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from enum import Enum
import functools
import logging
import math
from typing import Dict, Optional, Tuple
from einops import rearrange
import numpy as np
import torch
from torch import nn
import comfy.patcher_extension
import comfy.ldm.modules.attention
import comfy.ldm.common_dit
import comfy.model_management
import comfy.quant_ops
from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
logger = logging.getLogger(__name__)
def _log_base(x, base):
return np.log(x) / np.log(base)
class LTXRopeType(str, Enum):
INTERLEAVED = "interleaved"
SPLIT = "split"
KEY = "rope_type"
@classmethod
def from_dict(cls, kwargs, default=None):
if default is None:
default = cls.INTERLEAVED
return cls(kwargs.get(cls.KEY, default))
class LTXFrequenciesPrecision(str, Enum):
FLOAT32 = "float32"
FLOAT64 = "float64"
KEY = "frequencies_precision"
@classmethod
def from_dict(cls, kwargs, default=None):
if default is None:
default = cls.FLOAT32
return cls(kwargs.get(cls.KEY, default))
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, embedding_coefficient: int = 6, 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, embedding_coefficient * 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 NormSingleLinearTextProjection(nn.Module):
"""Text projection for 20B models - single linear with RMSNorm (no activation)."""
def __init__(
self, in_features, hidden_size, dtype=None, device=None, operations=None
):
super().__init__()
if operations is None:
operations = comfy.ops.disable_weight_init
self.in_norm = operations.RMSNorm(
in_features, eps=1e-6, elementwise_affine=False
)
self.linear_1 = operations.Linear(
in_features, hidden_size, bias=True, dtype=dtype, device=device
)
self.hidden_size = hidden_size
self.in_features = in_features
def forward(self, caption):
caption = self.in_norm(caption)
caption = caption * (self.hidden_size / self.in_features) ** 0.5
return self.linear_1(caption)
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=0.0, 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):
rotation_matrix, split_pe = freqs_cis
original_shape = input_tensor.shape
input_tensor = input_tensor.reshape(
input_tensor.shape[0], input_tensor.shape[1], rotation_matrix.shape[2], -1
)
if comfy.model_management.in_training:
if split_pe:
t = input_tensor.reshape(*input_tensor.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
else:
t = input_tensor.reshape(*input_tensor.shape[:-1], -1, 1, 2)
t = t.to(rotation_matrix.dtype)
output = rotation_matrix[..., 0] * t[..., 0] + rotation_matrix[..., 1] * t[..., 1]
if split_pe:
output = output.movedim(-1, -2)
output = output.reshape(input_tensor.shape).type_as(input_tensor)
elif split_pe:
output = comfy.quant_ops.ck.apply_rope_split_half1(input_tensor, rotation_matrix)
else:
output = comfy.quant_ops.ck.apply_rope1(input_tensor, rotation_matrix)
return output.reshape(original_shape)
def apply_rotary_emb_qk(q, k, freqs_cis):
if comfy.model_management.in_training:
return apply_rotary_emb(q, freqs_cis), apply_rotary_emb(k, freqs_cis)
rotation_matrix, split_pe = freqs_cis
q_shape = q.shape
k_shape = k.shape
q = q.reshape(q.shape[0], q.shape[1], rotation_matrix.shape[2], -1)
k = k.reshape(k.shape[0], k.shape[1], rotation_matrix.shape[2], -1)
if split_pe:
q, k = comfy.quant_ops.ck.apply_rope_split_half(q, k, rotation_matrix)
else:
q, k = comfy.quant_ops.ck.apply_rope(q, k, rotation_matrix)
return q.reshape(q_shape), k.reshape(k_shape)
class GuideAttentionMask:
"""Holds the two per-group masks for LTXV guide self-attention.
_attention_with_guide_mask splits queries into noisy and tracked-guide
groups, so the largest mask is (1, 1, tracked_count, T).
