[diffusion] model: support LTX2.3 (#22111)

docs/diffusion/compatibility_matrix.md

@@ -33,12 +33,15 @@ default parameters when initializing and generating videos.
 | TurboWan2.1 T2V 14B          | `IPostYellow/TurboWan2.1-T2V-14B-Diffusers`       | 480p                |    ✅     |         ❌         |     ❌     |              ❌               |              ✅               |              ✅               |    ⭕     |
 | TurboWan2.1 T2V 14B 720P     | `IPostYellow/TurboWan2.1-T2V-14B-720P-Diffusers`  | 720p                |    ✅     |         ❌         |     ❌     |              ❌               |              ✅               |              ✅               |    ⭕     |
 | TurboWan2.2 I2V A14B         | `IPostYellow/TurboWan2.2-I2V-A14B-Diffusers`      | 720p                |    ✅     |         ❌         |     ❌     |              ❌               |              ✅               |              ✅               |    ⭕     |
-| LTX-2                        | `Lightricks/LTX-2`                                | 1536×1024           |    ❌     |         ❌         |     ❌     |              ❌               |              ❌               |              ❌               |    ❌     |
+| LTX-2                        | `Lightricks/LTX-2`                                | 768×512<br>1536×1024 |    ❌     |         ❌         |     ❌     |              ❌               |              ❌               |              ❌               |    ❌     |
+| LTX-2.3                      | `Lightricks/LTX-2.3`                              | 768×512<br>1536×1024 |    ❌     |         ❌         |     ❌     |              ❌               |              ❌               |              ❌               |    ❌     |
 
 **Note**:
 1. Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
 2. SageSLA is based on SpargeAttn. Install it first with `pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation`
 3. LTX-2 two-stage generation uses `--pipeline-class-name LTX2TwoStagePipeline`. The spatial upsampler and distilled LoRA are auto-resolved from the model snapshot by default, and can still be overridden with `--spatial-upsampler-path` and `--distilled-lora-path`.
+4. `Lightricks/LTX-2.3` is supported through the bundled native overlay materialization path. One-stage generation uses the default `LTX2Pipeline`; two-stage generation uses `--pipeline-class-name LTX2TwoStagePipeline`.
+5. For LTX models, the `Resolutions` column uses output video `width×height` semantics, matching `sglang generate --width ... --height ...`. One-stage generation is validated at `768×512`; two-stage generation is validated at `1536×1024`.
 
 ### Image Generation Models
 

python/sglang/multimodal_gen/README.md

@@ -76,11 +76,6 @@ sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
     --save-output
 ```
 
-For LTX-2 two-stage generation, use `--pipeline-class-name LTX2TwoStagePipeline`. The
-spatial upsampler and distilled LoRA are auto-resolved from the same model snapshot by
-default, and can still be overridden with `--spatial-upsampler-path` and
-`--distilled-lora-path` when needed.
-
 ### LoRA support
 
 Apply LoRA adapters via `--lora-path`:

python/sglang/multimodal_gen/configs/models/adapter/ltx_2_connector.py

@@ -12,13 +12,17 @@ class LTX2ConnectorArchConfig(AdapterArchConfig):
     audio_connector_num_attention_heads: int = 30
     audio_connector_num_layers: int = 2
     audio_connector_num_learnable_registers: int = 128
+    audio_feature_extractor_out_features: int = 0
     caption_channels: int = 3840
     causal_temporal_positioning: bool = False
     connector_rope_base_seq_len: int = 4096
+    connector_apply_gated_attention: bool = False
+    feature_extractor_in_features: int = 0
     rope_double_precision: bool = True
     rope_theta: float = 10000.0
     rope_type: str = "split"
     text_proj_in_factor: int = 49
+    video_feature_extractor_out_features: int = 0
     video_connector_attention_head_dim: int = 128
     video_connector_num_attention_heads: int = 30
     video_connector_num_layers: int = 2

python/sglang/multimodal_gen/configs/models/dits/ltx_2.py

@@ -63,6 +63,7 @@ class LTX2ArchConfig(DiTArchConfig):
             # We use upstream variable names (patchify_proj, adaln_single) but HF uses different keys.
             #
             # HF key -> SGLang key (upstream naming)
+            r"^model\.diffusion_model\.(.*)$": r"\1",
             r"^proj_in\.(.*)$": r"patchify_proj.\1",
             r"^time_embed\.(.*)$": r"adaln_single.\1",
             r"^audio_proj_in\.(.*)$": r"audio_patchify_proj.\1",
@@ -123,6 +124,10 @@ class LTX2ArchConfig(DiTArchConfig):
     attention_type: LTX2AttentionFunction = LTX2AttentionFunction.DEFAULT
     rope_type: LTX2RopeType = LTX2RopeType.INTERLEAVED
     double_precision_rope: bool = False
+    quantize_video_rope_coords_to_hidden_dtype: bool = False
+    apply_gated_attention: bool = False
+    cross_attention_adaln: bool = False
+    caption_proj_before_connector: bool = False
 
     # Video parameters
     num_attention_heads: int = 32
@@ -147,6 +152,14 @@ class LTX2ArchConfig(DiTArchConfig):
     audio_positional_embedding_max_pos: list[int] | None = None
     av_ca_timestep_scale_multiplier: int = 1
 
+    # 2.3 connector-related fields may show up in transformer/config.json.
+    connector_attention_head_dim: int = 128
+    connector_num_attention_heads: int = 30
+    connector_num_layers: int = 2
+    audio_connector_attention_head_dim: int = 128
+    audio_connector_num_attention_heads: int = 30
+    audio_connector_num_layers: int = 2
+
     # SGLang-specific parameters
     patch_size: tuple[int, int, int] = (1, 2, 2)
     text_len: int = 512

python/sglang/multimodal_gen/configs/models/vaes/ltx_video.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import List
+from typing import Any, List
 
 from sglang.multimodal_gen.configs.models.vaes.base import VAEArchConfig, VAEConfig
 
@@ -52,6 +52,12 @@ class LTXVideoVAEArchConfig(VAEArchConfig):
     decoder_causal: bool = False
     decoder_spatial_padding_mode: str = "reflect"
 
+    # Native LTX variant metadata.
+    ltx_variant: str = "ltx_2"
+    condition_encoder_subdir: str = ""
+    video_decoder_variant: str = "ltx_2"
+    video_decoder_config: dict[str, Any] = field(default_factory=dict)
+
 
 @dataclass
 class LTXVideoVAEConfig(VAEConfig):

python/sglang/multimodal_gen/configs/pipeline_configs/ltx_2.py

@@ -93,20 +93,48 @@ def pack_text_embeds(
     return normalized_hidden_states
 
 
+def pack_text_embeds_v2(
+    text_hidden_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+    eps: float = 1e-6,
+) -> torch.Tensor:
+    """
+    LTX-2.3 feature extractor pre-processing.
+
+    Upstream `FeatureExtractorV2` applies per-token RMS normalization on each
+    Gemma layer and then flattens `[hidden_dim, num_layers]` into the channel
+    dimension, zeroing out padded positions afterwards.
+    """
+
+    variance = torch.mean(text_hidden_states**2, dim=2, keepdim=True)
+    normalized_hidden_states = text_hidden_states * torch.rsqrt(variance + eps)
+    normalized_hidden_states = normalized_hidden_states.flatten(2)
+    mask = attention_mask.bool().unsqueeze(-1)
+    return torch.where(
+        mask, normalized_hidden_states, torch.zeros_like(normalized_hidden_states)
+    )
+
+
+def is_ltx23_native_variant(arch_config: object) -> bool:
+    return str(getattr(arch_config, "ltx_variant", "ltx_2")) == "ltx_2_3"
+
+
 def _gemma_postprocess_func(
     outputs: BaseEncoderOutput,
     text_inputs: dict,
     pipeline_config: Optional["LTX2PipelineConfig"] = None,
 ) -> torch.Tensor:
-    _ = pipeline_config
     # LTX-2 requires all hidden states concatenated for the connector
     if hasattr(outputs, "hidden_states") and outputs.hidden_states is not None:
-        # outputs.hidden_states is a tuple of tensors
-        # We need to stack them along the last dimension and pack them
         hidden_states = torch.stack(outputs.hidden_states, dim=-1)
         attention_mask = text_inputs["attention_mask"]
+        if (
+            pipeline_config is not None
+            and pipeline_config.dit_config.arch_config.caption_proj_before_connector
+        ):
+            return pack_text_embeds_v2(hidden_states, attention_mask)
+
         sequence_lengths = attention_mask.sum(dim=-1)
-        # Assuming left padding for Gemma as per Diffusers
         return pack_text_embeds(hidden_states, sequence_lengths, padding_side="left")
     else:
         raise AttributeError(

python/sglang/multimodal_gen/configs/sample/ltx_2.py

@@ -1,4 +1,6 @@
 import dataclasses
+from dataclasses import field
+from typing import Any
 
 from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
 
@@ -39,3 +41,44 @@ class LTX2SamplingParams(SamplingParams):
         "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
         "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
     )
+
+
+@dataclasses.dataclass
+class LTX23SamplingParams(LTX2SamplingParams):
+    """Sampling parameters matching official LTX-2.3 one-stage defaults."""
+
+    generator_device: str = "cuda"
+    guidance_scale: float = 3.0
+    num_inference_steps: int = 30
+
+    video_cfg_scale: float = 3.0
+    video_stg_scale: float = 1.0
+    video_rescale_scale: float = 0.7
+    video_modality_scale: float = 3.0
+    video_skip_step: int = 0
+    video_stg_blocks: list[int] = field(default_factory=lambda: [28])
+
+    audio_cfg_scale: float = 7.0
+    audio_stg_scale: float = 1.0
+    audio_rescale_scale: float = 0.7
+    audio_modality_scale: float = 3.0
+    audio_skip_step: int = 0
+    audio_stg_blocks: list[int] = field(default_factory=lambda: [28])
+
+    def build_request_extra(self) -> dict[str, Any]:
+        extra = super().build_request_extra()
+        extra["ltx2_stage1_guider_params"] = {
+            "video_cfg_scale": self.video_cfg_scale,
+            "video_stg_scale": self.video_stg_scale,
+            "video_rescale_scale": self.video_rescale_scale,
+            "video_modality_scale": self.video_modality_scale,
+            "video_skip_step": self.video_skip_step,
+            "video_stg_blocks": self.video_stg_blocks,
+            "audio_cfg_scale": self.audio_cfg_scale,
+            "audio_stg_scale": self.audio_stg_scale,
+            "audio_rescale_scale": self.audio_rescale_scale,
+            "audio_modality_scale": self.audio_modality_scale,
+            "audio_skip_step": self.audio_skip_step,
+            "audio_stg_blocks": self.audio_stg_blocks,
+        }
+        return extra

python/sglang/multimodal_gen/configs/sample/sampling_params.py

@@ -253,6 +253,18 @@ class SamplingParams:
         if env_steps is not None and self.num_inference_steps is not None:
             self.num_inference_steps = int(env_steps)
 
+    def build_request_extra(self) -> dict[str, Any]:
+        """Return optional request-scoped extras for downstream pipeline stages."""
+        extra = {}
+        diffusers_kwargs = getattr(self, "diffusers_kwargs", None)
+        if diffusers_kwargs:
+            extra["diffusers_kwargs"] = diffusers_kwargs
+        return extra
+
+    def apply_request_extra(self, req: Any) -> None:
+        """Merge request extras (model specific, e.g., LTX2.3) into an already-created pipeline request."""
