,
+            )
+
+        if self.affline_emb_norm:
+            logging.debug("Building affine embedding normalization layer")
+            self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype)
+        else:
+            self.affline_norm = nn.Identity()
+
+        self.final_layer = FinalLayer(
+            hidden_size=self.model_channels,
+            spatial_patch_size=self.patch_spatial,
+            temporal_patch_size=self.patch_temporal,
+            out_channels=self.out_channels,
+            use_adaln_lora=self.use_adaln_lora,
+            adaln_lora_dim=self.adaln_lora_dim,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def build_pos_embed(self, device=None, dtype=None):
+        if self.pos_emb_cls == "rope3d":
+            cls_type = VideoRopePosition3DEmb
+        else:
+            raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}")
+
+        logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}")
+        kwargs = dict(
+            model_channels=self.model_channels,
+            len_h=self.max_img_h // self.patch_spatial,
+            len_w=self.max_img_w // self.patch_spatial,
+            len_t=self.max_frames // self.patch_temporal,
+            is_learnable=self.pos_emb_learnable,
+            interpolation=self.pos_emb_interpolation,
+            head_dim=self.model_channels // self.num_heads,
+            h_extrapolation_ratio=self.rope_h_extrapolation_ratio,
+            w_extrapolation_ratio=self.rope_w_extrapolation_ratio,
+            t_extrapolation_ratio=self.rope_t_extrapolation_ratio,
+            device=device,
+        )
+        self.pos_embedder = cls_type(
+            **kwargs,
+        )
+
+        if self.extra_per_block_abs_pos_emb:
+            assert self.extra_per_block_abs_pos_emb_type in [
+                "learnable",
+            ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}"
+            kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio
+            kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
+            kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
+            kwargs["device"] = device
+            kwargs["dtype"] = dtype
+            self.extra_pos_embedder = LearnablePosEmbAxis(
+                **kwargs,
+            )
+
+    def prepare_embedded_sequence(
+        self,
+        x_B_C_T_H_W: torch.Tensor,
+        fps: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.
+
+        Args:
+            x_B_C_T_H_W (torch.Tensor): video
+            fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
+                                    If None, a default value (`self.base_fps`) will be used.
+            padding_mask (Optional[torch.Tensor]): current it is not used
+
+        Returns:
+            Tuple[torch.Tensor, Optional[torch.Tensor]]:
+                - A tensor of shape (B, T, H, W, D) with the embedded sequence.
+                - An optional positional embedding tensor, returned only if the positional embedding class
+                (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.
+
+        Notes:
+            - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
+            - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
+            - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
+                the `self.pos_embedder` with the shape [T, H, W].
+            - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the
+            `self.pos_embedder` with the fps tensor.
+            - Otherwise, the positional embeddings are generated without considering fps.
+        """
+        if self.concat_padding_mask:
+            if padding_mask is not None:
+                padding_mask = transforms.functional.resize(
+                    padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
+                )
+            else:
+                padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)
+
+            x_B_C_T_H_W = torch.cat(
+                [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
+            )
+        x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
+
+        if self.extra_per_block_abs_pos_emb:
+            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
+        else:
+            extra_pos_emb = None
+
+        if "rope" in self.pos_emb_cls.lower():
+            return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb
+
+        if "fps_aware" in self.pos_emb_cls:
+            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
+        else:
+            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
+
+        return x_B_T_H_W_D, None, extra_pos_emb
+
+    def decoder_head(
+        self,
+        x_B_T_H_W_D: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        origin_shape: Tuple[int, int, int, int, int],  # [B, C, T, H, W]
+        crossattn_mask: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        del crossattn_emb, crossattn_mask
+        B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape
+        x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D")
+        x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D)
+        # This is to ensure x_BT_HW_D has the correct shape because
+        # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D).
+        x_BT_HW_D = x_BT_HW_D.view(
+            B * T_before_patchify // self.patch_temporal,
+            H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial,
+            -1,
+        )
+        x_B_D_T_H_W = rearrange(
+            x_BT_HW_D,
+            "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)",
+            p1=self.patch_spatial,
+            p2=self.patch_spatial,
+            H=H_before_patchify // self.patch_spatial,
+            W=W_before_patchify // self.patch_spatial,
+            t=self.patch_temporal,
+            B=B,
+        )
+        return x_B_D_T_H_W
+
+    def forward_before_blocks(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        fps: Optional[torch.Tensor] = None,
+        image_size: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        scalar_feature: Optional[torch.Tensor] = None,
+        data_type: Optional[DataType] = DataType.VIDEO,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: (B, C, T, H, W) tensor of spatial-temp inputs
+            timesteps: (B, ) tensor of timesteps
+            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
+            crossattn_mask: (B, N) tensor of cross-attention masks
+        """
+        del kwargs
+        assert isinstance(
+            data_type, DataType
+        ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later."
