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sglang/python/sglang/multimodal_gen/configs/models/dits/ltx_2.py
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# SPDX-License-Identifier: Apache-2.0
from dataclasses import dataclass, field
from enum import Enum
from sglang.multimodal_gen.configs.models.dits.base import DiTArchConfig, DiTConfig
class LTXModelType(Enum):
"""
Model type enum mirroring upstream `LTXModelType`.
Upstream reference:
- `LTX-2/packages/ltx-core/src/ltx_core/model/transformer/model.py::LTXModelType`
"""
AudioVideo = "ltx av model"
VideoOnly = "ltx video only model"
AudioOnly = "ltx audio only model"
def is_video_enabled(self) -> bool:
return self in (LTXModelType.AudioVideo, LTXModelType.VideoOnly)
def is_audio_enabled(self) -> bool:
return self in (LTXModelType.AudioVideo, LTXModelType.AudioOnly)
class LTX2RopeType(str, Enum):
"""
Minimal RoPE type enum mirroring LTX-2 upstream `LTXRopeType`.
Upstream reference:
- `LTX-2/packages/ltx-core/src/ltx_core/model/transformer/rope.py::LTXRopeType`
"""
INTERLEAVED = "interleaved"
SPLIT = "split"
class LTX2AttentionFunction(str, Enum):
"""
Placeholder enum for upstream `AttentionFunction.DEFAULT`.
Upstream reference:
- `LTX-2/packages/ltx-core/src/ltx_core/model/transformer/attention.py`
"""
DEFAULT = "default"
def is_blocks(n: str, m) -> bool:
return "blocks" in n and str.isdigit(n.split(".")[-1])
@dataclass
class LTX2ArchConfig(DiTArchConfig):
"""Architecture configuration for LTX-2 Video Transformer."""
_fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
param_names_mapping: dict = field(
default_factory=lambda: {
# Parameter name mappings from HuggingFace checkpoint keys to SGLang module names.
# We use upstream variable names (patchify_proj, adaln_single) but HF uses different keys.
#
# HF key -> SGLang key (upstream naming)
r"^proj_in\.(.*)$": r"patchify_proj.\1",
r"^time_embed\.(.*)$": r"adaln_single.\1",
r"^audio_proj_in\.(.*)$": r"audio_patchify_proj.\1",
r"^audio_time_embed\.(.*)$": r"audio_adaln_single.\1",
# FeedForward
r"(.*)ff\.net\.0\.proj\.(.*)$": r"\1ff.proj_in.\2",
r"(.*)ff\.net\.2\.(.*)$": r"\1ff.proj_out.\2",
# Attention Norms
r"(.*)\.norm_q\.(.*)$": r"\1.q_norm.\2",
r"(.*)\.norm_k\.(.*)$": r"\1.k_norm.\2",
# Scale Shift Tables (Global)
r"^av_cross_attn_video_scale_shift\.(.*)$": r"av_ca_video_scale_shift_adaln_single.\1",
r"^av_cross_attn_audio_scale_shift\.(.*)$": r"av_ca_audio_scale_shift_adaln_single.\1",
r"^av_cross_attn_video_a2v_gate\.(.*)$": r"av_ca_a2v_gate_adaln_single.\1",
r"^av_cross_attn_audio_v2a_gate\.(.*)$": r"av_ca_v2a_gate_adaln_single.\1",
# Scale Shift Tables (Block Level)
# HF: scale_shift_table_a2v_ca_video -> SGLang: video_a2v_cross_attn_scale_shift_table
r"(.*)scale_shift_table_a2v_ca_video": r"\1video_a2v_cross_attn_scale_shift_table",
r"(.*)scale_shift_table_a2v_ca_audio": r"\1audio_a2v_cross_attn_scale_shift_table",
}
)