"""
__slots__ = ("guide_start", "tracked_count", "noisy_mask", "tracked_mask")
def __init__(self, total_tokens, guide_start, tracked_count, tracked_weights):
device = tracked_weights.device
dtype = tracked_weights.dtype
finfo = torch.finfo(dtype)
pos = tracked_weights > 0
log_w = torch.full_like(tracked_weights, finfo.min)
log_w[pos] = torch.log(tracked_weights[pos].clamp(min=finfo.tiny))
self.guide_start = guide_start
self.tracked_count = tracked_count
self.noisy_mask = torch.zeros((1, 1, 1, total_tokens), device=device, dtype=dtype)
self.noisy_mask[:, :, :, guide_start:guide_start + tracked_count] = log_w.view(1, 1, 1, -1)
self.tracked_mask = torch.zeros((1, 1, tracked_count, total_tokens), device=device, dtype=dtype)
self.tracked_mask[:, :, :, :guide_start] = log_w.view(1, 1, -1, 1)
def _attention_with_guide_mask(q, k, v, heads, guide_mask, attn_precision, transformer_options):
"""Apply the guide mask by partitioning Q into noisy and tracked-guide
groups, so each group needs only its own sub-mask. Avoids materializing
the (1,1,T,T) dense mask.
"""
guide_start = guide_mask.guide_start
tracked_end = guide_start + guide_mask.tracked_count
out = torch.empty_like(q)
if guide_start > 0: # In practice currently guides are always after noise, guard for safety if this changes.
out[:, :guide_start, :] = comfy.ldm.modules.attention.optimized_attention(
q[:, :guide_start, :], k, v, heads, mask=guide_mask.noisy_mask,
attn_precision=attn_precision, transformer_options=transformer_options,
low_precision_attention=False, # sageattn mask support is unreliable
)
out[:, guide_start:tracked_end, :] = comfy.ldm.modules.attention.optimized_attention(
q[:, guide_start:tracked_end, :], k, v, heads, mask=guide_mask.tracked_mask,
attn_precision=attn_precision, transformer_options=transformer_options,
low_precision_attention=False,
)
if tracked_end < q.shape[1]: # Every guide token is tracked, and nothing comes after them, guard for safety if this changes.
out[:, tracked_end:, :] = comfy.ldm.modules.attention.optimized_attention(
q[:, tracked_end:, :], k, v, heads,
attn_precision=attn_precision, transformer_options=transformer_options,
)
return out
class CrossAttention(nn.Module):
def __init__(
self,
query_dim,
context_dim=None,
heads=8,
dim_head=64,
dropout=0.0,
attn_precision=None,
apply_gated_attention=False,
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 = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)
self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, 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)
# Optional per-head gating
if apply_gated_attention:
self.to_gate_logits = operations.Linear(query_dim, heads, bias=True, dtype=dtype, device=device)
else:
self.to_gate_logits = None
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, k_pe=None, transformer_options={}):
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)
# These norms span all heads, so the per-head RMS+RoPE kernel is not equivalent.
if pe is not None:
if k_pe is None and q.shape == k.shape:
q, k = apply_rotary_emb_qk(q, k, pe)
else:
q = apply_rotary_emb(q, pe)
k = apply_rotary_emb(k, pe if k_pe is None else k_pe)
if mask is None:
out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
elif isinstance(mask, GuideAttentionMask):
out = _attention_with_guide_mask(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
else:
out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, mask=mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
# Apply per-head gating if enabled
if self.to_gate_logits is not None:
gate_logits = self.to_gate_logits(x) # (B, T, H)
b, t, _ = out.shape
out = out.view(b, t, self.heads, self.dim_head)
gates = 2.0 * torch.sigmoid(gate_logits) # zero-init -> identity
out = out * gates.unsqueeze(-1)
out = out.view(b, t, self.heads * self.dim_head)
return self.to_out(out)
# 6 base ADaLN params (shift/scale/gate for MSA + MLP), +3 for cross-attention Q (shift/scale/gate)
ADALN_BASE_PARAMS_COUNT = 6
ADALN_CROSS_ATTN_PARAMS_COUNT = 9
class BasicTransformerBlock(nn.Module):
def __init__(
self, dim, n_heads, d_head, context_dim=None, attn_precision=None, cross_attention_adaln=False, dtype=None, device=None, operations=None
):
super().__init__()
self.attn_precision = attn_precision
self.cross_attention_adaln = cross_attention_adaln
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,
)
num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, dim, device=device, dtype=dtype))
if cross_attention_adaln:
self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype))
def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None, prompt_timestep=None):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None, :6].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, :6, :]).unbind(dim=2)
x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, mask=self_attention_mask, transformer_options=transformer_options) * gate_msa
if self.cross_attention_adaln:
shift_q_mca, scale_q_mca, gate_mca = (self.scale_shift_table[None, None, 6:9].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, 6:9, :]).unbind(dim=2)
x += apply_cross_attention_adaln(
x, context, self.attn2, shift_q_mca, scale_q_mca, gate_mca,
self.prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options,
)
else:
x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
y = comfy.ldm.common_dit.rms_norm(x)
y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
x.addcmul_(self.ff(y), gate_mlp)
return x
def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, hidden_dtype):
"""Compute a single global prompt timestep for cross-attention ADaLN.