+        req.extra.update(self.build_request_extra())
+
     def _adjust_output_quality(self, output_quality: str, data_type: DataType) -> int:
         """Convert output_quality string to compression level."""
         output_quality_mapper = {"maximum": 100, "high": 90, "medium": 55, "low": 35}

python/sglang/multimodal_gen/model_overlays/ltx_2_3/_overlay/materialize.py

@@ -0,0 +1,302 @@
+import json
+import os
+
+from huggingface_hub import snapshot_download
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+from sglang.multimodal_gen.runtime.utils.model_overlay import (
+    _copytree_link_or_copy,
+    _ensure_dir,
+    _link_or_copy_file,
+)
+
+AUXILIARY_MODEL_ID = "Lightricks/LTX-2"
+CONFIG_DONOR_MODEL_ID = "FastVideo/LTX-2.3-Distilled-Diffusers"
+
+AUXILIARY_PATTERNS = [
+    "audio_vae/**",
+    "scheduler/**",
+    "text_encoder/**",
+    "tokenizer/**",
+    "vae/config.json",
+    "vae/diffusion_pytorch_model.safetensors",
+]
+
+CONFIG_DONOR_PATTERNS = [
+    "transformer/config.json",
+    "text_encoder/config.json",
+    "vae/**",
+    "vocoder/**",
+]
+
+MONOLITH_PREFIX = "model.diffusion_model."
+VIDEO_CONNECTOR_PREFIX = f"{MONOLITH_PREFIX}video_embeddings_connector."
+AUDIO_CONNECTOR_PREFIX = f"{MONOLITH_PREFIX}audio_embeddings_connector."
+TEXT_PROJ_IN_PREFIX = f"{MONOLITH_PREFIX}text_proj_in."
+VIDEO_AGGREGATE_PREFIX = "text_embedding_projection.video_aggregate_embed."
+AUDIO_AGGREGATE_PREFIX = "text_embedding_projection.audio_aggregate_embed."
+
+
+def _load_json(path: str) -> dict:
+    with open(path) as f:
+        return json.load(f)
+
+
+def _write_json(path: str, payload: dict) -> None:
+    with open(path, "w") as f:
+        json.dump(payload, f, indent=2)
+        f.write("\n")
+
+
+def _rename_connector_key(key: str) -> str | None:
+    if key.startswith(VIDEO_CONNECTOR_PREFIX):
+        suffix = key[len(VIDEO_CONNECTOR_PREFIX) :]
+        suffix = suffix.replace("transformer_1d_blocks", "transformer_blocks")
+        suffix = suffix.replace(".attn1.q_norm.", ".attn1.norm_q.")
+        suffix = suffix.replace(".attn1.k_norm.", ".attn1.norm_k.")
+        return f"video_connector.{suffix}"
+    if key.startswith(AUDIO_CONNECTOR_PREFIX):
+        suffix = key[len(AUDIO_CONNECTOR_PREFIX) :]
+        suffix = suffix.replace("transformer_1d_blocks", "transformer_blocks")
+        suffix = suffix.replace(".attn1.q_norm.", ".attn1.norm_q.")
+        suffix = suffix.replace(".attn1.k_norm.", ".attn1.norm_k.")
+        return f"audio_connector.{suffix}"
+    if key.startswith(TEXT_PROJ_IN_PREFIX):
+        return key[len(MONOLITH_PREFIX) :]
+    if key.startswith(VIDEO_AGGREGATE_PREFIX):
+        return f"video_aggregate_embed.{key[len(VIDEO_AGGREGATE_PREFIX):]}"
+    if key.startswith(AUDIO_AGGREGATE_PREFIX):
+        return f"audio_aggregate_embed.{key[len(AUDIO_AGGREGATE_PREFIX):]}"
+    return None
+
+
+def _repack_transformer_weights(source_path: str, output_path: str) -> None:
+    tensors = {}
+    with safe_open(source_path, framework="pt") as f:
+        for key in f.keys():
+            if not key.startswith(MONOLITH_PREFIX):
+                continue
+            if key.startswith(VIDEO_CONNECTOR_PREFIX):
+                continue
+            if key.startswith(AUDIO_CONNECTOR_PREFIX):
+                continue
+            if key.startswith(TEXT_PROJ_IN_PREFIX):
+                continue
+            tensors[key[len(MONOLITH_PREFIX) :]] = f.get_tensor(key)
+    if not tensors:
+        raise ValueError("No transformer tensors found in LTX-2.3 source checkpoint.")
+    save_file(tensors, output_path)
+
+
+def _repack_connectors_weights(source_path: str, output_path: str) -> None:
+    tensors = {}
+    with safe_open(source_path, framework="pt") as f:
+        for key in f.keys():
+            renamed = _rename_connector_key(key)
+            if renamed is None:
+                continue
+            tensors[renamed] = f.get_tensor(key)
+    if not tensors:
+        raise ValueError("No connector tensors found in LTX-2.3 source checkpoint.")
+    save_file(tensors, output_path)
+
+
+def _build_transformer_config(config_donor_dir: str) -> dict:
+    config = _load_json(os.path.join(config_donor_dir, "transformer", "config.json"))
+    config["_class_name"] = "LTX2VideoTransformer3DModel"
+    config["force_sdpa_v2a_cross_attention"] = True
+    config["quantize_video_rope_coords_to_hidden_dtype"] = True
+    return config
+
+
+def _build_connectors_config(config_donor_dir: str) -> dict:
+    text_encoder_config = _load_json(
+        os.path.join(config_donor_dir, "text_encoder", "config.json")
+    )
+    return {
+        "_class_name": "LTX2TextConnectors",
+        "_diffusers_version": "0.37.0.dev0",
+        "audio_connector_attention_head_dim": text_encoder_config[
+            "audio_connector_attention_head_dim"
+        ],
+        "audio_connector_num_attention_heads": text_encoder_config[
+            "audio_connector_num_attention_heads"
+        ],
+        "audio_connector_num_layers": text_encoder_config["audio_connector_num_layers"],
+        "audio_connector_num_learnable_registers": text_encoder_config[
+            "connector_num_learnable_registers"
+        ],
+        "audio_feature_extractor_out_features": text_encoder_config[
+            "audio_feature_extractor_out_features"
+        ],
+        "caption_channels": text_encoder_config["hidden_size"],
+        "causal_temporal_positioning": False,
+        "connector_apply_gated_attention": text_encoder_config[
+            "connector_apply_gated_attention"
+        ],
+        "feature_extractor_in_features": text_encoder_config[
+            "feature_extractor_in_features"
+        ],
+        "connector_rope_base_seq_len": text_encoder_config[
+            "connector_positional_embedding_max_pos"
+        ][0],
+        "rope_double_precision": text_encoder_config["connector_double_precision_rope"],
+        "rope_theta": text_encoder_config["connector_positional_embedding_theta"],
+        "rope_type": text_encoder_config["connector_rope_type"],
+        "text_proj_in_factor": text_encoder_config["feature_extractor_in_features"]
+        // text_encoder_config["hidden_size"],
+        "video_feature_extractor_out_features": text_encoder_config[
+            "video_feature_extractor_out_features"
+        ],
+        "video_connector_attention_head_dim": text_encoder_config[
+            "connector_attention_head_dim"
+        ],
+        "video_connector_num_attention_heads": text_encoder_config[
+            "connector_num_attention_heads"
+        ],
+        "video_connector_num_layers": text_encoder_config["connector_num_layers"],
+        "video_connector_num_learnable_registers": text_encoder_config[
+            "connector_num_learnable_registers"
+        ],
+    }
+
+
+def _build_vae_config(auxiliary_dir: str, config_donor_dir: str) -> dict:
+    config = _load_json(os.path.join(auxiliary_dir, "vae", "config.json"))
+    config["ltx_variant"] = "ltx_2_3"
+    config["condition_encoder_subdir"] = "ltx23_image_encoder"
+    config["video_decoder_variant"] = "ltx_2_3"
+    config["video_decoder_config"] = _load_json(
+        os.path.join(config_donor_dir, "vae", "config.json")
+    )["vae"]
+    return config
+
+
+def _repack_ltx23_image_encoder_weights(source_path: str, output_path: str) -> None:
+    tensors = {}
+    with safe_open(source_path, framework="pt") as f:
+        for key in f.keys():
+            if key.startswith("encoder."):
+                tensors[key[len("encoder.") :]] = f.get_tensor(key)
+                continue
+            if key.startswith("per_channel_statistics."):
+                tensors[key] = f.get_tensor(key)
+    if not tensors:
+        raise ValueError("No LTX-2.3 image-encoder tensors found in donor checkpoint.")
+    save_file(tensors, output_path)
+
+
+def _repack_ltx23_video_decoder_weights(
+    auxiliary_encoder_path: str,
+    donor_decoder_path: str,
+    output_path: str,
+) -> None:
+    tensors = {}
+    with safe_open(auxiliary_encoder_path, framework="pt") as f:
+        for key in f.keys():
+            if key.startswith("encoder."):
+                tensors[key] = f.get_tensor(key)
+    with safe_open(donor_decoder_path, framework="pt") as f:
+        for key in f.keys():
+            if key.startswith("decoder."):
+                tensors[key] = f.get_tensor(key)
+                continue
+            if key == "per_channel_statistics.mean-of-means":
+                tensor = f.get_tensor(key)
+                tensors["decoder.per_channel_statistics.mean_of_means"] = tensor
+                tensors["latents_mean"] = tensor.clone()
+                continue
+            if key == "per_channel_statistics.std-of-means":
+                tensor = f.get_tensor(key)
+                tensors["decoder.per_channel_statistics.std_of_means"] = tensor
+                tensors["latents_std"] = tensor.clone()
+                continue
+    if not tensors:
+        raise ValueError("No LTX-2.3 decoder tensors found in donor checkpoint.")
+    save_file(tensors, output_path)
+
+
+def materialize(
+    *,
+    overlay_dir: str,
+    source_dir: str,
+    output_dir: str,
+    manifest: dict,
+) -> None:
+    _ = overlay_dir, manifest
+
+    auxiliary_dir = snapshot_download(
+        repo_id=AUXILIARY_MODEL_ID,
+        allow_patterns=AUXILIARY_PATTERNS,
+        max_workers=8,
+    )
+    config_donor_dir = snapshot_download(
+        repo_id=CONFIG_DONOR_MODEL_ID,
+        allow_patterns=CONFIG_DONOR_PATTERNS,
+        max_workers=8,
+    )
+
+    for component_name in ("audio_vae", "scheduler", "text_encoder", "tokenizer"):
+        _copytree_link_or_copy(
+            os.path.join(auxiliary_dir, component_name),
+            os.path.join(output_dir, component_name),
+        )
+    _copytree_link_or_copy(
+        os.path.join(config_donor_dir, "vocoder"),
+        os.path.join(output_dir, "vocoder"),
+    )
+
+    source_checkpoint = os.path.join(source_dir, "ltx-2.3-22b-dev.safetensors")
+
+    transformer_dir = os.path.join(output_dir, "transformer")
+    _ensure_dir(transformer_dir)
+    _write_json(
+        os.path.join(transformer_dir, "config.json"),
+        _build_transformer_config(config_donor_dir),
+    )
+    _repack_transformer_weights(
+        source_checkpoint, os.path.join(transformer_dir, "model.safetensors")
+    )
+
+    connectors_dir = os.path.join(output_dir, "connectors")
+    _ensure_dir(connectors_dir)
+    _write_json(
+        os.path.join(connectors_dir, "config.json"),
+        _build_connectors_config(config_donor_dir),
+    )
+    _repack_connectors_weights(
+        source_checkpoint, os.path.join(connectors_dir, "model.safetensors")
+    )
+
+    vae_dir = os.path.join(output_dir, "vae")
+    _ensure_dir(vae_dir)
+    _write_json(
+        os.path.join(vae_dir, "config.json"),
+        _build_vae_config(auxiliary_dir, config_donor_dir),
+    )
+    _repack_ltx23_video_decoder_weights(
+        os.path.join(auxiliary_dir, "vae", "diffusion_pytorch_model.safetensors"),
+        os.path.join(config_donor_dir, "vae", "model.safetensors"),
+        os.path.join(vae_dir, "model.safetensors"),
+    )
+
+    image_encoder_dir = os.path.join(vae_dir, "ltx23_image_encoder")
+    _ensure_dir(image_encoder_dir)
+    _link_or_copy_file(
+        os.path.join(config_donor_dir, "vae", "config.json"),
+        os.path.join(image_encoder_dir, "config.json"),
+    )
+    _repack_ltx23_image_encoder_weights(
+        os.path.join(config_donor_dir, "vae", "model.safetensors"),
+        os.path.join(image_encoder_dir, "model.safetensors"),
+    )
+
+    _link_or_copy_file(
+        os.path.join(source_dir, "ltx-2.3-22b-distilled-lora-384.safetensors"),
+        os.path.join(output_dir, "ltx-2.3-22b-distilled-lora-384.safetensors"),
+    )
+    _link_or_copy_file(
+        os.path.join(source_dir, "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"),
+        os.path.join(output_dir, "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"),
+    )

python/sglang/multimodal_gen/registry.py

@@ -90,7 +90,10 @@ from sglang.multimodal_gen.configs.sample.hunyuan import (
     HunyuanSamplingParams,
 )
 from sglang.multimodal_gen.configs.sample.hunyuan3d import Hunyuan3DSamplingParams
-from sglang.multimodal_gen.configs.sample.ltx_2 import LTX2SamplingParams
+from sglang.multimodal_gen.configs.sample.ltx_2 import (
+    LTX2SamplingParams,
+    LTX23SamplingParams,
+)
 from sglang.multimodal_gen.configs.sample.mova import (
     MOVA_360P_SamplingParams,
     MOVA_720P_SamplingParams,
@@ -155,7 +158,18 @@ def _discover_and_register_pipelines():
         package.__path__, package.__name__ + "."