+        original_shape = x.shape
+        x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(
+            x,
+            fps=fps,
+            padding_mask=padding_mask,
+            latent_condition=latent_condition,
+            latent_condition_sigma=latent_condition_sigma,
+        )
+        # logging affline scale information
+        affline_scale_log_info = {}
+
+        timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype))
+        affline_emb_B_D = timesteps_B_D
+        affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach()
+
+        if scalar_feature is not None:
+            raise NotImplementedError("Scalar feature is not implemented yet.")
+
+        affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach()
+        affline_emb_B_D = self.affline_norm(affline_emb_B_D)
+
+        if self.use_cross_attn_mask:
+            if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask):
+                crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max
+            crossattn_mask = crossattn_mask[:, None, None, :]  # .to(dtype=torch.bool)  # [B, 1, 1, length]
+        else:
+            crossattn_mask = None
+
+        if self.blocks["block0"].x_format == "THWBD":
+            x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D")
+            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+                extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange(
+                    extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D"
+                )
+            crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D")
+
+            if crossattn_mask:
+                crossattn_mask = rearrange(crossattn_mask, "B M -> M B")
+
+        elif self.blocks["block0"].x_format == "BTHWD":
+            x = x_B_T_H_W_D
+        else:
+            raise ValueError(f"Unknown x_format {self.blocks[0].x_format}")
+        output = {
+            "x": x,
+            "affline_emb_B_D": affline_emb_B_D,
+            "crossattn_emb": crossattn_emb,
+            "crossattn_mask": crossattn_mask,
+            "rope_emb_L_1_1_D": rope_emb_L_1_1_D,
+            "adaln_lora_B_3D": adaln_lora_B_3D,
+            "original_shape": original_shape,
+            "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+        }
+        return output
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        # crossattn_emb: torch.Tensor,
+        # crossattn_mask: Optional[torch.Tensor] = None,
+        fps: Optional[torch.Tensor] = None,
+        image_size: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        scalar_feature: Optional[torch.Tensor] = None,
+        data_type: Optional[DataType] = DataType.VIDEO,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+        condition_video_augment_sigma: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
+        ).execute(x,
+                timesteps,
+                context,
+                attention_mask,
+                fps,
+                image_size,
+                padding_mask,
+                scalar_feature,
+                data_type,
+                latent_condition,
+                latent_condition_sigma,
+                condition_video_augment_sigma,
+                **kwargs)
+
+    def _forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        # crossattn_emb: torch.Tensor,
+        # crossattn_mask: Optional[torch.Tensor] = None,
+        fps: Optional[torch.Tensor] = None,
+        image_size: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        scalar_feature: Optional[torch.Tensor] = None,
+        data_type: Optional[DataType] = DataType.VIDEO,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+        condition_video_augment_sigma: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            x: (B, C, T, H, W) tensor of spatial-temp inputs
+            timesteps: (B, ) tensor of timesteps
+            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
+            crossattn_mask: (B, N) tensor of cross-attention masks
+            condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to
+                augment condition input, the lvg model will condition on the condition_video_augment_sigma value;
+                we need forward_before_blocks pass to the forward_before_blocks function.
+        """
+
+        crossattn_emb = context
+        crossattn_mask = attention_mask
+
+        inputs = self.forward_before_blocks(
+            x=x,
+            timesteps=timesteps,
+            crossattn_emb=crossattn_emb,
+            crossattn_mask=crossattn_mask,
+            fps=fps,
+            image_size=image_size,
+            padding_mask=padding_mask,
+            scalar_feature=scalar_feature,
+            data_type=data_type,
+            latent_condition=latent_condition,
+            latent_condition_sigma=latent_condition_sigma,
+            condition_video_augment_sigma=condition_video_augment_sigma,
+            **kwargs,
+        )
+        x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = (
+            inputs["x"],
+            inputs["affline_emb_B_D"],
+            inputs["crossattn_emb"],
+            inputs["crossattn_mask"],
+            inputs["rope_emb_L_1_1_D"],
+            inputs["adaln_lora_B_3D"],
+            inputs["original_shape"],
+        )
+        extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype)
+        del inputs
+
+        if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+            assert (
+                x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
+            ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
+
+        for _, block in self.blocks.items():
+            assert (
+                self.blocks["block0"].x_format == block.x_format
+            ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}"
+
+            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+                x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D
+            x = block(
+                x,
+                affline_emb_B_D,
+                crossattn_emb,
+                crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                adaln_lora_B_3D=adaln_lora_B_3D,
+            )
+
+        x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")
+
+        x_B_D_T_H_W = self.decoder_head(
+            x_B_T_H_W_D=x_B_T_H_W_D,
+            emb_B_D=affline_emb_B_D,
+            crossattn_emb=None,
+            origin_shape=original_shape,
+            crossattn_mask=None,
+            adaln_lora_B_3D=adaln_lora_B_3D,
+        )
+
+        return x_B_D_T_H_W