Merge branch 'master' into patch_hooks_improved_memory

README.md

@@ -39,6 +39,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
 - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system

comfy/latent_formats.py

@@ -216,3 +216,7 @@ class Mochi(LatentFormat):
         latents_mean = self.latents_mean.to(latent.device, latent.dtype)
         latents_std = self.latents_std.to(latent.device, latent.dtype)
         return latent * latents_std / self.scale_factor + latents_mean
+
+class LTXV(LatentFormat):
+    latent_channels = 128
+

comfy/ldm/lightricks/model.py

@@ -0,0 +1,502 @@
+import torch
+from torch import nn
+import comfy.ldm.modules.attention
+from comfy.ldm.genmo.joint_model.layers import RMSNorm
+import comfy.ldm.common_dit
+from einops import rearrange
+import math
+from typing import Dict, Optional, Tuple
+
+from .symmetric_patchifier import SymmetricPatchifier
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True,
+        dtype=None, device=None, operations=None,
+    ):
+        super().__init__()
+
+        self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device)
+        else:
+            self.cond_proj = None
+
+        self.act = nn.SiLU()
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+
+        if post_act_fn is None:
+            self.post_act = None
+        # else:
+        #     self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+        )
+        return t_emb
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+    """
+    For PixArt-Alpha.
+
+    Reference:
+    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
+    """
+
+    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.outdim = size_emb_dim
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+        return timesteps_emb
+
+
+class AdaLayerNormSingle(nn.Module):
+    r"""
+    Norm layer adaptive layer norm single (adaLN-single).
+
+    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+        )
+
+        self.silu = nn.SiLU()
+        self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        batch_size: Optional[int] = None,
+        hidden_dtype: Optional[torch.dtype] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # No modulation happening here.
+        added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None}
+        embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
+        return self.linear(self.silu(embedded_timestep)), embedded_timestep
+
+class PixArtAlphaTextProjection(nn.Module):
+    """
+    Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+        super().__init__()
+        if out_features is None:
+            out_features = hidden_size
+        self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+        if act_fn == "gelu_tanh":
+            self.act_1 = nn.GELU(approximate="tanh")
+        elif act_fn == "silu":
+            self.act_1 = nn.SiLU()
+        else:
+            raise ValueError(f"Unknown activation function: {act_fn}")
+        self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class GELU_approx(nn.Module):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return torch.nn.functional.gelu(self.proj(x), approximate="tanh")
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
+    cos_freqs = freqs_cis[0]
+    sin_freqs = freqs_cis[1]
+
+    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+    t1, t2 = t_dup.unbind(dim=-1)
+    t_dup = torch.stack((-t2, t1), dim=-1)
+    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+    return out
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+        self.attn_precision = attn_precision
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+        self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+    def forward(self, x, context=None, mask=None, pe=None):
+        q = self.to_q(x)
+        context = x if context is None else context
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if pe is not None:
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, pe)
+
+        if mask is None:
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+        else:
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.attn_precision = attn_precision
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
+
+        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+
+        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None] + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+
+        x += self.attn2(x, context=context, mask=attention_mask)
+
+        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
+        x += self.ff(y) * gate_mlp
+
+        return x
+
+def get_fractional_positions(indices_grid, max_pos):
+    fractional_positions = torch.stack(
+        [
+            indices_grid[:, i] / max_pos[i]
+            for i in range(3)
+        ],
+        dim=-1,
+    )
+    return fractional_positions
+
+
+def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
+    dtype = torch.float32 #self.dtype
+
+    fractional_positions = get_fractional_positions(indices_grid, max_pos)
+
+    start = 1
+    end = theta
+    device = fractional_positions.device
+
+    indices = theta ** (
+        torch.linspace(
+            math.log(start, theta),
+            math.log(end, theta),
+            dim // 6,
+            device=device,
+            dtype=dtype,
+        )
+    )
+    indices = indices.to(dtype=dtype)
+
+    indices = indices * math.pi / 2
+
+    freqs = (
+        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+        .transpose(-1, -2)
+        .flatten(2)
+    )
+
+    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    if dim % 6 != 0:
+        cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
+        sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
+        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+    return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+
+
+class LTXVModel(torch.nn.Module):
+    def __init__(self,
+                 in_channels=128,
+                 cross_attention_dim=2048,
+                 attention_head_dim=64,
+                 num_attention_heads=32,
+
+                 caption_channels=4096,
+                 num_layers=28,
+
+
+                 positional_embedding_theta=10000.0,
+                 positional_embedding_max_pos=[20, 2048, 2048],
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.adaln_single = AdaLayerNormSingle(
+            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+        )
+
+        # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.caption_projection = PixArtAlphaTextProjection(
+            in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+        )
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    self.inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    context_dim=cross_attention_dim,
+                    # attn_precision=attn_precision,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
+        self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+
+        self.patchifier = SymmetricPatchifier(1)
+
+    def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, **kwargs):
+        indices_grid = self.patchifier.get_grid(
+            orig_num_frames=x.shape[2],
+            orig_height=x.shape[3],
+            orig_width=x.shape[4],
+            batch_size=x.shape[0],
+            scale_grid=((1 / frame_rate) * 8, 32, 32), #TODO: controlable frame rate
+            device=x.device,
+        )
+
+        if guiding_latent is not None:
+            ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype)
+            input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1))
+            ts *= input_ts
+            ts[:, :, 0] = 0.0
+            timestep = self.patchifier.patchify(ts)
+            input_x = x.clone()
+            x[:, :, 0] = guiding_latent[:, :, 0]
+
+        orig_shape = list(x.shape)
+
+        x = self.patchifier.patchify(x)
+
+        x = self.patchify_proj(x)
+        timestep = timestep * 1000.0
+
+        attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
+        attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf"))  # not sure about this
+        # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
+
+        pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+
+        batch_size = x.shape[0]
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=x.dtype,
+        )
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(
+            batch_size, -1, embedded_timestep.shape[-1]
+        )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = x.shape[0]
+            context = self.caption_projection(context)
+            context = context.view(
+                batch_size, -1, x.shape[-1]
+            )
+
+        for block in self.transformer_blocks:
+            x = block(
+                x,
+                context=context,
+                attention_mask=attention_mask,
+                timestep=timestep,
+                pe=pe
+            )
+
+        # 3. Output
+        scale_shift_values = (
+            self.scale_shift_table[None, None] + embedded_timestep[:, :, None]
+        )
+        shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+        x = self.norm_out(x)
+        # Modulation
+        x = x * (1 + scale) + shift
+        x = self.proj_out(x)
+
+        x = self.patchifier.unpatchify(
+            latents=x,
+            output_height=orig_shape[3],
+            output_width=orig_shape[4],
+            output_num_frames=orig_shape[2],
+            out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+        )
+
+        if guiding_latent is not None:
+            x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0]
+
+        # print("res", x)
+        return x