Uses the max across tokens (matching JAX max_per_segment) and broadcasts
over text tokens. Returns None when *adaln_module* is None.
"""
if adaln_module is None:
return None
ts_input = (
timestep_scaled.max(dim=1, keepdim=True).values.flatten()
if timestep_scaled.dim() > 1
else timestep_scaled.flatten()
)
prompt_ts, _ = adaln_module(
ts_input,
{"resolution": None, "aspect_ratio": None},
batch_size=batch_size,
hidden_dtype=hidden_dtype,
)
return prompt_ts.view(batch_size, 1, prompt_ts.shape[-1])
def apply_cross_attention_adaln(
x, context, attn, q_shift, q_scale, q_gate,
prompt_scale_shift_table, prompt_timestep,
attention_mask=None, transformer_options={},
):
"""Apply cross-attention with ADaLN modulation (shift/scale/gate on Q and KV).
Q params (q_shift, q_scale, q_gate) are pre-extracted by the caller so
that both regular tensors and CompressedTimestep are supported.
"""
batch_size = x.shape[0]
shift_kv, scale_kv = (
prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+ prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1)
).unbind(dim=2)
attn_input = comfy.ldm.common_dit.rms_norm(x) * (1 + q_scale) + q_shift
encoder_hidden_states = context * (1 + scale_kv) + shift_kv
return attn(attn_input, context=encoder_hidden_states, mask=attention_mask, transformer_options=transformer_options) * q_gate
def get_fractional_positions(indices_grid, max_pos):
n_pos_dims = indices_grid.shape[1]
assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})'
fractional_positions = torch.stack(
[indices_grid[:, i] / max_pos[i] for i in range(n_pos_dims)],
axis=-1,
)
return fractional_positions
@functools.lru_cache(maxsize=5)
def generate_freq_grid_np(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, _ = None):
theta = positional_embedding_theta
start = 1
end = theta
n_elem = 2 * positional_embedding_max_pos_count
pow_indices = np.power(
theta,
np.linspace(
_log_base(start, theta),
_log_base(end, theta),
inner_dim // n_elem,
dtype=np.float64,
),
)
return torch.tensor(pow_indices * math.pi / 2, dtype=torch.float32)
def generate_freq_grid_pytorch(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, device):
theta = positional_embedding_theta
start = 1
end = theta
n_elem = 2 * positional_embedding_max_pos_count
indices = theta ** (
torch.linspace(
math.log(start, theta),
math.log(end, theta),
inner_dim // n_elem,
device=device,
dtype=torch.float32,
)
)
indices = indices.to(dtype=torch.float32)
indices = indices * math.pi / 2
return indices
def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid):
if use_middle_indices_grid:
assert(len(indices_grid.shape) == 4 and indices_grid.shape[-1] ==2)
indices_grid_start, indices_grid_end = indices_grid[..., 0], indices_grid[..., 1]
indices_grid = (indices_grid_start + indices_grid_end) / 2.0
elif len(indices_grid.shape) == 4:
indices_grid = indices_grid[..., 0]
# Get fractional positions and compute frequency indices
fractional_positions = get_fractional_positions(indices_grid, max_pos)
indices = indices.to(device=fractional_positions.device)
freqs = (
(indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
.transpose(-1, -2)
.flatten(2)
)
return freqs
def freqs_cis_matrix(freqs, pad_size, split_mode, num_attention_heads, out_dtype):
cos_freq = freqs.cos().to(out_dtype)
sin_freq = freqs.sin().to(out_dtype)
if pad_size:
matrix_pad_size = pad_size if split_mode else pad_size // 2
cos_padding = torch.ones_like(cos_freq[:, :, :matrix_pad_size])
sin_padding = torch.zeros_like(sin_freq[:, :, :matrix_pad_size])
cos_freq = torch.cat((cos_padding, cos_freq), dim=-1)
sin_freq = torch.cat((sin_padding, sin_freq), dim=-1)
B, T, half_HD = cos_freq.shape
cos_freq = cos_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
sin_freq = sin_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
rotation_matrix = torch.stack(
(cos_freq, -sin_freq, sin_freq, cos_freq), dim=-1