reverse_param_names_mapping: dict = field(
default_factory=lambda: {
# Reverse mapping: SGLang module names -> HF checkpoint keys (for saving).
r"^patchify_proj\.(.*)$": r"proj_in.\1",
r"^adaln_single\.(.*)$": r"time_embed.\1",
r"^audio_patchify_proj\.(.*)$": r"audio_proj_in.\1",
r"^audio_adaln_single\.(.*)$": r"audio_time_embed.\1",
# FeedForward
r"(.*)ff\.proj_in\.(.*)$": r"\1ff.net.0.proj.\2",
r"(.*)ff\.proj_out\.(.*)$": r"\1ff.net.2.\2",
# Attention Norms
r"(.*)\.q_norm\.(.*)$": r"\1.norm_q.\2",
r"(.*)\.k_norm\.(.*)$": r"\1.norm_k.\2",
# Scale Shift Tables (Global)
r"^av_ca_video_scale_shift_adaln_single\.(.*)$": r"av_cross_attn_video_scale_shift.\1",
r"^av_ca_audio_scale_shift_adaln_single\.(.*)$": r"av_cross_attn_audio_scale_shift.\1",
r"^av_ca_a2v_gate_adaln_single\.(.*)$": r"av_cross_attn_video_a2v_gate.\1",
r"^av_ca_v2a_gate_adaln_single\.(.*)$": r"av_cross_attn_audio_v2a_gate.\1",
# Scale Shift Tables (Block Level)
# SGLang: video_a2v_cross_attn_scale_shift_table -> HF: scale_shift_table_a2v_ca_video
r"(.*)video_a2v_cross_attn_scale_shift_table": r"\1scale_shift_table_a2v_ca_video",
r"(.*)audio_a2v_cross_attn_scale_shift_table": r"\1scale_shift_table_a2v_ca_audio",
}
)
lora_param_names_mapping: dict = field(
default_factory=lambda: {
# LoRA parameter name mappings from official repo format to HF format.
# This is applied before param_names_mapping when loading LoRA adapters.
# Will be populated if LoRA adapters use different naming conventions.
}
)
# Model type and attention configuration
model_type: LTXModelType = LTXModelType.AudioVideo
attention_type: LTX2AttentionFunction = LTX2AttentionFunction.DEFAULT
rope_type: LTX2RopeType = LTX2RopeType.INTERLEAVED
double_precision_rope: bool = False
# Video parameters
num_attention_heads: int = 32
attention_head_dim: int = 128
in_channels: int = 128
out_channels: int = 128
num_layers: int = 48
cross_attention_dim: int = 4096
norm_eps: float = 1e-6
caption_channels: int = 3840
positional_embedding_theta: float = 10000.0
positional_embedding_max_pos: list[int] | None = None
timestep_scale_multiplier: int = 1000
use_middle_indices_grid: bool = True
# Audio parameters
audio_num_attention_heads: int = 32
audio_attention_head_dim: int = 64
audio_in_channels: int = 128
audio_out_channels: int = 128
audio_cross_attention_dim: int = 2048
audio_positional_embedding_max_pos: list[int] | None = None
av_ca_timestep_scale_multiplier: int = 1
# SGLang-specific parameters
patch_size: tuple[int, int, int] = (1, 2, 2)
text_len: int = 512
def __post_init__(self):
super().__post_init__()
# Video derived values
self.hidden_size = self.num_attention_heads * self.attention_head_dim
self.num_channels_latents = self.out_channels
if self.positional_embedding_max_pos is None:
self.positional_embedding_max_pos = [20, 2048, 2048]
# Audio derived values
self.audio_hidden_size = (
self.audio_num_attention_heads * self.audio_attention_head_dim
)
if self.audio_positional_embedding_max_pos is None:
self.audio_positional_embedding_max_pos = [20]
@dataclass
class LTX2Config(DiTConfig):
"""Configuration for LTX-2 Video Transformer."""
arch_config: LTX2ArchConfig = field(default_factory=LTX2ArchConfig)
prefix: str = "ltx2"