comfy/ldm/lightricks/symmetric_patchifier.py

@@ -0,0 +1,105 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+
+def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor:
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    elif dims_to_append == 0:
+        return x
+    return x[(...,) + (None,) * dims_to_append]
+
+
+class Patchifier(ABC):
+    def __init__(self, patch_size: int):
+        super().__init__()
+        self._patch_size = (1, patch_size, patch_size)
+
+    @abstractmethod
+    def patchify(
+        self, latents: Tensor, frame_rates: Tensor, scale_grid: bool
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @abstractmethod
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @property
+    def patch_size(self):
+        return self._patch_size
+
+    def get_grid(
+        self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device
+    ):
+        f = orig_num_frames // self._patch_size[0]
+        h = orig_height // self._patch_size[1]
+        w = orig_width // self._patch_size[2]
+        grid_h = torch.arange(h, dtype=torch.float32, device=device)
+        grid_w = torch.arange(w, dtype=torch.float32, device=device)
+        grid_f = torch.arange(f, dtype=torch.float32, device=device)
+        grid = torch.meshgrid(grid_f, grid_h, grid_w)
+        grid = torch.stack(grid, dim=0)
+        grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+
+        if scale_grid is not None:
+            for i in range(3):
+                if isinstance(scale_grid[i], Tensor):
+                    scale = append_dims(scale_grid[i], grid.ndim - 1)
+                else:
+                    scale = scale_grid[i]
+                grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i]
+
+        grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size)
+        return grid
+
+
+class SymmetricPatchifier(Patchifier):
+    def patchify(
+        self,
+        latents: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        latents = rearrange(
+            latents,
+            "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
+            p1=self._patch_size[0],
+            p2=self._patch_size[1],
+            p3=self._patch_size[2],
+        )
+        return latents
+
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        output_height = output_height // self._patch_size[1]
+        output_width = output_width // self._patch_size[2]
+        latents = rearrange(
+            latents,
+            "b (f h w) (c p q) -> b c f (h p) (w q) ",
+            f=output_num_frames,
+            h=output_height,
+            w=output_width,
+            p=self._patch_size[1],
+            q=self._patch_size[2],
+        )
+        return latents