     ):
         if not ispkg:
-            pipeline_module = importlib.import_module(module_name)
+            try:
+                pipeline_module = importlib.import_module(module_name)
+            except Exception as exc:
+                logger.warning(
+                    "Skipping pipeline module %s during discovery due to import failure: %s",
+                    module_name,
+                    exc,
+                )
+                logger.debug(
+                    "Pipeline import failure details for %s", module_name, exc_info=True
+                )
+                continue
             if hasattr(pipeline_module, "EntryClass"):
                 entry_cls = pipeline_module.EntryClass
                 entry_cls_list = (
@@ -594,12 +608,18 @@ def _register_configs():
     register_configs(
         sampling_param_cls=LTX2SamplingParams,
         pipeline_config_cls=LTX2PipelineConfig,
-        hf_model_paths=[
-            "Lightricks/LTX-2",
-        ],
+        hf_model_paths=["Lightricks/LTX-2"],
         model_detectors=[
             lambda path: "ltx" in path.lower() and "video" in path.lower(),
-            lambda path: "ltx-2" in path.lower(),
+            lambda path: "ltx-2" in path.lower() and "ltx-2.3" not in path.lower(),
+        ],
+    )
+    register_configs(
+        sampling_param_cls=LTX23SamplingParams,
+        pipeline_config_cls=LTX2PipelineConfig,
+        hf_model_paths=["Lightricks/LTX-2.3"],
+        model_detectors=[
+            lambda path: "ltx-2.3" in path.lower(),
         ],
     )
 

python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py

@@ -552,13 +552,15 @@ class DiffGenerator:
         self.shutdown()
 
     def __del__(self):
-        if self.owns_scheduler_client:
+        owns_scheduler_client = bool(getattr(self, "owns_scheduler_client", False))
+        local_scheduler_process = getattr(self, "local_scheduler_process", None)
+        if owns_scheduler_client:
             logger.warning(
                 "Generator was garbage collected without being shut down. "
                 "Attempting to shut down the local server and client."
             )
             self.shutdown()
-        elif self.local_scheduler_process:
+        elif local_scheduler_process:
             logger.warning(
                 "Generator was garbage collected without being shut down. "
                 "Attempting to shut down the local server."

python/sglang/multimodal_gen/runtime/entrypoints/utils.py

@@ -288,12 +288,7 @@ def prepare_request(
         sampling_params=sampling_params,
         VSA_sparsity=server_args.attention_backend_config.VSA_sparsity,
     )
-    try:
-        diffusers_kwargs = sampling_params.diffusers_kwargs
-    except AttributeError:
-        diffusers_kwargs = None
-    if diffusers_kwargs:
-        req.extra["diffusers_kwargs"] = diffusers_kwargs
+    sampling_params.apply_request_extra(req)
 
     req.adjust_size(server_args)
 

python/sglang/multimodal_gen/runtime/loader/component_loaders/adapter_loader.py

@@ -1,5 +1,8 @@
 from safetensors.torch import load_file as safetensors_load_file
 
+from sglang.multimodal_gen.configs.models.adapter.ltx_2_connector import (
+    LTX2ConnectorConfig,
+)
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
 from sglang.multimodal_gen.runtime.loader.component_loaders.component_loader import (
     ComponentLoader,
@@ -50,10 +53,9 @@ class AdapterLoader(ComponentLoader):
         target_device = get_local_torch_device()
         default_dtype = PRECISION_TO_TYPE[server_args.pipeline_config.dit_precision]
 
-        from types import SimpleNamespace
-
         with set_default_torch_dtype(default_dtype), skip_init_modules():
-            connector_cfg = SimpleNamespace(**config)
+            connector_cfg = LTX2ConnectorConfig()
+            connector_cfg.update_model_arch(config)
             model = model_cls(connector_cfg).to(
                 device=target_device, dtype=default_dtype
             )

python/sglang/multimodal_gen/runtime/models/adapter/ltx_2_connector.py

@@ -1,3 +1,4 @@
+import math
 from typing import Optional, Tuple, Union
 
 import torch
@@ -89,6 +90,7 @@ class LTX2Attention(torch.nn.Module):
         norm_eps: float = 1e-6,
         norm_elementwise_affine: bool = True,
         rope_type: str = "interleaved",
+        apply_gated_attention: bool = False,
         processor=None,
     ):
         super().__init__()
@@ -125,6 +127,9 @@ class LTX2Attention(torch.nn.Module):
         self.to_v = torch.nn.Linear(
             self.cross_attention_dim, self.inner_kv_dim, bias=bias
         )
+        self.to_gate_logits = None
+        if apply_gated_attention:
+            self.to_gate_logits = torch.nn.Linear(query_dim, heads, bias=True)
         self.to_out = torch.nn.ModuleList([])
         self.to_out.append(torch.nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
         self.to_out.append(torch.nn.Dropout(dropout))
@@ -153,6 +158,7 @@ class LTX2Attention(torch.nn.Module):
         query_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         key_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ) -> torch.Tensor:
+        gate_input = hidden_states
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
 
@@ -199,6 +205,15 @@ class LTX2Attention(torch.nn.Module):
         hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
         hidden_states = hidden_states.to(query.dtype)
 
+        if self.to_gate_logits is not None:
+            gate_logits = self.to_gate_logits(gate_input)
+            b, t, _ = hidden_states.shape
+            hidden_states = hidden_states.view(b, t, self.heads, self.head_dim)
+            hidden_states = hidden_states * (
+                2.0 * torch.sigmoid(gate_logits).unsqueeze(-1)
+            )
+            hidden_states = hidden_states.view(b, t, self.heads * self.head_dim)
+
         hidden_states = self.to_out[0](hidden_states)
         hidden_states = self.to_out[1](hidden_states)
         return hidden_states
@@ -317,6 +332,7 @@ class LTX2TransformerBlock1d(nn.Module):
         activation_fn: str = "gelu-approximate",
         eps: float = 1e-6,
         rope_type: str = "interleaved",
+        apply_gated_attention: bool = False,
     ):
         super().__init__()
 
@@ -327,6 +343,7 @@ class LTX2TransformerBlock1d(nn.Module):
             kv_heads=num_attention_heads,
             dim_head=attention_head_dim,
             rope_type=rope_type,
+            apply_gated_attention=apply_gated_attention,
         )
 
         self.norm2 = torch.nn.RMSNorm(dim, eps=eps, elementwise_affine=False)
@@ -373,6 +390,7 @@ class LTX2ConnectorTransformer1d(nn.Module):
         eps: float = 1e-6,
         causal_temporal_positioning: bool = False,
         rope_type: str = "interleaved",
+        apply_gated_attention: bool = False,
     ):
         super().__init__()
         self.num_attention_heads = num_attention_heads
@@ -403,6 +421,7 @@ class LTX2ConnectorTransformer1d(nn.Module):
                     num_attention_heads=num_attention_heads,
                     attention_head_dim=attention_head_dim,
                     rope_type=rope_type,
+                    apply_gated_attention=apply_gated_attention,
                 )
                 for _ in range(num_layers)
             ]
@@ -516,10 +535,37 @@ class LTX2TextConnectors(nn.Module):
         rope_double_precision = config.rope_double_precision
         causal_temporal_positioning = config.causal_temporal_positioning
         rope_type = config.rope_type
-
-        self.text_proj_in = nn.Linear(
-            caption_channels * text_proj_in_factor, caption_channels, bias=False
+        connector_apply_gated_attention = config.connector_apply_gated_attention
+        feature_extractor_in_features = config.feature_extractor_in_features
+        video_feature_extractor_out_features = (
+            config.video_feature_extractor_out_features
         )
+        audio_feature_extractor_out_features = (
+            config.audio_feature_extractor_out_features
+        )
+
+        self.text_proj_in: nn.Linear | None = None
+        self.video_aggregate_embed: nn.Linear | None = None
+        self.audio_aggregate_embed: nn.Linear | None = None
+        if (
+            feature_extractor_in_features > 0
+            and video_feature_extractor_out_features > 0
+            and audio_feature_extractor_out_features > 0
+        ):
+            self.video_aggregate_embed = nn.Linear(
+                feature_extractor_in_features,
+                video_feature_extractor_out_features,
+                bias=True,
+            )
+            self.audio_aggregate_embed = nn.Linear(
+                feature_extractor_in_features,
+                audio_feature_extractor_out_features,
+                bias=True,
+            )
+        else:
+            self.text_proj_in = nn.Linear(
+                caption_channels * text_proj_in_factor, caption_channels, bias=False
+            )
         self.video_connector = LTX2ConnectorTransformer1d(
             num_attention_heads=video_connector_num_attention_heads,
             attention_head_dim=video_connector_attention_head_dim,
@@ -530,6 +576,7 @@ class LTX2TextConnectors(nn.Module):
             rope_double_precision=rope_double_precision,
             causal_temporal_positioning=causal_temporal_positioning,
             rope_type=rope_type,
+            apply_gated_attention=connector_apply_gated_attention,
         )
         self.audio_connector = LTX2ConnectorTransformer1d(
             num_attention_heads=audio_connector_num_attention_heads,
@@ -541,8 +588,15 @@ class LTX2TextConnectors(nn.Module):
             rope_double_precision=rope_double_precision,
             causal_temporal_positioning=causal_temporal_positioning,
             rope_type=rope_type,
+            apply_gated_attention=connector_apply_gated_attention,
         )
 
+    @staticmethod
+    def _rescale_v2_features(
+        x: torch.Tensor, target_dim: int, source_dim: int
+    ) -> torch.Tensor:
+        return x * math.sqrt(target_dim / source_dim)
+
     def forward(
         self,
         text_encoder_hidden_states: torch.Tensor,
@@ -557,12 +611,6 @@ class LTX2TextConnectors(nn.Module):
             )
             attention_mask = attention_mask.to(text_dtype) * torch.finfo(text_dtype).max
 
-        # Ensure input dtype matches the layer's weight dtype
-        if text_encoder_hidden_states.dtype != self.text_proj_in.weight.dtype:
-            text_encoder_hidden_states = text_encoder_hidden_states.to(
-                self.text_proj_in.weight.dtype
-            )
-
         # Ensure sequence length is divisible by num_learnable_registers (128)
         seq_len = text_encoder_hidden_states.shape[1]
         num_learnable_registers = self.video_connector.num_learnable_registers
@@ -579,10 +627,44 @@ class LTX2TextConnectors(nn.Module):
                 # Pad with a large negative value to mask out the new tokens
                 attention_mask = F.pad(attention_mask, (0, pad_len), value=-1000000.0)
 