)
return rotation_matrix.reshape(*rotation_matrix.shape[:-1], 2, 2), split_mode
class LTXBaseModel(torch.nn.Module, ABC):
"""
Abstract base class for LTX models (Lightricks Transformer models).
This class defines the common interface and shared functionality for all LTX models,
including LTXV (video) and LTXAV (audio-video) variants.
"""
def __init__(
self,
in_channels: int,
cross_attention_dim: int,
attention_head_dim: int,
num_attention_heads: int,
caption_channels: int,
num_layers: int,
positional_embedding_theta: float = 10000.0,
positional_embedding_max_pos: list = [20, 2048, 2048],
causal_temporal_positioning: bool = False,
vae_scale_factors: tuple = (8, 32, 32),
use_middle_indices_grid=False,
timestep_scale_multiplier = 1000.0,
caption_proj_before_connector=False,
cross_attention_adaln=False,
caption_projection_first_linear=True,
dtype=None,
device=None,
operations=None,
**kwargs,
):
super().__init__()
self.generator = None
self.vae_scale_factors = vae_scale_factors
self.use_middle_indices_grid = use_middle_indices_grid
self.dtype = dtype
self.in_channels = in_channels
self.cross_attention_dim = cross_attention_dim
self.attention_head_dim = attention_head_dim
self.num_attention_heads = num_attention_heads
self.caption_channels = caption_channels
self.num_layers = num_layers
self.positional_embedding_theta = positional_embedding_theta
self.positional_embedding_max_pos = positional_embedding_max_pos
self.split_positional_embedding = LTXRopeType.from_dict(kwargs)
self.freq_grid_generator = (
generate_freq_grid_np if LTXFrequenciesPrecision.from_dict(kwargs) == LTXFrequenciesPrecision.FLOAT64
else generate_freq_grid_pytorch
)
self.causal_temporal_positioning = causal_temporal_positioning
self.operations = operations
self.timestep_scale_multiplier = timestep_scale_multiplier
self.caption_proj_before_connector = caption_proj_before_connector
self.cross_attention_adaln = cross_attention_adaln
self.caption_projection_first_linear = caption_projection_first_linear
# Common dimensions
self.inner_dim = num_attention_heads * attention_head_dim
self.out_channels = in_channels
# Initialize common components
self._init_common_components(device, dtype)
# Initialize model-specific components
self._init_model_components(device, dtype, **kwargs)
# Initialize transformer blocks
self._init_transformer_blocks(device, dtype, **kwargs)
# Initialize output components
self._init_output_components(device, dtype)
def _init_common_components(self, device, dtype):
"""Initialize components common to all LTX models
- patchify_proj: Linear projection for patchifying input
- adaln_single: AdaLN layer for timestep embedding
- caption_projection: Linear projection for caption embedding
"""
self.patchify_proj = self.operations.Linear(
self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device
)
embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
self.adaln_single = AdaLayerNormSingle(
self.inner_dim, embedding_coefficient=embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
)
if self.cross_attention_adaln:
self.prompt_adaln_single = AdaLayerNormSingle(
self.inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
)
else:
self.prompt_adaln_single = None
if self.caption_proj_before_connector:
if self.caption_projection_first_linear:
self.caption_projection = NormSingleLinearTextProjection(
in_features=self.caption_channels,
hidden_size=self.inner_dim,
dtype=dtype,
device=device,
operations=self.operations,
)
else:
self.caption_projection = lambda a: a
else:
self.caption_projection = PixArtAlphaTextProjection(
in_features=self.caption_channels,
hidden_size=self.inner_dim,
dtype=dtype,
device=device,
operations=self.operations,
)
@abstractmethod
def _init_model_components(self, device, dtype, **kwargs):
"""Initialize model-specific components. Must be implemented by subclasses."""