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -0,0 +1,62 @@
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+
+
+class CausalConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size: int = 3,
+        stride: Union[int, Tuple[int]] = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        kernel_size = (kernel_size, kernel_size, kernel_size)
+        self.time_kernel_size = kernel_size[0]
+
+        dilation = (dilation, 1, 1)
+
+        height_pad = kernel_size[1] // 2
+        width_pad = kernel_size[2] // 2
+        padding = (0, height_pad, width_pad)
+
+        self.conv = nn.Conv3d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding,
+            padding_mode="zeros",
+            groups=groups,
+        )
+
+    def forward(self, x, causal: bool = True):
+        if causal:
+            first_frame_pad = x[:, :, :1, :, :].repeat(
+                (1, 1, self.time_kernel_size - 1, 1, 1)
+            )
+            x = torch.concatenate((first_frame_pad, x), dim=2)
+        else:
+            first_frame_pad = x[:, :, :1, :, :].repeat(
+                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+            )
+            last_frame_pad = x[:, :, -1:, :, :].repeat(
+                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+            )
+            x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
+        x = self.conv(x)
+        return x
+
+    @property
+    def weight(self):
+        return self.conv.weight

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -0,0 +1,698 @@
+import torch
+from torch import nn
+from functools import partial
+import math
+from einops import rearrange
+from typing import Any, Mapping, Optional, Tuple, Union, List
+from .conv_nd_factory import make_conv_nd, make_linear_nd
+from .pixel_norm import PixelNorm
+
+
+class Encoder(nn.Module):
+    r"""
+    The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
+
+    Args:
+        dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+            The number of dimensions to use in convolutions.
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+            The blocks to use. Each block is a tuple of the block name and the number of layers.
+        base_channels (`int`, *optional*, defaults to 128):
+            The number of output channels for the first convolutional layer.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        patch_size (`int`, *optional*, defaults to 1):
+            The patch size to use. Should be a power of 2.
+        norm_layer (`str`, *optional*, defaults to `group_norm`):
+            The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+        latent_log_var (`str`, *optional*, defaults to `per_channel`):
+            The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`.
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]] = 3,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        blocks=[("res_x", 1)],
+        base_channels: int = 128,
+        norm_num_groups: int = 32,
+        patch_size: Union[int, Tuple[int]] = 1,
+        norm_layer: str = "group_norm",  # group_norm, pixel_norm
+        latent_log_var: str = "per_channel",
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.norm_layer = norm_layer
+        self.latent_channels = out_channels
+        self.latent_log_var = latent_log_var
+        self.blocks_desc = blocks
+
+        in_channels = in_channels * patch_size**2
+        output_channel = base_channels
+
+        self.conv_in = make_conv_nd(
+            dims=dims,
+            in_channels=in_channels,
+            out_channels=output_channel,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.down_blocks = nn.ModuleList([])
+
+        for block_name, block_params in blocks:
+            input_channel = output_channel
+            if isinstance(block_params, int):
+                block_params = {"num_layers": block_params}
+
+            if block_name == "res_x":
+                block = UNetMidBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    num_layers=block_params["num_layers"],
+                    resnet_eps=1e-6,
+                    resnet_groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "res_x_y":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = ResnetBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    eps=1e-6,
+                    groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "compress_time":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 1, 1),
+                    causal=True,
+                )
+            elif block_name == "compress_space":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(1, 2, 2),
+                    causal=True,
+                )
+            elif block_name == "compress_all":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 2, 2),
+                    causal=True,
+                )
+            elif block_name == "compress_all_x_y":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 2, 2),
+                    causal=True,
+                )
+            else:
+                raise ValueError(f"unknown block: {block_name}")
+
+            self.down_blocks.append(block)
+
+        # out
+        if norm_layer == "group_norm":
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+            )
+        elif norm_layer == "pixel_norm":
+            self.conv_norm_out = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+        self.conv_act = nn.SiLU()
+
+        conv_out_channels = out_channels
+        if latent_log_var == "per_channel":
+            conv_out_channels *= 2
+        elif latent_log_var == "uniform":
+            conv_out_channels += 1
+        elif latent_log_var != "none":
+            raise ValueError(f"Invalid latent_log_var: {latent_log_var}")
+        self.conv_out = make_conv_nd(
+            dims, output_channel, conv_out_channels, 3, padding=1, causal=True
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+
+        sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+        sample = self.conv_in(sample)
+
+        checkpoint_fn = (
+            partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+            if self.gradient_checkpointing and self.training
+            else lambda x: x
+        )
+
+        for down_block in self.down_blocks:
+            sample = checkpoint_fn(down_block)(sample)
+
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if self.latent_log_var == "uniform":
+            last_channel = sample[:, -1:, ...]
+            num_dims = sample.dim()
+
+            if num_dims == 4:
+                # For shape (B, C, H, W)
+                repeated_last_channel = last_channel.repeat(
+                    1, sample.shape[1] - 2, 1, 1
+                )
+                sample = torch.cat([sample, repeated_last_channel], dim=1)
+            elif num_dims == 5:
+                # For shape (B, C, F, H, W)
+                repeated_last_channel = last_channel.repeat(
+                    1, sample.shape[1] - 2, 1, 1, 1
+                )