-        text_encoder_hidden_states = self.text_proj_in(text_encoder_hidden_states)
+        if (
+            self.video_aggregate_embed is not None
+            and self.audio_aggregate_embed is not None
+        ):
+            video_hidden_states = text_encoder_hidden_states
+            audio_hidden_states = text_encoder_hidden_states
+            if video_hidden_states.dtype != self.video_aggregate_embed.weight.dtype:
+                video_hidden_states = video_hidden_states.to(
+                    self.video_aggregate_embed.weight.dtype
+                )
+            if audio_hidden_states.dtype != self.audio_aggregate_embed.weight.dtype:
+                audio_hidden_states = audio_hidden_states.to(
+                    self.audio_aggregate_embed.weight.dtype
+                )
+            source_dim = self.video_aggregate_embed.out_features
+            video_hidden_states = self._rescale_v2_features(
+                video_hidden_states,
+                self.video_aggregate_embed.out_features,
+                source_dim,
+            )
+            audio_hidden_states = self._rescale_v2_features(
+                audio_hidden_states,
+                self.audio_aggregate_embed.out_features,
+                source_dim,
+            )
+            video_hidden_states = self.video_aggregate_embed(video_hidden_states)
+            audio_hidden_states = self.audio_aggregate_embed(audio_hidden_states)
+        else:
+            assert self.text_proj_in is not None
+            if text_encoder_hidden_states.dtype != self.text_proj_in.weight.dtype:
+                text_encoder_hidden_states = text_encoder_hidden_states.to(
+                    self.text_proj_in.weight.dtype
+                )
+            video_hidden_states = self.text_proj_in(text_encoder_hidden_states)
+            audio_hidden_states = video_hidden_states
 
         video_text_embedding, new_attn_mask = self.video_connector(
-            text_encoder_hidden_states, attention_mask
+            video_hidden_states, attention_mask
         )
 
         attn_mask = (new_attn_mask < 1e-6).to(torch.int64)
@@ -593,7 +675,7 @@ class LTX2TextConnectors(nn.Module):
         new_attn_mask = attn_mask.squeeze(-1)
 
         audio_text_embedding, _ = self.audio_connector(
-            text_encoder_hidden_states, attention_mask
+            audio_hidden_states, attention_mask
         )
 
         return video_text_embedding, audio_text_embedding, new_attn_mask

python/sglang/multimodal_gen/runtime/models/dits/ltx_2.py

@@ -37,6 +37,15 @@ from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)
 
+ADALN_NUM_BASE_PARAMS = 6
+ADALN_NUM_CROSS_ATTN_PARAMS = 3
+
+
+def adaln_embedding_coefficient(cross_attention_adaln: bool) -> int:
+    return ADALN_NUM_BASE_PARAMS + (
+        ADALN_NUM_CROSS_ATTN_PARAMS if cross_attention_adaln else 0
+    )
+
 
 def apply_interleaved_rotary_emb(
     x: torch.Tensor, freqs: Tuple[torch.Tensor, torch.Tensor]
@@ -447,6 +456,7 @@ class LTX2Attention(nn.Module):
         norm_eps: float = 1e-6,
         qk_norm: bool = True,
         use_local_attention: bool = False,
+        apply_gated_attention: bool = False,
         supported_attention_backends: set[AttentionBackendEnum] | None = None,
         prefix: str = "",
         quant_config: QuantizationConfig | None = None,
@@ -461,6 +471,8 @@ class LTX2Attention(nn.Module):
         self.norm_eps = float(norm_eps)
         self.qk_norm = bool(qk_norm)
         self.use_local_attention = bool(use_local_attention)
+        self.apply_gated_attention = bool(apply_gated_attention)
+        self.prefix = prefix
 
         tp_size = get_tp_world_size()
         if tp_size <= 0:
@@ -499,6 +511,15 @@ class LTX2Attention(nn.Module):
             gather_output=False,
             quant_config=quant_config,
         )
+        self.to_gate_logits: ColumnParallelLinear | None = None
+        if self.apply_gated_attention:
+            self.to_gate_logits = ColumnParallelLinear(
+                self.query_dim,
+                self.heads,
+                bias=True,
+                gather_output=False,
+                quant_config=quant_config,
+            )
 
         self.q_norm: nn.Module | None = None
         self.k_norm: nn.Module | None = None
@@ -561,6 +582,7 @@ class LTX2Attention(nn.Module):
         perturbation_mask: torch.Tensor | None = None,
         all_perturbed: bool = False,
     ) -> torch.Tensor:
+        gate_input = x
         context_ = x if context is None else context
         v, _ = self.to_v(context_)
         use_attention = not all_perturbed
@@ -609,9 +631,17 @@ class LTX2Attention(nn.Module):
         if not use_attention:
             out = v
 
-        out = out.flatten(2)
-        out, _ = self.to_out[0](out)
-        return out
+        if self.to_gate_logits is not None:
+            gate_logits, _ = self.to_gate_logits(gate_input)
+            b, t = out.shape[:2]
+            out = out.view(b, t, self.local_heads, self.dim_head)
+            out = out * (2.0 * torch.sigmoid(gate_logits).unsqueeze(-1))
+            out = out.view(b, t, self.local_heads * self.dim_head)
+
+        out_flat = out.flatten(2)
+        out_proj, _ = self.to_out[0](out_flat)
+
+        return out_proj
 
     def _slice_rope_for_tp(
         self,
@@ -688,6 +718,10 @@ class LTX2TransformerBlock(nn.Module):
         audio_cross_attention_dim: int,
         qk_norm: bool = True,
         norm_eps: float = 1e-6,
+        apply_gated_attention: bool = False,
+        cross_attention_adaln: bool = False,
+        use_local_av_cross_attention: bool = False,
+        force_sdpa_v2a_cross_attention: bool = False,
         supported_attention_backends: set[AttentionBackendEnum] | None = None,
         prefix: str = "",
         quant_config: QuantizationConfig | None = None,
@@ -695,6 +729,9 @@ class LTX2TransformerBlock(nn.Module):
         super().__init__()
         self.idx = idx
         self.norm_eps = norm_eps
+        # LTX2.3
+        self.cross_attention_adaln = cross_attention_adaln
+        self.use_local_av_cross_attention = use_local_av_cross_attention
 
         # 1. Self-Attention (video and audio)
         self.attn1 = LTX2Attention(
@@ -703,6 +740,7 @@ class LTX2TransformerBlock(nn.Module):
             dim_head=attention_head_dim,
             norm_eps=norm_eps,
             qk_norm=qk_norm,
+            apply_gated_attention=apply_gated_attention,
             supported_attention_backends=supported_attention_backends,
             prefix=f"{prefix}.attn1",
             quant_config=quant_config,
@@ -713,6 +751,7 @@ class LTX2TransformerBlock(nn.Module):
             dim_head=audio_attention_head_dim,
             norm_eps=norm_eps,
             qk_norm=qk_norm,
+            apply_gated_attention=apply_gated_attention,
             supported_attention_backends=supported_attention_backends,
             prefix=f"{prefix}.audio_attn1",
             quant_config=quant_config,
@@ -729,6 +768,7 @@ class LTX2TransformerBlock(nn.Module):
             norm_eps=norm_eps,
             qk_norm=qk_norm,
             use_local_attention=True,
+            apply_gated_attention=apply_gated_attention,
             supported_attention_backends=supported_attention_backends,
             prefix=f"{prefix}.attn2",
             quant_config=quant_config,
@@ -741,6 +781,7 @@ class LTX2TransformerBlock(nn.Module):
             norm_eps=norm_eps,
             qk_norm=qk_norm,
             use_local_attention=True,
+            apply_gated_attention=apply_gated_attention,
             supported_attention_backends=supported_attention_backends,
             prefix=f"{prefix}.audio_attn2",
             quant_config=quant_config,
@@ -754,6 +795,8 @@ class LTX2TransformerBlock(nn.Module):
             dim_head=audio_attention_head_dim,
             norm_eps=norm_eps,
             qk_norm=qk_norm,
+            use_local_attention=use_local_av_cross_attention,
+            apply_gated_attention=apply_gated_attention,
             supported_attention_backends=supported_attention_backends,
             prefix=f"{prefix}.audio_to_video_attn",
             quant_config=quant_config,
@@ -765,7 +808,13 @@ class LTX2TransformerBlock(nn.Module):
             dim_head=audio_attention_head_dim,
             norm_eps=norm_eps,
             qk_norm=qk_norm,
-            supported_attention_backends=supported_attention_backends,
+            use_local_attention=use_local_av_cross_attention,
+            apply_gated_attention=apply_gated_attention,
+            supported_attention_backends=(
+                {AttentionBackendEnum.TORCH_SDPA}
+                if force_sdpa_v2a_cross_attention
+                else supported_attention_backends
+            ),
             prefix=f"{prefix}.video_to_audio_attn",
             quant_config=quant_config,
         )
@@ -777,14 +826,23 @@ class LTX2TransformerBlock(nn.Module):
         )
 
         # 5. Modulation Parameters
-        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
+        num_ada_params = adaln_embedding_coefficient(cross_attention_adaln)
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(num_ada_params, dim) / dim**0.5
+        )
         self.audio_scale_shift_table = nn.Parameter(
-            torch.randn(6, audio_dim) / audio_dim**0.5
+            torch.randn(num_ada_params, audio_dim) / audio_dim**0.5
         )
         self.video_a2v_cross_attn_scale_shift_table = nn.Parameter(torch.randn(5, dim))
         self.audio_a2v_cross_attn_scale_shift_table = nn.Parameter(
             torch.randn(5, audio_dim)
         )
+        if self.cross_attention_adaln:
+            # LTX2.3
+            self.prompt_scale_shift_table = nn.Parameter(torch.randn(2, dim))
+            self.audio_prompt_scale_shift_table = nn.Parameter(
+                torch.randn(2, audio_dim)
+            )
 
     def get_ada_values(
         self,
@@ -813,6 +871,8 @@ class LTX2TransformerBlock(nn.Module):
         audio_encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         temb_audio: torch.Tensor,
+        temb_prompt: torch.Tensor | None,
+        temb_audio_prompt: torch.Tensor | None,
         temb_ca_scale_shift: torch.Tensor,
         temb_ca_audio_scale_shift: torch.Tensor,
         temb_ca_gate: torch.Tensor,
@@ -860,21 +920,70 @@ class LTX2TransformerBlock(nn.Module):
         )
         audio_hidden_states = audio_hidden_states + attn_audio_hidden_states * agate_msa
         # 2. Prompt Cross-Attention
-        norm_hidden_states = rms_norm(hidden_states, self.norm_eps)
-        attn_hidden_states = self.attn2(
-            norm_hidden_states,
-            context=encoder_hidden_states,
-            mask=encoder_attention_mask,
-        )
-        hidden_states = hidden_states + attn_hidden_states
+        if self.cross_attention_adaln:
+            # LTX2.3
+            if temb_prompt is None or temb_audio_prompt is None:
+                raise ValueError(
+                    "cross_attention_adaln requires prompt modulation tensors."