pass
@abstractmethod
def _init_transformer_blocks(self, device, dtype, **kwargs):
"""Initialize transformer blocks. Must be implemented by subclasses."""
pass
@abstractmethod
def _init_output_components(self, device, dtype):
"""Initialize output components. Must be implemented by subclasses."""
pass
@abstractmethod
def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
"""Process input data. Must be implemented by subclasses."""
pass
def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):
"""Build self-attention mask for per-guide attention attenuation.
Base implementation returns None (no attenuation). Subclasses that
support guide-based attention control should override this.
"""
return None
@abstractmethod
def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, self_attention_mask=None, **kwargs):
"""Process transformer blocks. Must be implemented by subclasses."""
pass
@abstractmethod
def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
"""Process output data. Must be implemented by subclasses."""
pass
def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
"""Prepare timestep embeddings."""
grid_mask = kwargs.get("grid_mask", None)
if grid_mask is not None:
timestep = timestep[:, grid_mask]
timestep_scaled = timestep * self.timestep_scale_multiplier
timestep, embedded_timestep = self.adaln_single(
timestep_scaled.flatten(),
{"resolution": None, "aspect_ratio": None},
batch_size=batch_size,
hidden_dtype=hidden_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])
prompt_timestep = compute_prompt_timestep(
self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
)
return timestep, embedded_timestep, prompt_timestep
def _prepare_context(self, context, batch_size, x, attention_mask=None):
"""Prepare context for transformer blocks."""
if self.caption_proj_before_connector is False:
context = self.caption_projection(context)
context = context.view(batch_size, -1, x.shape[-1])
return context, attention_mask
def _precompute_freqs_cis(
self,
indices_grid,
dim,
out_dtype,
theta=10000.0,
max_pos=[20, 2048, 2048],
use_middle_indices_grid=False,
num_attention_heads=32,
):
split_mode = self.split_positional_embedding == LTXRopeType.SPLIT
indices = self.freq_grid_generator(theta, indices_grid.shape[1], dim, indices_grid.device)
freqs = generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid)
if split_mode:
expected_freqs = dim // 2
current_freqs = freqs.shape[-1]
pad_size = expected_freqs - current_freqs
else:
# 2 because of cos and sin by 3 for (t, x, y), 1 for temporal only
n_elem = 2 * indices_grid.shape[1]
pad_size = dim % n_elem
return freqs_cis_matrix(
freqs,
pad_size,
split_mode,
num_attention_heads,
out_dtype,
)
def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
"""Prepare positional embeddings."""
fractional_coords = pixel_coords.to(torch.float32)
fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
pe = self._precompute_freqs_cis(
fractional_coords,
dim=self.inner_dim,
out_dtype=x_dtype,
max_pos=self.positional_embedding_max_pos,
use_middle_indices_grid=self.use_middle_indices_grid,
num_attention_heads=self.num_attention_heads,
)
return pe
def _prepare_attention_mask(self, attention_mask, x_dtype):
"""Prepare attention mask."""
if attention_mask is not None and not torch.is_floating_point(attention_mask):
attention_mask = (attention_mask - 1).to(x_dtype).reshape(
(attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
) * torch.finfo(x_dtype).max
return attention_mask
def forward(
self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
):
"""
Forward pass for LTX models.