+                sample = torch.cat([sample, repeated_last_channel], dim=1)
+            else:
+                raise ValueError(f"Invalid input shape: {sample.shape}")
+
+        return sample
+
+
+class Decoder(nn.Module):
+    r"""
+    The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
+
+    Args:
+        dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+            The number of dimensions to use in convolutions.
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+            The blocks to use. Each block is a tuple of the block name and the number of layers.
+        base_channels (`int`, *optional*, defaults to 128):
+            The number of output channels for the first convolutional layer.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        patch_size (`int`, *optional*, defaults to 1):
+            The patch size to use. Should be a power of 2.
+        norm_layer (`str`, *optional*, defaults to `group_norm`):
+            The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+        causal (`bool`, *optional*, defaults to `True`):
+            Whether to use causal convolutions or not.
+    """
+
+    def __init__(
+        self,
+        dims,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        blocks=[("res_x", 1)],
+        base_channels: int = 128,
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        patch_size: int = 1,
+        norm_layer: str = "group_norm",
+        causal: bool = True,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.layers_per_block = layers_per_block
+        out_channels = out_channels * patch_size**2
+        self.causal = causal
+        self.blocks_desc = blocks
+
+        # Compute output channel to be product of all channel-multiplier blocks
+        output_channel = base_channels
+        for block_name, block_params in list(reversed(blocks)):
+            block_params = block_params if isinstance(block_params, dict) else {}
+            if block_name == "res_x_y":
+                output_channel = output_channel * block_params.get("multiplier", 2)
+
+        self.conv_in = make_conv_nd(
+            dims,
+            in_channels,
+            output_channel,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.up_blocks = nn.ModuleList([])
+
+        for block_name, block_params in list(reversed(blocks)):
+            input_channel = output_channel
+            if isinstance(block_params, int):
+                block_params = {"num_layers": block_params}
+
+            if block_name == "res_x":
+                block = UNetMidBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    num_layers=block_params["num_layers"],
+                    resnet_eps=1e-6,
+                    resnet_groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "res_x_y":
+                output_channel = output_channel // block_params.get("multiplier", 2)
+                block = ResnetBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    eps=1e-6,
+                    groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "compress_time":
+                block = DepthToSpaceUpsample(
+                    dims=dims, in_channels=input_channel, stride=(2, 1, 1)
+                )
+            elif block_name == "compress_space":
+                block = DepthToSpaceUpsample(
+                    dims=dims, in_channels=input_channel, stride=(1, 2, 2)
+                )
+            elif block_name == "compress_all":
+                block = DepthToSpaceUpsample(
+                    dims=dims,
+                    in_channels=input_channel,
+                    stride=(2, 2, 2),
+                    residual=block_params.get("residual", False),
+                )
+            else:
+                raise ValueError(f"unknown layer: {block_name}")
+
+            self.up_blocks.append(block)
+
+        if norm_layer == "group_norm":
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+            )
+        elif norm_layer == "pixel_norm":
+            self.conv_norm_out = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+        self.conv_act = nn.SiLU()
+        self.conv_out = make_conv_nd(
+            dims, output_channel, out_channels, 3, padding=1, causal=True
+        )
+
+        self.gradient_checkpointing = False
+
+    # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
+    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        r"""The forward method of the `Decoder` class."""
+        # assert target_shape is not None, "target_shape must be provided"
+
+        sample = self.conv_in(sample, causal=self.causal)
+
+        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+
+        checkpoint_fn = (
+            partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+            if self.gradient_checkpointing and self.training
+            else lambda x: x
+        )
+
+        sample = sample.to(upscale_dtype)
+
+        for up_block in self.up_blocks:
+            sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, causal=self.causal)
+
+        sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+
+        return sample
+
+
+class UNetMidBlock3D(nn.Module):
+    """
+    A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks.
+
+    Args:
+        in_channels (`int`): The number of input channels.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
+        num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
+        resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
+        resnet_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use in the group normalization layers of the resnet blocks.
+
+    Returns:
+        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+        in_channels, height, width)`.
+
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]],
+        in_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_groups: int = 32,
+        norm_layer: str = "group_norm",
+    ):
+        super().__init__()
+        resnet_groups = (
+            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        )
+
+        self.res_blocks = nn.ModuleList(
+            [
+                ResnetBlock3D(
+                    dims=dims,
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    norm_layer=norm_layer,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(
+        self, hidden_states: torch.FloatTensor, causal: bool = True
+    ) -> torch.FloatTensor:
+        for resnet in self.res_blocks:
+            hidden_states = resnet(hidden_states, causal=causal)
+
+        return hidden_states
+
+
+class DepthToSpaceUpsample(nn.Module):
+    def __init__(self, dims, in_channels, stride, residual=False):
+        super().__init__()
+        self.stride = stride
+        self.out_channels = math.prod(stride) * in_channels
+        self.conv = make_conv_nd(
+            dims=dims,
+            in_channels=in_channels,
+            out_channels=self.out_channels,
+            kernel_size=3,
+            stride=1,
+            causal=True,
+        )
+        self.residual = residual
+