+                )
+            vshift_q, vscale_q, vgate_q = self.get_ada_values(
+                self.scale_shift_table, batch_size, temb, slice(6, 9)
+            )
+            v_prompt_shift, v_prompt_scale = self.get_ada_values(
+                self.prompt_scale_shift_table, batch_size, temb_prompt, slice(None)
+            )
+            norm_hidden_states = (
+                rms_norm(hidden_states, self.norm_eps) * (1 + vscale_q) + vshift_q
+            )
+            mod_encoder_hidden_states = (
+                encoder_hidden_states * (1 + v_prompt_scale) + v_prompt_shift
+            )
+            attn_hidden_states = self.attn2(
+                norm_hidden_states,
+                context=mod_encoder_hidden_states,
+                mask=encoder_attention_mask,
+            )
+            hidden_states = hidden_states + attn_hidden_states * vgate_q
 
-        norm_audio_hidden_states = rms_norm(audio_hidden_states, self.norm_eps)
-        attn_audio_hidden_states = self.audio_attn2(
-            norm_audio_hidden_states,
-            context=audio_encoder_hidden_states,
-            mask=audio_encoder_attention_mask,
-        )
-        audio_hidden_states = audio_hidden_states + attn_audio_hidden_states
+            ashift_q, ascale_q, agate_q = self.get_ada_values(
+                self.audio_scale_shift_table, batch_size, temb_audio, slice(6, 9)
+            )
+            a_prompt_shift, a_prompt_scale = self.get_ada_values(
+                self.audio_prompt_scale_shift_table,
+                batch_size,
+                temb_audio_prompt,
+                slice(None),
+            )
+            norm_audio_hidden_states = (
+                rms_norm(audio_hidden_states, self.norm_eps) * (1 + ascale_q) + ashift_q
+            )
+            mod_audio_encoder_hidden_states = (
+                audio_encoder_hidden_states * (1 + a_prompt_scale) + a_prompt_shift
+            )
+            attn_audio_hidden_states = self.audio_attn2(
+                norm_audio_hidden_states,
+                context=mod_audio_encoder_hidden_states,
+                mask=audio_encoder_attention_mask,
+            )
+            audio_hidden_states = (
+                audio_hidden_states + attn_audio_hidden_states * agate_q
+            )
+        else:
+            norm_hidden_states = rms_norm(hidden_states, self.norm_eps)
+            attn_hidden_states = self.attn2(
+                norm_hidden_states,
+                context=encoder_hidden_states,
+                mask=encoder_attention_mask,
+            )
+            hidden_states = hidden_states + attn_hidden_states
+
+            norm_audio_hidden_states = rms_norm(audio_hidden_states, self.norm_eps)
+            attn_audio_hidden_states = self.audio_attn2(
+                norm_audio_hidden_states,
+                context=audio_encoder_hidden_states,
+                mask=audio_encoder_attention_mask,
+            )
+            audio_hidden_states = audio_hidden_states + attn_audio_hidden_states
         # 3. Audio-to-Video and Video-to-Audio Cross-Attention
         norm_hidden_states = rms_norm(hidden_states, self.norm_eps)
         norm_audio_hidden_states = rms_norm(audio_hidden_states, self.norm_eps)
@@ -976,7 +1085,7 @@ class LTX2TransformerBlock(nn.Module):
             )
         # 4. Feedforward
         vshift_mlp, vscale_mlp, vgate_mlp = self.get_ada_values(
-            self.scale_shift_table, batch_size, temb, slice(3, None)
+            self.scale_shift_table, batch_size, temb, slice(3, 6)
         )
         norm_hidden_states = (
             rms_norm(hidden_states, self.norm_eps) * (1 + vscale_mlp) + vshift_mlp
@@ -985,7 +1094,7 @@ class LTX2TransformerBlock(nn.Module):
         hidden_states = hidden_states + ff_output * vgate_mlp
 
         ashift_mlp, ascale_mlp, agate_mlp = self.get_ada_values(
-            self.audio_scale_shift_table, batch_size, temb_audio, slice(3, None)
+            self.audio_scale_shift_table, batch_size, temb_audio, slice(3, 6)
         )
         norm_audio_hidden_states = (
             rms_norm(audio_hidden_states, self.norm_eps) * (1 + ascale_mlp) + ashift_mlp
@@ -1003,6 +1112,12 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
     reverse_param_names_mapping = LTX2ArchConfig().reverse_param_names_mapping
     lora_param_names_mapping = LTX2ArchConfig().lora_param_names_mapping
 
+    @staticmethod
+    def _collapse_prompt_timestep(timestep: torch.Tensor) -> torch.Tensor:
+        if timestep.ndim <= 1:
+            return timestep
+        return timestep.amax(dim=tuple(range(1, timestep.ndim)))
+
     def _validate_tp_config(self, *, arch: LTX2ArchConfig, tp_size: int) -> None:
         """Validate TP-related dimension constraints (fail-fast)."""
         if tp_size < 1:
@@ -1089,20 +1204,38 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
         )
 
         # 2. Prompt embeddings
-        self.caption_projection = LTX2TextProjection(
-            in_features=arch.caption_channels, hidden_size=self.hidden_size
-        )
-        self.audio_caption_projection = LTX2TextProjection(
-            in_features=arch.caption_channels, hidden_size=self.audio_hidden_size
-        )
+        self.caption_projection: LTX2TextProjection | None = None
+        self.audio_caption_projection: LTX2TextProjection | None = None
+        if not arch.caption_proj_before_connector:
+            self.caption_projection = LTX2TextProjection(
+                in_features=arch.caption_channels, hidden_size=self.hidden_size
+            )
+            self.audio_caption_projection = LTX2TextProjection(
+                in_features=arch.caption_channels, hidden_size=self.audio_hidden_size
+            )
 
         # 3. Timestep Modulation Params and Embedding
         self.adaln_single = LTX2AdaLayerNormSingle(
-            self.hidden_size, embedding_coefficient=6
+            self.hidden_size,
+            embedding_coefficient=adaln_embedding_coefficient(
+                arch.cross_attention_adaln
+            ),
         )
         self.audio_adaln_single = LTX2AdaLayerNormSingle(
-            self.audio_hidden_size, embedding_coefficient=6
+            self.audio_hidden_size,
+            embedding_coefficient=adaln_embedding_coefficient(
+                arch.cross_attention_adaln
+            ),
         )
+        self.prompt_adaln_single: LTX2AdaLayerNormSingle | None = None
+        self.audio_prompt_adaln_single: LTX2AdaLayerNormSingle | None = None
+        if arch.cross_attention_adaln:
+            self.prompt_adaln_single = LTX2AdaLayerNormSingle(
+                self.hidden_size, embedding_coefficient=2
+            )
+            self.audio_prompt_adaln_single = LTX2AdaLayerNormSingle(
+                self.audio_hidden_size, embedding_coefficient=2
+            )
 
         # Global Cross Attention Modulation Parameters
         self.av_ca_video_scale_shift_adaln_single = LTX2AdaLayerNormSingle(
@@ -1141,6 +1274,9 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
         rope_double_precision = bool(
             hf_config.get("rope_double_precision", arch.double_precision_rope)
         )
+        self.quantize_video_rope_coords_to_hidden_dtype = bool(
+            hf_config.get("quantize_video_rope_coords_to_hidden_dtype", False)
+        )
         causal_offset = int(hf_config.get("causal_offset", 1))
 
         pos_embed_max_pos = int(arch.positional_embedding_max_pos[0])
@@ -1231,6 +1367,14 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
                     audio_cross_attention_dim=arch.audio_cross_attention_dim,
                     norm_eps=self.norm_eps,
                     qk_norm=True,  # Always True in LTX2
+                    apply_gated_attention=arch.apply_gated_attention,
+                    cross_attention_adaln=arch.cross_attention_adaln,
+                    use_local_av_cross_attention=bool(
+                        getattr(arch, "use_local_av_cross_attention", False)
+                    ),
+                    force_sdpa_v2a_cross_attention=bool(
+                        getattr(arch, "force_sdpa_v2a_cross_attention", False)
+                    ),
                     supported_attention_backends=self._supported_attention_backends,
                     prefix=config.prefix,
                     quant_config=quant_config,
@@ -1336,6 +1480,14 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
                 device=audio_hidden_states.device,
             )
 
+        if self.quantize_video_rope_coords_to_hidden_dtype:
+            video_coords = video_coords.to(
+                device=hidden_states.device, dtype=hidden_states.dtype
+            )
+        else:
+            video_coords = video_coords.to(device=hidden_states.device)
+        audio_coords = audio_coords.to(device=audio_hidden_states.device)
+
         video_rotary_emb = self.rope(video_coords, device=hidden_states.device)
         audio_rotary_emb = self.audio_rope(
             audio_coords, device=audio_hidden_states.device
@@ -1367,12 +1519,25 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
         audio_embedded_timestep = audio_embedded_timestep.view(
             batch_size, -1, audio_embedded_timestep.size(-1)
         )
+        temb_prompt = None
+        temb_audio_prompt = None
+        if self.prompt_adaln_single is not None:
+            prompt_timestep = self._collapse_prompt_timestep(timestep)
+            temb_prompt, _ = self.prompt_adaln_single(
+                prompt_timestep.flatten(), hidden_dtype=hidden_states.dtype
+            )
+            temb_prompt = temb_prompt.view(batch_size, -1, temb_prompt.size(-1))
+        if self.audio_prompt_adaln_single is not None:
+            audio_prompt_timestep = self._collapse_prompt_timestep(audio_timestep)
+            temb_audio_prompt, _ = self.audio_prompt_adaln_single(
+                audio_prompt_timestep.flatten(),
+                hidden_dtype=audio_hidden_states.dtype,
+            )
+            temb_audio_prompt = temb_audio_prompt.view(
+                batch_size, -1, temb_audio_prompt.size(-1)
+            )
 
         # 3.2. Prepare global modality cross attention modulation parameters
-        ts_ca_mult = (
-            self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier
-        )
-
         hidden_dtype = hidden_states.dtype
         temb_ca_scale_shift, _ = self.av_ca_video_scale_shift_adaln_single(
             timestep.flatten(), hidden_dtype=hidden_dtype
@@ -1403,10 +1568,12 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
         )
 
         # 4. Prepare prompt embeddings
-        encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-        audio_encoder_hidden_states = self.audio_caption_projection(
-            audio_encoder_hidden_states
-        )
+        if self.caption_projection is not None:
+            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
+        if self.audio_caption_projection is not None:
+            audio_encoder_hidden_states = self.audio_caption_projection(
+                audio_encoder_hidden_states
+            )
         # 5. Run blocks
         skip_video_self_attn_blocks = set(skip_video_self_attn_blocks or ())
         skip_audio_self_attn_blocks = set(skip_audio_self_attn_blocks or ())