Args:
x: Input tensor
timestep: Timestep tensor
context: Context tensor (e.g., text embeddings)
attention_mask: Attention mask tensor
frame_rate: Frame rate for temporal processing
transformer_options: Additional options for transformer blocks
keyframe_idxs: Keyframe indices for temporal processing
**kwargs: Additional keyword arguments
Returns:
Processed output tensor
"""
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, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)
def _forward(
self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
):
"""
Internal forward pass for LTX models.
Args:
x: Input tensor
timestep: Timestep tensor
context: Context tensor (e.g., text embeddings)
attention_mask: Attention mask tensor
frame_rate: Frame rate for temporal processing
transformer_options: Additional options for transformer blocks
keyframe_idxs: Keyframe indices for temporal processing
**kwargs: Additional keyword arguments
Returns:
Processed output tensor
"""
if isinstance(x, list):
input_dtype = x[0].dtype
batch_size = x[0].shape[0]
else:
input_dtype = x.dtype
batch_size = x.shape[0]
# Process input
merged_args = {**transformer_options, **kwargs}
x, pixel_coords, additional_args = self._process_input(x, keyframe_idxs, denoise_mask, **merged_args)
merged_args.update(additional_args)
# Prepare timestep and context
timestep, embedded_timestep, prompt_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
merged_args["prompt_timestep"] = prompt_timestep
context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)
# Prepare attention mask and positional embeddings
attention_mask = self._prepare_attention_mask(attention_mask, input_dtype)
pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype)
# Build self-attention mask for per-guide attenuation
self_attention_mask = self._build_guide_self_attention_mask(
x, transformer_options, merged_args
)
# Process transformer blocks
x = self._process_transformer_blocks(
x, context, attention_mask, timestep, pe,
transformer_options=transformer_options,
self_attention_mask=self_attention_mask,
**merged_args,
)
# Process output
x = self._process_output(x, embedded_timestep, keyframe_idxs, **merged_args)
return x
class LTXVModel(LTXBaseModel):
"""LTXV model for video generation."""
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],
causal_temporal_positioning=False,
vae_scale_factors=(8, 32, 32),
use_middle_indices_grid=False,
timestep_scale_multiplier=1000.0,
caption_proj_before_connector=False,
cross_attention_adaln=False,
dtype=None,
device=None,
operations=None,
**kwargs,
):
super().__init__(
in_channels=in_channels,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
num_attention_heads=num_attention_heads,
caption_channels=caption_channels,
num_layers=num_layers,
positional_embedding_theta=positional_embedding_theta,
positional_embedding_max_pos=positional_embedding_max_pos,
causal_temporal_positioning=causal_temporal_positioning,
vae_scale_factors=vae_scale_factors,
use_middle_indices_grid=use_middle_indices_grid,
timestep_scale_multiplier=timestep_scale_multiplier,
caption_proj_before_connector=caption_proj_before_connector,
cross_attention_adaln=cross_attention_adaln,
dtype=dtype,
device=device,
operations=operations,
**kwargs,
)
def _init_model_components(self, device, dtype, **kwargs):
"""Initialize LTXV-specific components."""
pass
def _init_transformer_blocks(self, device, dtype, **kwargs):
"""Initialize transformer blocks for LTXV."""
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
self.inner_dim,
self.num_attention_heads,
self.attention_head_dim,
context_dim=self.cross_attention_dim,
cross_attention_adaln=self.cross_attention_adaln,
dtype=dtype,
device=device,
operations=self.operations,
)
for _ in range(self.num_layers)
]
)
def _init_output_components(self, device, dtype):
"""Initialize output components for LTXV."""
self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
self.norm_out = self.operations.LayerNorm(
self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
)
self.proj_out = self.operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
self.patchifier = SymmetricPatchifier(1, start_end=True)
def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
"""Process input for LTXV."""