+    def forward(self, x, causal: bool = True):
+        if self.residual:
+            # Reshape and duplicate the input to match the output shape
+            x_in = rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.stride[0],
+                p2=self.stride[1],
+                p3=self.stride[2],
+            )
+            x_in = x_in.repeat(1, math.prod(self.stride), 1, 1, 1)
+            if self.stride[0] == 2:
+                x_in = x_in[:, :, 1:, :, :]
+        x = self.conv(x, causal=causal)
+        x = rearrange(
+            x,
+            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+        if self.stride[0] == 2:
+            x = x[:, :, 1:, :, :]
+        if self.residual:
+            x = x + x_in
+        return x
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, eps, elementwise_affine=True) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    def forward(self, x):
+        x = rearrange(x, "b c d h w -> b d h w c")
+        x = self.norm(x)
+        x = rearrange(x, "b d h w c -> b c d h w")
+        return x
+
+
+class ResnetBlock3D(nn.Module):
+    r"""
+    A Resnet block.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv layer. If None, same as `in_channels`.
+        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
+        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
+        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]],
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        dropout: float = 0.0,
+        groups: int = 32,
+        eps: float = 1e-6,
+        norm_layer: str = "group_norm",
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        if norm_layer == "group_norm":
+            self.norm1 = nn.GroupNorm(
+                num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+            )
+        elif norm_layer == "pixel_norm":
+            self.norm1 = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+
+        self.non_linearity = nn.SiLU()
+
+        self.conv1 = make_conv_nd(
+            dims,
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        if norm_layer == "group_norm":
+            self.norm2 = nn.GroupNorm(
+                num_groups=groups, num_channels=out_channels, eps=eps, affine=True
+            )
+        elif norm_layer == "pixel_norm":
+            self.norm2 = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True)
+
+        self.dropout = torch.nn.Dropout(dropout)
+
+        self.conv2 = make_conv_nd(
+            dims,
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.conv_shortcut = (
+            make_linear_nd(
+                dims=dims, in_channels=in_channels, out_channels=out_channels
+            )
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+        self.norm3 = (
+            LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+    def forward(
+        self,
+        input_tensor: torch.FloatTensor,
+        causal: bool = True,
+    ) -> torch.FloatTensor:
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+
+        hidden_states = self.non_linearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states, causal=causal)
+
+        hidden_states = self.norm2(hidden_states)
+
+        hidden_states = self.non_linearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+
+        hidden_states = self.conv2(hidden_states, causal=causal)
+
+        input_tensor = self.norm3(input_tensor)
+
+        input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+def patchify(x, patch_size_hw, patch_size_t=1):
+    if patch_size_hw == 1 and patch_size_t == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw
+        )
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c (f p) (h q) (w r) -> b (c p r q) f h w",
+            p=patch_size_t,
+            q=patch_size_hw,
+            r=patch_size_hw,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size_hw, patch_size_t=1):
+    if patch_size_hw == 1 and patch_size_t == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw
+        )
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c p r q) f h w -> b c (f p) (h q) (w r)",
+            p=patch_size_t,
+            q=patch_size_hw,
+            r=patch_size_hw,
+        )
+
+    return x
+
+class processor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.register_buffer("std-of-means", torch.empty(128))
+        self.register_buffer("mean-of-means", torch.empty(128))
+        self.register_buffer("mean-of-stds", torch.empty(128))
+        self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128))
+        self.register_buffer("channel", torch.empty(128))
+
+    def un_normalize(self, x):
+        return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1)
+
+    def normalize(self, x):
+        return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1)
+
+class VideoVAE(nn.Module):
+    def __init__(self):
+        super().__init__()
+        config = {
+            "_class_name": "CausalVideoAutoencoder",
+            "dims": 3,
+            "in_channels": 3,
+            "out_channels": 3,
+            "latent_channels": 128,
+            "blocks": [
+                ["res_x", 4],
+                ["compress_all", 1],
+                ["res_x_y", 1],
+                ["res_x", 3],
+                ["compress_all", 1],
+                ["res_x_y", 1],
+                ["res_x", 3],
+                ["compress_all", 1],
+                ["res_x", 3],
+                ["res_x", 4],
+            ],
+            "scaling_factor": 1.0,
+            "norm_layer": "pixel_norm",
+            "patch_size": 4,
+            "latent_log_var": "uniform",
+            "use_quant_conv": False,
+            "causal_decoder": False,
+        }
+
+        double_z = config.get("double_z", True)
+        latent_log_var = config.get(
+            "latent_log_var", "per_channel" if double_z else "none"
+        )
+
+        self.encoder = Encoder(
+            dims=config["dims"],
+            in_channels=config.get("in_channels", 3),
+            out_channels=config["latent_channels"],
+            blocks=config.get("encoder_blocks", config.get("blocks")),
+            patch_size=config.get("patch_size", 1),
+            latent_log_var=latent_log_var,
+            norm_layer=config.get("norm_layer", "group_norm"),
+        )
+
+        self.decoder = Decoder(
+            dims=config["dims"],
+            in_channels=config["latent_channels"],
+            out_channels=config.get("out_channels", 3),
+            blocks=config.get("decoder_blocks", config.get("blocks")),
+            patch_size=config.get("patch_size", 1),
+            norm_layer=config.get("norm_layer", "group_norm"),
+            causal=config.get("causal_decoder", False),
+        )
+
+        self.per_channel_statistics = processor()
+
+    def encode(self, x):
+        means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
+        return self.per_channel_statistics.normalize(means)
+
+    def decode(self, x):
+        return self.decoder(self.per_channel_statistics.un_normalize(x))
+