@@ -1421,6 +1588,8 @@ class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
                 # under ForwardPattern.Pattern_0.
                 temb=temb,
                 temb_audio=temb_audio,
+                temb_prompt=temb_prompt,
+                temb_audio_prompt=temb_audio_prompt,
                 temb_ca_scale_shift=temb_ca_scale_shift,
                 temb_ca_audio_scale_shift=temb_ca_audio_scale_shift,
                 temb_ca_gate=temb_ca_gate,

python/sglang/multimodal_gen/runtime/models/vaes/ltx_2_3_condition_encoder.py

@@ -0,0 +1,204 @@
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from sglang.multimodal_gen.runtime.models.vaes.ltx_2_vae import (
+    LTX2VideoCausalConv3d,
+    LTX2VideoResnetBlock3d,
+    LTXVideoDownsampler3d,
+)
+
+
+def _patchify_video(sample: torch.Tensor, patch_size: int) -> torch.Tensor:
+    if patch_size == 1:
+        return sample
+    batch_size, channels, num_frames, height, width = sample.shape
+    sample = sample.reshape(
+        batch_size,
+        channels,
+        num_frames,
+        1,
+        height // patch_size,
+        patch_size,
+        width // patch_size,
+        patch_size,
+    )
+    return sample.permute(0, 1, 3, 7, 5, 2, 4, 6).flatten(1, 4)
+
+
+class LTX23VideoPixelNorm(nn.Module):
+    def __init__(self, dim: int = 1, eps: float = 1e-8) -> None:
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        mean_sq = torch.mean(x**2, dim=self.dim, keepdim=True)
+        rms = torch.sqrt(mean_sq + self.eps)
+        return x / rms
+
+
+class LTX23PerChannelStatistics(nn.Module):
+    def __init__(self, latent_channels: int) -> None:
+        super().__init__()
+        self.register_buffer("std-of-means", torch.empty(latent_channels))
+        self.register_buffer("mean-of-means", torch.empty(latent_channels))
+
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
+        mean = self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
+        std = self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
+        return (x - mean) / std
+
+
+class LTX23VideoResBlockStack(nn.Module):
+    def __init__(
+        self, channels: int, num_layers: int, spatial_padding_mode: str
+    ) -> None:
+        super().__init__()
+        self.res_blocks = nn.ModuleList(
+            [
+                LTX2VideoResnetBlock3d(
+                    in_channels=channels,
+                    out_channels=channels,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for res_block in self.res_blocks:
+            hidden_states = res_block(hidden_states, causal=True)
+        return hidden_states
+
+
+def _make_ltx23_encoder_block(
+    block_name: str,
+    block_config: dict[str, Any],
+    in_channels: int,
+    spatial_padding_mode: str,
+) -> tuple[nn.Module, int]:
+    if block_name == "res_x":
+        return (
+            LTX23VideoResBlockStack(
+                channels=in_channels,
+                num_layers=int(block_config["num_layers"]),
+                spatial_padding_mode=spatial_padding_mode,
+            ),
+            in_channels,
+        )
+
+    multiplier = int(block_config.get("multiplier", 2))
+    stride_map = {
+        "compress_space_res": (1, 2, 2),
+        "compress_time_res": (2, 1, 1),
+        "compress_all_res": (2, 2, 2),
+    }
+    stride = stride_map.get(block_name)
+    if stride is None:
+        raise ValueError(f"Unsupported LTX-2.3 encoder block: {block_name}")
+    out_channels = in_channels * multiplier
+    return (
+        LTXVideoDownsampler3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            stride=stride,
+            spatial_padding_mode=spatial_padding_mode,
+        ),
+        out_channels,
+    )
+
+
+class LTX23VideoConditionEncoder(nn.Module):
+    def __init__(self, config: dict[str, Any]) -> None:
+        super().__init__()
+
+        vae_config = config.get("vae", config)
+        latent_channels = int(vae_config["latent_channels"])
+        patch_size = int(vae_config.get("patch_size", 4))
+        spatial_padding_mode = str(vae_config.get("spatial_padding_mode", "zeros"))
+        encoder_blocks = list(vae_config["encoder_blocks"])
+        latent_log_var = str(vae_config.get("latent_log_var", "uniform"))
+
+        self.patch_size = patch_size
+        self.latency_channels = latent_channels
+        self.latent_log_var = latent_log_var
+        self.per_channel_statistics = LTX23PerChannelStatistics(latent_channels)
+
+        feature_channels = latent_channels
+        self.conv_in = LTX2VideoCausalConv3d(
+            in_channels=int(vae_config.get("in_channels", 3)) * patch_size**2,
+            out_channels=feature_channels,
+            kernel_size=3,
+            stride=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+        self.down_blocks = nn.ModuleList()
+        for block_name, block_params in encoder_blocks:
+            block_config = (
+                {"num_layers": block_params}
+                if isinstance(block_params, int)
+                else dict(block_params)
+            )
+            block, feature_channels = _make_ltx23_encoder_block(
+                block_name=block_name,
+                block_config=block_config,
+                in_channels=feature_channels,
+                spatial_padding_mode=spatial_padding_mode,
+            )
+            self.down_blocks.append(block)
+
+        self.conv_norm_out = LTX23VideoPixelNorm(dim=1, eps=1e-8)
+        self.conv_act = nn.SiLU()
+
+        conv_out_channels = latent_channels
+        if latent_log_var == "per_channel":
+            conv_out_channels *= 2
+        elif latent_log_var in {"uniform", "constant"}:
+            conv_out_channels += 1
+        elif latent_log_var != "none":
+            raise ValueError(f"Unsupported latent_log_var: {latent_log_var}")
+
+        self.conv_out = LTX2VideoCausalConv3d(
+            in_channels=feature_channels,
+            out_channels=conv_out_channels,
+            kernel_size=3,
+            stride=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        frames_count = int(sample.shape[2])
+        if (frames_count - 1) % 8 != 0:
+            frames_to_crop = (frames_count - 1) % 8
+            sample = sample[:, :, :-frames_to_crop, ...]
+
+        hidden_states = _patchify_video(sample, self.patch_size)
+        hidden_states = self.conv_in(hidden_states, causal=True)
+
+        for block in self.down_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states, causal=True)
+
+        if self.latent_log_var == "uniform":
+            means = hidden_states[:, :-1, ...]
+            logvar = hidden_states[:, -1:, ...]
+            hidden_states = torch.cat(
+                [
+                    means,
+                    logvar.repeat(1, means.shape[1], *([1] * (means.ndim - 2))),
+                ],
+                dim=1,
+            )
+        elif self.latent_log_var == "constant":
+            means = hidden_states[:, :-1, ...]
+            logvar = torch.full_like(means, -30.0)
+            hidden_states = torch.cat([means, logvar], dim=1)
+
+        means, _ = torch.chunk(hidden_states, 2, dim=1)
+        return self.per_channel_statistics.normalize(means)

python/sglang/multimodal_gen/runtime/models/vaes/ltx_2_vae.py

@@ -394,6 +394,18 @@ class LTXVideoUpsampler3d(nn.Module):
         return hidden_states
 
 
+class LTX23PerChannelStatistics(nn.Module):
+    def __init__(self, latent_channels: int) -> None:
+        super().__init__()
+        self.register_buffer("mean_of_means", torch.empty(latent_channels))
+        self.register_buffer("std_of_means", torch.empty(latent_channels))
+
+    def un_normalize(self, x: torch.Tensor) -> torch.Tensor:
+        mean = self.mean_of_means.view(1, -1, 1, 1, 1).to(x)
+        std = self.std_of_means.view(1, -1, 1, 1, 1).to(x)
+        return x * std + mean
+
+
 # Like LTX 1.0 LTXVideo095DownBlock3D, but with the updated LTX2VideoResnetBlock3d
 class LTX2VideoDownBlock3D(nn.Module):
     r"""
@@ -609,6 +621,64 @@ class LTX2VideoMidBlock3d(nn.Module):
         return hidden_states
 
 
+class LTX23VideoMidBlock3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        inject_noise: bool = False,
+        timestep_conditioning: bool = False,
+        spatial_padding_mode: str = "zeros",
+    ) -> None:
+        super().__init__()
+
+        self.time_embedder = None
+        if timestep_conditioning:
+            self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(
+                in_channels * 4, 0
+            )
+
+        self.res_blocks = nn.ModuleList(
+            [
+                LTX2VideoResnetBlock3d(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    dropout=dropout,
+                    eps=resnet_eps,
+                    non_linearity=resnet_act_fn,
+                    inject_noise=inject_noise,
+                    timestep_conditioning=timestep_conditioning,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        causal: bool = True,
+    ) -> torch.Tensor:
+        if self.time_embedder is not None:
+            temb = self.time_embedder(
+                timestep=temb.flatten(),
+                resolution=None,
+                aspect_ratio=None,
+                batch_size=hidden_states.size(0),
+                hidden_dtype=hidden_states.dtype,
+            )
+            temb = temb.view(hidden_states.size(0), -1, 1, 1, 1)
+
+        for res_block in self.res_blocks:
+            hidden_states = res_block(hidden_states, temb, causal=causal)
+
+        return hidden_states
+
+
 # Like LTXVideoUpBlock3d but with no conv_in and the updated LTX2VideoResnetBlock3d
 class LTX2VideoUpBlock3d(nn.Module):
     r"""
@@ -1104,6 +1174,192 @@ class LTX2VideoDecoder3d(nn.Module):
         return hidden_states
 
 
+def _make_ltx23_decoder_block(
+    block_name: str,
+    block_config: dict,
+    in_channels: int,
+    resnet_norm_eps: float,
+    timestep_conditioning: bool,
+    spatial_padding_mode: str,
+) -> tuple[nn.Module, int]:
+    out_channels = in_channels
+    if block_name == "res_x":
+        block = LTX23VideoMidBlock3d(
+            in_channels=in_channels,
+            num_layers=int(block_config["num_layers"]),
+            resnet_eps=resnet_norm_eps,
+            timestep_conditioning=timestep_conditioning,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+    elif block_name == "res_x_y":
+        out_channels = in_channels // int(block_config.get("multiplier", 2))
+        block = LTX2VideoResnetBlock3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            eps=resnet_norm_eps,
+            timestep_conditioning=False,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+    elif block_name == "compress_time":
+        out_channels = in_channels // int(block_config.get("multiplier", 1))
+        block = LTXVideoUpsampler3d(
+            in_channels=in_channels,
+            stride=(2, 1, 1),
+            residual=False,
+            upscale_factor=int(block_config.get("multiplier", 1)),
+            spatial_padding_mode=spatial_padding_mode,
+        )
+    elif block_name == "compress_space":
+        out_channels = in_channels // int(block_config.get("multiplier", 1))
+        block = LTXVideoUpsampler3d(
+            in_channels=in_channels,
+            stride=(1, 2, 2),
+            residual=False,
+            upscale_factor=int(block_config.get("multiplier", 1)),
+            spatial_padding_mode=spatial_padding_mode,
+        )
+    elif block_name == "compress_all":
+        out_channels = in_channels // int(block_config.get("multiplier", 1))
+        block = LTXVideoUpsampler3d(
+            in_channels=in_channels,
+            stride=(2, 2, 2),
+            residual=bool(block_config.get("residual", False)),
+            upscale_factor=int(block_config.get("multiplier", 1)),
+            spatial_padding_mode=spatial_padding_mode,
+        )
+    else:
+        raise ValueError(f"Unsupported LTX-2.3 decoder block: {block_name}")
+
+    return block, out_channels
+
+
+class LTX23VideoDecoder3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 128,
+        out_channels: int = 3,
+        decoder_blocks: tuple[tuple[str, dict], ...] = (),
+        patch_size: int = 4,
+        patch_size_t: int = 1,
+        resnet_norm_eps: float = 1e-6,
+        is_causal: bool = False,
+        timestep_conditioning: bool = False,
+        base_channels: int = 128,
+        spatial_padding_mode: str = "zeros",
+    ) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.out_channels = out_channels * patch_size**2
+        self.is_causal = is_causal
+        self.per_channel_statistics = LTX23PerChannelStatistics(in_channels)
+
+        feature_channels = base_channels * 8
+        self.conv_in = LTX2VideoCausalConv3d(
+            in_channels=in_channels,
+            out_channels=feature_channels,
+            kernel_size=3,
+            stride=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+        self.up_blocks = nn.ModuleList([])
+        for block_name, block_params in reversed(tuple(decoder_blocks)):
+            block_config = (
+                {"num_layers": block_params}
+                if isinstance(block_params, int)
+                else dict(block_params)
+            )
+            block, feature_channels = _make_ltx23_decoder_block(
+                block_name=block_name,
+                block_config=block_config,
+                in_channels=feature_channels,
+                resnet_norm_eps=resnet_norm_eps,
+                timestep_conditioning=timestep_conditioning,
+                spatial_padding_mode=spatial_padding_mode,
+            )
+            self.up_blocks.append(block)
+
+        self.norm_out = PerChannelRMSNorm()
+        self.conv_act = nn.SiLU()
+        self.conv_out = LTX2VideoCausalConv3d(
+            in_channels=feature_channels,
+            out_channels=self.out_channels,
+            kernel_size=3,
+            stride=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+        self.time_embedder = None
+        self.scale_shift_table = None
+        self.timestep_scale_multiplier = None
+        if timestep_conditioning:
+            self.timestep_scale_multiplier = nn.Parameter(
+                torch.tensor(1000.0, dtype=torch.float32)
+            )
+            self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(
+                feature_channels * 2, 0
+            )
+            self.scale_shift_table = nn.Parameter(
+                torch.randn(2, feature_channels) / feature_channels**0.5
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        causal: Optional[bool] = None,
+    ) -> torch.Tensor:
+        causal = self.is_causal if causal is None else causal
+
+        hidden_states = self.per_channel_statistics.un_normalize(hidden_states)
+        hidden_states = self.conv_in(hidden_states, causal=causal)
+
+        if self.timestep_scale_multiplier is not None and temb is not None:
+            temb = temb * self.timestep_scale_multiplier
+
+        for up_block in self.up_blocks:
+            if isinstance(up_block, LTX23VideoMidBlock3d):
+                hidden_states = up_block(hidden_states, temb, causal=causal)
+            elif isinstance(up_block, LTX2VideoResnetBlock3d):
+                hidden_states = up_block(hidden_states, None, causal=causal)
+            else:
+                hidden_states = up_block(hidden_states, causal=causal)
+
+        hidden_states = self.norm_out(hidden_states)
+
+        if self.time_embedder is not None and temb is not None:
+            temb = self.time_embedder(
+                timestep=temb.flatten(),
+                resolution=None,
+                aspect_ratio=None,
+                batch_size=hidden_states.size(0),
+                hidden_dtype=hidden_states.dtype,
+            )
+            temb = temb.view(hidden_states.size(0), -1, 1, 1, 1).unflatten(1, (2, -1))
+            temb = temb + self.scale_shift_table[None, ..., None, None, None]
+            shift, scale = temb.unbind(dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states, causal=causal)
+
+        p = self.patch_size
+        p_t = self.patch_size_t
+        batch_size, _, num_frames, height, width = hidden_states.shape
+        hidden_states = hidden_states.reshape(
+            batch_size, -1, p_t, p, p, num_frames, height, width
+        )
+        hidden_states = (
+            hidden_states.permute(0, 1, 5, 2, 6, 4, 7, 3)
+            .flatten(6, 7)
+            .flatten(4, 5)
+            .flatten(2, 3)
+        )
+        return hidden_states
+
+
 class AutoencoderKLLTX2Video(ParallelTiledVAE):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
@@ -1157,6 +1413,10 @@ class AutoencoderKLLTX2Video(ParallelTiledVAE):
         timestep_conditioning = getattr(
             config.arch_config, "timestep_conditioning", False
         )
+        use_ltx23_video_decoder = (
+            str(getattr(config.arch_config, "video_decoder_variant", "ltx_2"))
+            == "ltx_2_3"
+        )
         decoder_causal = config.arch_config.decoder_causal
         decoder_spatial_padding_mode = config.arch_config.decoder_spatial_padding_mode
 
@@ -1175,22 +1435,53 @@ class AutoencoderKLLTX2Video(ParallelTiledVAE):
             encoder_spatial_padding_mode,
         )
 
-        self.decoder = LTX2VideoDecoder3d(
-            in_channels=latent_channels,
-            out_channels=out_channels,
-            block_out_channels=decoder_block_out_channels,
-            spatio_temporal_scaling=decoder_spatio_temporal_scaling,
-            layers_per_block=decoder_layers_per_block,
-            patch_size=patch_size,
-            patch_size_t=patch_size_t,
-            resnet_norm_eps=resnet_norm_eps,
-            is_causal=decoder_causal,
-            inject_noise=decoder_inject_noise,
-            timestep_conditioning=timestep_conditioning,
-            upsample_residual=upsample_residual,
-            upsample_factor=upsample_factor,
-            spatial_padding_mode=decoder_spatial_padding_mode,
-        )
+        if use_ltx23_video_decoder:
+            video_decoder_config = dict(config.arch_config.video_decoder_config)
+            if not video_decoder_config:
+                raise ValueError(
+                    "LTX-2.3 native video decoder requires video_decoder_config."