additional_args = {"orig_shape": list(x.shape)}
x, latent_coords = self.patchifier.patchify(x)
pixel_coords = latent_to_pixel_coords(
latent_coords=latent_coords,
scale_factors=self.vae_scale_factors,
causal_fix=self.causal_temporal_positioning,
)
grid_mask = None
if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0:
additional_args.update({ "orig_patchified_shape": list(x.shape)})
denoise_mask = self.patchifier.patchify(denoise_mask)[0]
grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0]
additional_args.update({"grid_mask": grid_mask})
x = x[:, grid_mask, :]
pixel_coords = pixel_coords[:, :, grid_mask, ...]
kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:]
# Compute per-guide surviving token counts from guide_attention_entries.
# Each entry tracks one guide reference; they are appended in order and
# their pre_filter_counts partition the kf_grid_mask.
guide_entries = kwargs.get("guide_attention_entries", None)
if guide_entries:
total_pfc = sum(e["pre_filter_count"] for e in guide_entries)
if total_pfc != len(kf_grid_mask):
raise ValueError(
f"guide pre_filter_counts ({total_pfc}) != "
f"keyframe grid mask length ({len(kf_grid_mask)})"
)
resolved_entries = []
offset = 0
for entry in guide_entries:
pfc = entry["pre_filter_count"]
entry_mask = kf_grid_mask[offset:offset + pfc]
surviving = int(entry_mask.sum().item())
resolved_entries.append({
**entry,
"surviving_count": surviving,
})
offset += pfc
additional_args["resolved_guide_entries"] = resolved_entries
keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]
if keyframe_idxs.shape[2] > 0: # Guard for the case of no keyframes surviving
pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
# Total surviving guide tokens (all guides)
additional_args["num_guide_tokens"] = keyframe_idxs.shape[2]
x = self.patchify_proj(x)
return x, pixel_coords, additional_args
def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):
"""Build self-attention mask for per-guide attention attenuation.
Reads resolved_guide_entries from merged_args (computed in _process_input)
to build a log-space additive bias mask that attenuates noisy ↔ guide
attention for each guide reference independently.
Returns None if no attenuation is needed (all strengths == 1.0 and no
spatial masks, or no guide tokens).
"""
if isinstance(x, list):
# AV model: x = [vx, ax]; use vx for token count and device
total_tokens = x[0].shape[1]
device = x[0].device
dtype = x[0].dtype
else:
total_tokens = x.shape[1]
device = x.device
dtype = x.dtype
num_guide_tokens = merged_args.get("num_guide_tokens", 0)
if num_guide_tokens == 0:
return None
resolved_entries = merged_args.get("resolved_guide_entries", None)
if not resolved_entries:
return None
# strength != 1.0 means we want to either attenuate (< 1) or amplify (> 1) guide attention.
needs_mask = any(
e["strength"] != 1.0 or e.get("pixel_mask") is not None
for e in resolved_entries
)
if not needs_mask:
return None
# Build per-guide-token weights for all tracked guide tokens.
# Guides are appended in order at the end of the sequence.
guide_start = total_tokens - num_guide_tokens
all_weights = []
total_tracked = 0
for entry in resolved_entries:
surviving = entry["surviving_count"]
if surviving == 0:
continue
strength = entry["strength"]
pixel_mask = entry.get("pixel_mask")
latent_shape = entry.get("latent_shape")
if pixel_mask is not None and latent_shape is not None:
f_lat, h_lat, w_lat = latent_shape
per_token = self._downsample_mask_to_latent(
pixel_mask.to(device=device, dtype=dtype),
f_lat, h_lat, w_lat,
)
# per_token shape: (B, f_lat*h_lat*w_lat).
# Collapse batch dim — the mask is assumed identical across the
# batch; validate and take the first element to get (1, tokens).
if per_token.shape[0] > 1:
ref = per_token[0]
for bi in range(1, per_token.shape[0]):
if not torch.equal(ref, per_token[bi]):
logger.warning(
"pixel_mask differs across batch elements; "
"using first element only."