comfy/ldm/lightricks/vae/conv_nd_factory.py

@@ -0,0 +1,82 @@
+from typing import Tuple, Union
+
+import torch
+
+from .dual_conv3d import DualConv3d
+from .causal_conv3d import CausalConv3d
+
+
+def make_conv_nd(
+    dims: Union[int, Tuple[int, int]],
+    in_channels: int,
+    out_channels: int,
+    kernel_size: int,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    bias=True,
+    causal=False,
+):
+    if dims == 2:
+        return torch.nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+    elif dims == 3:
+        if causal:
+            return CausalConv3d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=bias,
+            )
+        return torch.nn.Conv3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+    elif dims == (2, 1):
+        return DualConv3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=bias,
+        )
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def make_linear_nd(
+    dims: int,
+    in_channels: int,
+    out_channels: int,
+    bias=True,
+):
+    if dims == 2:
+        return torch.nn.Conv2d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+        )
+    elif dims == 3 or dims == (2, 1):
+        return torch.nn.Conv3d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+        )
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")

comfy/ldm/lightricks/vae/dual_conv3d.py

@@ -0,0 +1,195 @@
+import math
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class DualConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        groups=1,
+        bias=True,
+    ):
+        super(DualConv3d, self).__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        # Ensure kernel_size, stride, padding, and dilation are tuples of length 3
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size, kernel_size, kernel_size)
+        if kernel_size == (1, 1, 1):
+            raise ValueError(
+                "kernel_size must be greater than 1. Use make_linear_nd instead."
+            )
+        if isinstance(stride, int):
+            stride = (stride, stride, stride)
+        if isinstance(padding, int):
+            padding = (padding, padding, padding)
+        if isinstance(dilation, int):
+            dilation = (dilation, dilation, dilation)
+
+        # Set parameters for convolutions
+        self.groups = groups
+        self.bias = bias
+
+        # Define the size of the channels after the first convolution
+        intermediate_channels = (
+            out_channels if in_channels < out_channels else in_channels
+        )
+
+        # Define parameters for the first convolution
+        self.weight1 = nn.Parameter(
+            torch.Tensor(
+                intermediate_channels,
+                in_channels // groups,
+                1,
+                kernel_size[1],
+                kernel_size[2],
+            )
+        )
+        self.stride1 = (1, stride[1], stride[2])
+        self.padding1 = (0, padding[1], padding[2])
+        self.dilation1 = (1, dilation[1], dilation[2])
+        if bias:
+            self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels))
+        else:
+            self.register_parameter("bias1", None)
+
+        # Define parameters for the second convolution
+        self.weight2 = nn.Parameter(
+            torch.Tensor(
+                out_channels, intermediate_channels // groups, kernel_size[0], 1, 1
+            )
+        )
+        self.stride2 = (stride[0], 1, 1)
+        self.padding2 = (padding[0], 0, 0)
+        self.dilation2 = (dilation[0], 1, 1)
+        if bias:
+            self.bias2 = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter("bias2", None)
+
+        # Initialize weights and biases
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5))
+        if self.bias:
+            fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1)
+            bound1 = 1 / math.sqrt(fan_in1)
+            nn.init.uniform_(self.bias1, -bound1, bound1)
+            fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2)
+            bound2 = 1 / math.sqrt(fan_in2)
+            nn.init.uniform_(self.bias2, -bound2, bound2)
+
+    def forward(self, x, use_conv3d=False, skip_time_conv=False):
+        if use_conv3d:
+            return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv)
+        else:
+            return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv)
+
+    def forward_with_3d(self, x, skip_time_conv):
+        # First convolution
+        x = F.conv3d(
+            x,
+            self.weight1,
+            self.bias1,
+            self.stride1,
+            self.padding1,
+            self.dilation1,
+            self.groups,
+        )
+
+        if skip_time_conv:
+            return x
+
+        # Second convolution
+        x = F.conv3d(
+            x,
+            self.weight2,
+            self.bias2,
+            self.stride2,
+            self.padding2,
+            self.dilation2,
+            self.groups,
+        )
+
+        return x
+
+    def forward_with_2d(self, x, skip_time_conv):
+        b, c, d, h, w = x.shape
+
+        # First 2D convolution
+        x = rearrange(x, "b c d h w -> (b d) c h w")
+        # Squeeze the depth dimension out of weight1 since it's 1
+        weight1 = self.weight1.squeeze(2)
+        # Select stride, padding, and dilation for the 2D convolution
+        stride1 = (self.stride1[1], self.stride1[2])
+        padding1 = (self.padding1[1], self.padding1[2])
+        dilation1 = (self.dilation1[1], self.dilation1[2])
+        x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups)
+
+        _, _, h, w = x.shape
+
+        if skip_time_conv:
+            x = rearrange(x, "(b d) c h w -> b c d h w", b=b)
+            return x
+
+        # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension
+        x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b)
+
+        # Reshape weight2 to match the expected dimensions for conv1d
+        weight2 = self.weight2.squeeze(-1).squeeze(-1)
+        # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution
+        stride2 = self.stride2[0]
+        padding2 = self.padding2[0]
+        dilation2 = self.dilation2[0]
+        x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups)
+        x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w)
+
+        return x
+
+    @property
+    def weight(self):
+        return self.weight2
+
+
+def test_dual_conv3d_consistency():
+    # Initialize parameters
+    in_channels = 3
+    out_channels = 5
+    kernel_size = (3, 3, 3)
+    stride = (2, 2, 2)
+    padding = (1, 1, 1)
+
+    # Create an instance of the DualConv3d class
+    dual_conv3d = DualConv3d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        bias=True,
+    )
+
+    # Example input tensor
+    test_input = torch.randn(1, 3, 10, 10, 10)
+
+    # Perform forward passes with both 3D and 2D settings
+    output_conv3d = dual_conv3d(test_input, use_conv3d=True)
+    output_2d = dual_conv3d(test_input, use_conv3d=False)
+
+    # Assert that the outputs from both methods are sufficiently close
+    assert torch.allclose(
+        output_conv3d, output_2d, atol=1e-6
+    ), "Outputs are not consistent between 3D and 2D convolutions."