+                )
+            self.decoder = LTX23VideoDecoder3d(
+                in_channels=latent_channels,
+                out_channels=out_channels,
+                decoder_blocks=tuple(video_decoder_config["decoder_blocks"]),
+                patch_size=int(video_decoder_config.get("patch_size", patch_size)),
+                patch_size_t=patch_size_t,
+                resnet_norm_eps=resnet_norm_eps,
+                is_causal=bool(
+                    video_decoder_config.get("causal_decoder", decoder_causal)
+                ),
+                timestep_conditioning=bool(
+                    video_decoder_config.get(
+                        "timestep_conditioning", timestep_conditioning
+                    )
+                ),
+                base_channels=int(
+                    video_decoder_config.get("decoder_base_channels", 128)
+                ),
+                spatial_padding_mode=str(
+                    video_decoder_config.get(
+                        "spatial_padding_mode", decoder_spatial_padding_mode
+                    )
+                ),
+            )
+        else:
+            self.decoder = LTX2VideoDecoder3d(
+                in_channels=latent_channels,
+                out_channels=out_channels,
+                block_out_channels=decoder_block_out_channels,
+                spatio_temporal_scaling=decoder_spatio_temporal_scaling,
+                layers_per_block=decoder_layers_per_block,
+                patch_size=patch_size,
+                patch_size_t=patch_size_t,
+                resnet_norm_eps=resnet_norm_eps,
+                is_causal=decoder_causal,
+                inject_noise=decoder_inject_noise,
+                timestep_conditioning=timestep_conditioning,
+                upsample_residual=upsample_residual,
+                upsample_factor=upsample_factor,
+                spatial_padding_mode=decoder_spatial_padding_mode,
+            )
 
         latents_mean = torch.zeros((latent_channels,), requires_grad=False)
         latents_std = torch.ones((latent_channels,), requires_grad=False)

python/sglang/multimodal_gen/runtime/models/vocoder/ltx_2_vocoder.py

@@ -1,13 +1,237 @@
 import math
 from abc import ABC
+from contextlib import nullcontext
 from typing import Tuple
 
+import einops
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from sglang.multimodal_gen.configs.models.vocoder.ltx_vocoder import LTXVocoderConfig
 
+LRELU_SLOPE = 0.1
+
+
+def get_padding(kernel_size: int, dilation: int = 1) -> int:
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def _sinc(x: torch.Tensor) -> torch.Tensor:
+    return torch.where(
+        x == 0,
+        torch.tensor(1.0, device=x.device, dtype=x.dtype),
+        torch.sin(math.pi * x) / math.pi / x,
+    )
+
+
+def kaiser_sinc_filter1d(
+    cutoff: float, half_width: float, kernel_size: int
+) -> torch.Tensor:
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+    delta_f = 4 * half_width
+    amplitude = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if amplitude > 50.0:
+        beta = 0.1102 * (amplitude - 8.7)
+    elif amplitude >= 21.0:
+        beta = 0.5842 * (amplitude - 21) ** 0.4 + 0.07886 * (amplitude - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+    time = (
+        torch.arange(-half_size, half_size) + 0.5
+        if even
+        else torch.arange(kernel_size) - half_size
+    )
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time)
+        filter_ /= filter_.sum()
+    return filter_.view(1, 1, kernel_size)
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(
+        self,
+        cutoff: float = 0.5,
+        half_width: float = 0.6,
+        stride: int = 1,
+        padding: bool = True,
+        padding_mode: str = "replicate",
+        kernel_size: int = 12,
+    ):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        self.register_buffer(
+            "filter", kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        _, channels, _ = x.shape
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        return F.conv1d(
+            x,
+            self.filter.expand(channels, -1, -1),
+            stride=self.stride,
+            groups=channels,
+        )
+
+
+class UpSample1d(nn.Module):
+    def __init__(
+        self,
+        ratio: int = 2,
+        kernel_size: int | None = None,
+        persistent: bool = True,
+        window_type: str = "kaiser",
+    ):
+        super().__init__()
+        self.ratio = ratio
+        self.stride = ratio
+
+        if window_type == "hann":
+            rolloff = 0.99
+            lowpass_filter_width = 6
+            width = math.ceil(lowpass_filter_width / rolloff)
+            self.kernel_size = 2 * width * ratio + 1
+            self.pad = width
+            self.pad_left = 2 * width * ratio
+            self.pad_right = self.kernel_size - ratio
+            time_axis = (torch.arange(self.kernel_size) / ratio - width) * rolloff
+            time_clamped = time_axis.clamp(-lowpass_filter_width, lowpass_filter_width)
+            window = torch.cos(time_clamped * math.pi / lowpass_filter_width / 2) ** 2
+            sinc_filter = (torch.sinc(time_axis) * window * rolloff / ratio).view(
+                1, 1, -1
+            )
+        else:
+            self.kernel_size = (
+                int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+            )
+            self.pad = self.kernel_size // ratio - 1
+            self.pad_left = (
+                self.pad * self.stride + (self.kernel_size - self.stride) // 2
+            )
+            self.pad_right = (
+                self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+            )
+            sinc_filter = kaiser_sinc_filter1d(
+                cutoff=0.5 / ratio,
+                half_width=0.6 / ratio,
+                kernel_size=self.kernel_size,
+            )
+
+        self.register_buffer("filter", sinc_filter, persistent=persistent)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        _, channels, _ = x.shape
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        filt = self.filter.to(dtype=x.dtype, device=x.device).expand(channels, -1, -1)
+        x = self.ratio * F.conv_transpose1d(
+            x, filt, stride=self.stride, groups=channels
+        )
+        return x[..., self.pad_left : -self.pad_right]
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio: int = 2, kernel_size: int | None = None):
+        super().__init__()
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio,
+            half_width=0.6 / ratio,
+            stride=ratio,
+            kernel_size=int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lowpass(x)
+
+
+class Activation1d(nn.Module):
+    def __init__(
+        self,
+        activation: nn.Module,
+        up_ratio: int = 2,
+        down_ratio: int = 2,
+        up_kernel_size: int = 12,
+        down_kernel_size: int = 12,
+    ):
+        super().__init__()
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.upsample(x)
+        x = self.act(x)
+        return self.downsample(x)
+
+
+class Snake(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        alpha: float = 1.0,
+        alpha_trainable: bool = True,
+        alpha_logscale: bool = True,
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        return x + (1.0 / (alpha + self.eps)) * torch.sin(x * alpha).pow(2)
+
+
+class SnakeBeta(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        alpha: float = 1.0,
+        alpha_trainable: bool = True,
+        alpha_logscale: bool = True,
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.beta = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.beta.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        return x + (1.0 / (beta + self.eps)) * torch.sin(x * alpha).pow(2)
+
 
 class ResBlock(nn.Module):
     def __init__(
@@ -61,6 +285,252 @@ class ResBlock(nn.Module):
         return x
 
 
+class AMPBlock1(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 3,
+        dilation: tuple[int, int, int] = (1, 3, 5),
+        activation: str = "snake",
+    ):
+        super().__init__()
+        act_cls = SnakeBeta if activation == "snakebeta" else Snake
+        self.convs1 = nn.ModuleList(
+            [
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[2],
+                    padding=get_padding(kernel_size, dilation[2]),
+                ),
+            ]
+        )
+        self.convs2 = nn.ModuleList(
+            [
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+            ]
+        )
+        self.acts1 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs1))]
+        )
+        self.acts2 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs2))]
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for conv1, conv2, act1, act2 in zip(
+            self.convs1, self.convs2, self.acts1, self.acts2
+        ):
+            xt = act1(x)
+            xt = conv1(xt)
+            xt = act2(xt)
+            xt = conv2(xt)
+            x = x + xt
+        return x
+
+
+class LTX23MelSTFT(nn.Module):
+    class STFTFn(nn.Module):
+        def __init__(self, filter_length: int, hop_length: int, win_length: int):
+            super().__init__()
+            self.hop_length = hop_length
+            self.win_length = win_length
+            n_freqs = filter_length // 2 + 1
+            self.register_buffer(
+                "forward_basis", torch.zeros(n_freqs * 2, 1, filter_length)
+            )
+            self.register_buffer(
+                "inverse_basis", torch.zeros(n_freqs * 2, 1, filter_length)
+            )
+
+        def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+            if y.dim() == 2:
+                y = y.unsqueeze(1)
+            left_pad = max(0, self.win_length - self.hop_length)
+            y = F.pad(y, (left_pad, 0))
+            spec = F.conv1d(y, self.forward_basis, stride=self.hop_length, padding=0)
+            n_freqs = spec.shape[1] // 2
+            real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
+            magnitude = torch.sqrt(real**2 + imag**2)
+            phase = torch.atan2(imag.float(), real.float()).to(real.dtype)
+            return magnitude, phase
+
+    def __init__(
+        self, filter_length: int, hop_length: int, win_length: int, n_mel_channels: int
+    ):
+        super().__init__()
+        self.stft_fn = self.STFTFn(filter_length, hop_length, win_length)
+        n_freqs = filter_length // 2 + 1
+        self.register_buffer("mel_basis", torch.zeros(n_mel_channels, n_freqs))
+
+    def mel_spectrogram(
+        self, y: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        magnitude, phase = self.stft_fn(y)
+        energy = torch.norm(magnitude, dim=1)
+        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
+        log_mel = torch.log(torch.clamp(mel, min=1e-5))
+        return log_mel, magnitude, phase, energy
+
+
+class LTX23VocoderCore(nn.Module):
+    def __init__(  # noqa: PLR0913
+        self,
+        resblock_kernel_sizes: list[int] | None = None,
+        upsample_rates: list[int] | None = None,
+        upsample_kernel_sizes: list[int] | None = None,
+        resblock_dilation_sizes: list[list[int]] | None = None,
+        upsample_initial_channel: int = 1024,
+        resblock: str = "1",
+        output_sampling_rate: int = 24000,