)
break
per_token = per_token[:1]
# `surviving` is the post-grid_mask token count.
# Clamp to surviving to handle any mismatch safely.
n_weights = min(per_token.shape[1], surviving)
weights = per_token[:, :n_weights] * strength # (1, n_weights)
else:
weights = torch.full(
(1, surviving), strength, device=device, dtype=dtype
)
all_weights.append(weights)
total_tracked += weights.shape[1]
if not all_weights:
return None
# Concatenate per-token weights for all tracked guides
tracked_weights = torch.cat(all_weights, dim=1) # (1, total_tracked)
# Skip when every weight is exactly 1.0 (additive bias would be 0).
if (tracked_weights == 1.0).all():
return None
return GuideAttentionMask(total_tokens, guide_start, total_tracked, tracked_weights)
@staticmethod
def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):
"""Downsample a pixel-space mask to per-token latent weights.
Args:
mask: (B, 1, F_pix, H_pix, W_pix) pixel-space mask with values in [0, 1].
f_lat: Number of latent frames (pre-dilation original count).
h_lat: Latent height (pre-dilation original height).
w_lat: Latent width (pre-dilation original width).
Returns:
(B, F_lat * H_lat * W_lat) flattened per-token weights.
"""
b = mask.shape[0]
f_pix = mask.shape[2]
# Spatial downsampling: area interpolation per frame
spatial_down = torch.nn.functional.interpolate(
rearrange(mask, "b 1 f h w -> (b f) 1 h w"),
size=(h_lat, w_lat),
mode="area",
)
spatial_down = rearrange(spatial_down, "(b f) 1 h w -> b 1 f h w", b=b)
# Temporal downsampling: first pixel frame maps to first latent frame,
# remaining pixel frames are averaged in groups for causal temporal structure.
first_frame = spatial_down[:, :, :1, :, :]
if f_pix > 1 and f_lat > 1:
remaining_pix = f_pix - 1
remaining_lat = f_lat - 1
t = remaining_pix // remaining_lat
if t < 1:
# Fewer pixel frames than latent frames — upsample by repeating
# the available pixel frames via nearest interpolation.
rest_flat = rearrange(
spatial_down[:, :, 1:, :, :],
"b 1 f h w -> (b h w) 1 f",
)
rest_up = torch.nn.functional.interpolate(
rest_flat, size=remaining_lat, mode="nearest",
)
rest = rearrange(
rest_up, "(b h w) 1 f -> b 1 f h w",
b=b, h=h_lat, w=w_lat,
)
else:
# Trim trailing pixel frames that don't fill a complete group
usable = remaining_lat * t
rest = rearrange(
spatial_down[:, :, 1:1 + usable, :, :],
"b 1 (f t) h w -> b 1 f t h w",
t=t,
)
rest = rest.mean(dim=3)
latent_mask = torch.cat([first_frame, rest], dim=2)
elif f_lat > 1:
# Single pixel frame but multiple latent frames — repeat the
# single frame across all latent frames.
latent_mask = first_frame.expand(-1, -1, f_lat, -1, -1)
else:
latent_mask = first_frame
return rearrange(latent_mask, "b 1 f h w -> b (f h w)")
def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs):
"""Process transformer blocks for LTXV."""
patches_replace = transformer_options.get("patches_replace", {})
blocks_replace = patches_replace.get("dit", {})
prompt_timestep = kwargs.get("prompt_timestep", None)
for i, block in enumerate(self.transformer_blocks):
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"), prompt_timestep=args.get("prompt_timestep"))
return out
out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask, "prompt_timestep": prompt_timestep}, {"original_block": block_wrap})
x = out["img"]
else:
x = block(
x,
context=context,
attention_mask=attention_mask,
timestep=timestep,
pe=pe,
transformer_options=transformer_options,
self_attention_mask=self_attention_mask,
prompt_timestep=prompt_timestep,
)
return x
def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
"""Process output for LTXV."""
# Apply scale-shift modulation
scale_shift_values = (
self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
)
shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
x = self.norm_out(x)
x = x * (1 + scale) + shift
x = self.proj_out(x)
if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0:
grid_mask = kwargs["grid_mask"]
orig_patchified_shape = kwargs["orig_patchified_shape"]
full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device)
full_x[:, grid_mask, :] = x
x = full_x
# Unpatchify to restore original dimensions
orig_shape = kwargs["orig_shape"]
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),
)
return x
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