comfy/ldm/lightricks/vae/pixel_norm.py

@@ -0,0 +1,12 @@
+import torch
+from torch import nn
+
+
+class PixelNorm(nn.Module):
+    def __init__(self, dim=1, eps=1e-8):
+        super(PixelNorm, self).__init__()
+        self.dim = dim
+        self.eps = eps
+
+    def forward(self, x):
+        return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps)

comfy/ldm/modules/attention.py

@@ -299,7 +299,10 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                     if len(mask.shape) == 2:
                         s1 += mask[i:end]
                     else:
-                        s1 += mask[:, i:end]
+                        if mask.shape[1] == 1:
+                            s1 += mask
+                        else:
+                            s1 += mask[:, i:end]
 
                 s2 = s1.softmax(dim=-1).to(v.dtype)
                 del s1

comfy/ldm/modules/sub_quadratic_attention.py

@@ -234,6 +234,8 @@ def efficient_dot_product_attention(
     def get_mask_chunk(chunk_idx: int) -> Tensor:
         if mask is None:
             return None
+        if mask.shape[1] == 1:
+            return mask
         chunk = min(query_chunk_size, q_tokens)
         return mask[:,chunk_idx:chunk_idx + chunk]
 

comfy/model_base.py

@@ -30,6 +30,7 @@ import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.ldm.flux.model
+import comfy.ldm.lightricks.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -153,6 +154,7 @@ class BaseModel(torch.nn.Module):
                     extra = extra.to(dtype)
             extra_conds[o] = extra
 
+        print(t)
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
         return self.model_sampling.calculate_denoised(sigma, model_output, x)
 
@@ -779,3 +781,23 @@ class GenmoMochi(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
+
+class LTXV(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        guiding_latent = kwargs.get("guiding_latent", None)
+        if guiding_latent is not None:
+            out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent)
+
+        out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+        return out

comfy/model_detection.py

@@ -183,6 +183,10 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["rope_theta"] = 10000.0
         return dit_config
 
+    if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
+        dit_config = {}
+        dit_config["image_model"] = "ltxv"
+        return dit_config
 
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None

comfy/sd.py

@@ -10,6 +10,7 @@ from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
 import comfy.ldm.genmo.vae.model
+import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import yaml
 
 import comfy.utils
@@ -29,6 +30,7 @@ import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 import comfy.text_encoders.long_clipl
 import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
 
 import comfy.model_patcher
 import comfy.lora
@@ -334,6 +336,14 @@ class VAE:
                 self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
                 self.working_dtypes = [torch.float16, torch.float32]
+            elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
+                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE()
+                self.latent_channels = 128
+                self.latent_dim = 3
+                self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.upscale_ratio = 8
+                self.working_dtypes = [torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -525,6 +535,7 @@ class CLIPType(Enum):
     HUNYUAN_DIT = 5
     FLUX = 6
     MOCHI = 7
+    LTXV = 8
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
@@ -603,6 +614,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+            elif clip_type == CLIPType.LTXV:
+                clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer

comfy/supported_models.py

@@ -11,6 +11,7 @@ import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
 
 from . import supported_models_base
 from . import latent_formats
@@ -702,7 +703,34 @@ class GenmoMochi(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))
 
+class LTXV(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "ltxv",
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell, GenmoMochi]
+    sampling_settings = {
+        "shift": 2.37,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.LTXV
+
+    memory_usage_factor = 2.7
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.LTXV(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell, GenmoMochi, LTXV]
 
 models += [SVD_img2vid]

comfy/text_encoders/lt.py

@@ -0,0 +1,18 @@
+from comfy import sd1_clip
+import os
+from transformers import T5TokenizerFast
+import comfy.text_encoders.genmo
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) #pad to 128?
+
+
+class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def ltxv_te(*args, **kwargs):
+    return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)

comfy_extras/nodes_lt.py

@@ -0,0 +1,183 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.model_sampling
+import math
+
+class EmptyLTXVLatentVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                              "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                              "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/video/ltxv"
+
+    def generate(self, width, height, length, batch_size=1):
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        return ({"samples": latent}, )
+
+
+class LTXVImgToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE",),
+                             "image": ("IMAGE",),
+                             "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    CATEGORY = "conditioning/video_models"
+    FUNCTION = "generate"
+
+    def generate(self, positive, negative, image, vae, width, height, length, batch_size):
+        pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+        positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t})
+        negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t})
+
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        latent[:, :, :t.shape[2]] = t
+        return (positive, negative, {"samples": latent}, )
+
+
+class LTXVConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning/video_models"
+
+    def append(self, positive, negative, frame_rate):
+        positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate})
+        negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate})
+        return (positive, negative)
+
+
+class ModelSamplingLTXV:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
+                              },
+                "optional": {"latent": ("LATENT",), }
+                }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, max_shift, base_shift, latent=None):
+        m = model.clone()
+
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        shift = (tokens) * mm + b
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+
+class LTXVScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
+                     "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
+                     "stretch": ("BOOLEAN", {
+                        "default": True,
+                        "tooltip": "Stretch the sigmas to be in the range [terminal, 1]."
+                    }),
+                     "terminal": (
+                        "FLOAT",
+                        {
+                            "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01,
+                            "tooltip": "The terminal value of the sigmas after stretching."
+                        },
+                    ),
+                    },
+                "optional": {"latent": ("LATENT",), }
+               }
+
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None):
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        sigmas = torch.linspace(1.0, 0.0, steps + 1)
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        sigma_shift = (tokens) * mm + b
+        print(sigma_shift)
+
+        power = 1
+        sigmas = torch.where(
+            sigmas != 0,
+            math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power),
+            0,
+        )
+
+        # Stretch sigmas so that its final value matches the given terminal value.
+        if stretch:
+            non_zero_mask = sigmas != 0
+            non_zero_sigmas = sigmas[non_zero_mask]
+            one_minus_z = 1.0 - non_zero_sigmas
+            scale_factor = one_minus_z[-1] / (1.0 - terminal)
+            stretched = 1.0 - (one_minus_z / scale_factor)
+            sigmas[non_zero_mask] = stretched
+
+        print(sigmas)
+        return (sigmas,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo,
+    "LTXVImgToVideo": LTXVImgToVideo,
+    "ModelSamplingLTXV": ModelSamplingLTXV,
+    "LTXVConditioning": LTXVConditioning,
+    "LTXVScheduler": LTXVScheduler,
+}

nodes.py

@@ -900,7 +900,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -918,6 +918,8 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.STABLE_AUDIO
         elif type == "mochi":
             clip_type = comfy.sd.CLIPType.MOCHI
+        elif type == "ltxv":
+            clip_type = comfy.sd.CLIPType.LTXV
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
@@ -2139,6 +2141,7 @@ def init_builtin_extra_nodes():
         "nodes_torch_compile.py",
         "nodes_mochi.py",
         "nodes_slg.py",
+        "nodes_lt.py",
         "nodes_hooks.py",
     ]