+        activation: str = "snake",
+        use_tanh_at_final: bool = True,
+        apply_final_activation: bool = True,
+        use_bias_at_final: bool = True,
+    ):
+        super().__init__()
+        if resblock_kernel_sizes is None:
+            resblock_kernel_sizes = [3, 7, 11]
+        if upsample_rates is None:
+            upsample_rates = [6, 5, 2, 2, 2]
+        if upsample_kernel_sizes is None:
+            upsample_kernel_sizes = [16, 15, 8, 4, 4]
+        if resblock_dilation_sizes is None:
+            resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+
+        self.output_sampling_rate = output_sampling_rate
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.use_tanh_at_final = use_tanh_at_final
+        self.apply_final_activation = apply_final_activation
+        self.is_amp = resblock == "AMP1"
+
+        self.conv_pre = nn.Conv1d(
+            in_channels=128,
+            out_channels=upsample_initial_channel,
+            kernel_size=7,
+            stride=1,
+            padding=3,
+        )
+        self.ups = nn.ModuleList(
+            nn.ConvTranspose1d(
+                upsample_initial_channel // (2**i),
+                upsample_initial_channel // (2 ** (i + 1)),
+                kernel_size,
+                stride,
+                padding=(kernel_size - stride) // 2,
+            )
+            for i, (stride, kernel_size) in enumerate(
+                zip(upsample_rates, upsample_kernel_sizes, strict=True)
+            )
+        )
+
+        final_channels = upsample_initial_channel // (2 ** len(upsample_rates))
+        self.resblocks = nn.ModuleList()
+        for i in range(len(upsample_rates)):
+            channels = upsample_initial_channel // (2 ** (i + 1))
+            for kernel_size, dilations in zip(
+                resblock_kernel_sizes, resblock_dilation_sizes, strict=True
+            ):
+                if self.is_amp:
+                    self.resblocks.append(
+                        AMPBlock1(
+                            channels,
+                            kernel_size,
+                            tuple(dilations),
+                            activation=activation,
+                        )
+                    )
+                else:
+                    self.resblocks.append(
+                        ResBlock(
+                            channels,
+                            kernel_size=kernel_size,
+                            dilations=tuple(dilations),
+                            leaky_relu_negative_slope=LRELU_SLOPE,
+                            padding_mode=get_padding(kernel_size, 1),
+                        )
+                    )
+
+        self.act_post = (
+            Activation1d(SnakeBeta(final_channels)) if self.is_amp else nn.LeakyReLU()
+        )
+        self.conv_post = nn.Conv1d(
+            in_channels=final_channels,
+            out_channels=2,
+            kernel_size=7,
+            stride=1,
+            padding=3,
+            bias=use_bias_at_final,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.transpose(2, 3)
+        if x.dim() == 4:
+            assert x.shape[1] == 2, "Input must have 2 channels for stereo"
+            x = einops.rearrange(x, "b s c t -> b (s c) t")
+
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            if not self.is_amp:
+                x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            start = i * self.num_kernels
+            end = start + self.num_kernels
+            block_outputs = torch.stack(
+                [self.resblocks[idx](x) for idx in range(start, end)],
+                dim=0,
+            )
+            x = block_outputs.mean(dim=0)
+
+        x = self.act_post(x)
+        x = self.conv_post(x)
+        if self.apply_final_activation:
+            x = torch.tanh(x) if self.use_tanh_at_final else torch.clamp(x, -1, 1)
+        return x
+
+
 class LTX2Vocoder(ABC, nn.Module):
     r"""
     LTX 2.0 vocoder for converting generated mel spectrograms back to audio waveforms.
@@ -72,10 +542,61 @@ class LTX2Vocoder(ABC, nn.Module):
     ):
         super().__init__()
         self.config = config
+        nested_vocoder_cfg = getattr(config.arch_config, "vocoder", None)
+        if isinstance(nested_vocoder_cfg, dict) and "bwe" in nested_vocoder_cfg:
+            vocoder_cfg = nested_vocoder_cfg.get("vocoder", {})
+            bwe_cfg = nested_vocoder_cfg["bwe"]
+            self.vocoder = LTX23VocoderCore(
+                resblock_kernel_sizes=vocoder_cfg.get("resblock_kernel_sizes"),
+                upsample_rates=vocoder_cfg.get("upsample_rates"),
+                upsample_kernel_sizes=vocoder_cfg.get("upsample_kernel_sizes"),
+                resblock_dilation_sizes=vocoder_cfg.get("resblock_dilation_sizes"),
+                upsample_initial_channel=vocoder_cfg.get(
+                    "upsample_initial_channel", 1024
+                ),
+                resblock=vocoder_cfg.get("resblock", "1"),
+                output_sampling_rate=bwe_cfg["input_sampling_rate"],
+                activation=vocoder_cfg.get("activation", "snake"),
+                use_tanh_at_final=vocoder_cfg.get("use_tanh_at_final", True),
+                apply_final_activation=vocoder_cfg.get("apply_final_activation", True),
+                use_bias_at_final=vocoder_cfg.get("use_bias_at_final", True),
+            )
+            self.bwe_generator = LTX23VocoderCore(
+                resblock_kernel_sizes=bwe_cfg.get("resblock_kernel_sizes"),
+                upsample_rates=bwe_cfg.get("upsample_rates"),
+                upsample_kernel_sizes=bwe_cfg.get("upsample_kernel_sizes"),
+                resblock_dilation_sizes=bwe_cfg.get("resblock_dilation_sizes"),
+                upsample_initial_channel=bwe_cfg.get("upsample_initial_channel", 1024),
+                resblock=bwe_cfg.get("resblock", "1"),
+                output_sampling_rate=bwe_cfg["output_sampling_rate"],
+                activation=bwe_cfg.get("activation", "snake"),
+                use_tanh_at_final=bwe_cfg.get("use_tanh_at_final", True),
+                apply_final_activation=bwe_cfg.get("apply_final_activation", True),
+                use_bias_at_final=bwe_cfg.get("use_bias_at_final", True),
+            )
+            self.mel_stft = LTX23MelSTFT(
+                filter_length=bwe_cfg["n_fft"],
+                hop_length=bwe_cfg["hop_length"],
+                win_length=bwe_cfg.get("win_size", bwe_cfg["n_fft"]),
+                n_mel_channels=bwe_cfg["num_mels"],
+            )
+            self.input_sampling_rate = bwe_cfg["input_sampling_rate"]
+            self.output_sampling_rate = bwe_cfg["output_sampling_rate"]
+            self.hop_length = bwe_cfg["hop_length"]
+            with torch.device("cpu"):
+                self.resampler = UpSample1d(
+                    ratio=self.output_sampling_rate // self.input_sampling_rate,
+                    persistent=False,
+                    window_type="hann",
+                )
+            self.sample_rate = self.output_sampling_rate
+            return
+
         self.sample_rate = (
             getattr(config.arch_config, "sample_rate", None)
             or getattr(config.arch_config, "sampling_rate", None)
             or getattr(config.arch_config, "audio_sample_rate", None)
+            or getattr(config.arch_config, "output_sampling_rate", None)
         )
 
         in_channels = config.arch_config.in_channels
@@ -139,6 +660,12 @@ class LTX2Vocoder(ABC, nn.Module):
 
         self.conv_out = nn.Conv1d(output_channels, out_channels, 7, stride=1, padding=3)
 
+    def _compute_ltx23_mel(self, audio: torch.Tensor) -> torch.Tensor:
+        batch, channels, _ = audio.shape
+        flat = audio.reshape(batch * channels, -1)
+        mel, _, _, _ = self.mel_stft.mel_spectrogram(flat)
+        return mel.reshape(batch, channels, mel.shape[1], mel.shape[2])
+
     def forward(
         self, hidden_states: torch.Tensor, time_last: bool = False
     ) -> torch.Tensor:
@@ -157,6 +684,32 @@ class LTX2Vocoder(ABC, nn.Module):
             `torch.Tensor`:
                 Audio waveform tensor of shape (batch_size, out_channels, audio_length)
         """
+        if hasattr(self, "bwe_generator"):
+            input_dtype = hidden_states.dtype
+            autocast_ctx = (
+                torch.autocast(
+                    device_type=hidden_states.device.type, dtype=torch.float32
+                )
+                if hidden_states.device.type != "cpu"
+                else nullcontext()
+            )
+            with autocast_ctx:
+                waveform = self.vocoder(hidden_states.float())
+                length_low_rate = waveform.shape[-1]
+                output_length = (
+                    length_low_rate
+                    * self.output_sampling_rate
+                    // self.input_sampling_rate
+                )
+                remainder = length_low_rate % self.hop_length
+                if remainder != 0:
+                    waveform = F.pad(waveform, (0, self.hop_length - remainder))
+                mel = self._compute_ltx23_mel(waveform)
+                residual = self.bwe_generator(mel.transpose(2, 3))
+                skip = self.resampler(waveform)
+                assert residual.shape == skip.shape
+                waveform = torch.clamp(residual + skip, -1, 1)[..., :output_length]
+            return waveform.to(input_dtype)
 
         # Ensure that the time/frame dimension is last
         if not time_last:

python/sglang/multimodal_gen/runtime/pipelines/ltx_2_pipeline.py

@@ -5,6 +5,9 @@ import numpy as np
 import torch
 from diffusers import FlowMatchEulerDiscreteScheduler
 
+from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import (
+    is_ltx23_native_variant,
+)
 from sglang.multimodal_gen.runtime.loader.component_loaders.component_loader import (
     PipelineComponentLoader,
 )
@@ -44,6 +47,8 @@ def _resolve_ltx2_two_stage_component_paths(
     if "spatial_upsampler" not in resolved:
         spatial_candidates = [
             os.path.join(model_path, "latent_upsampler"),
+            os.path.join(model_path, "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"),
+            os.path.join(model_path, "ltx-2.3-spatial-upscaler-x2-1.0.safetensors"),
             os.path.join(model_path, "ltx-2-spatial-upscaler-x2-1.0.safetensors"),
         ]
         for candidate in spatial_candidates:
@@ -53,12 +58,15 @@ def _resolve_ltx2_two_stage_component_paths(
                 break
 
     if "distilled_lora" not in resolved:
-        distilled_lora = os.path.join(
-            model_path, "ltx-2-19b-distilled-lora-384.safetensors"
-        )
-        if os.path.exists(distilled_lora):
-            resolved["distilled_lora"] = distilled_lora
-            auto_resolved.append(f"distilled_lora={distilled_lora}")
+        distilled_lora_candidates = [
+            os.path.join(model_path, "ltx-2.3-22b-distilled-lora-384.safetensors"),
+            os.path.join(model_path, "ltx-2-19b-distilled-lora-384.safetensors"),
+        ]
+        for distilled_lora in distilled_lora_candidates:
+            if os.path.exists(distilled_lora):
+                resolved["distilled_lora"] = distilled_lora
+                auto_resolved.append(f"distilled_lora={distilled_lora}")
+                break
 
     if auto_resolved:
         logger.info(
@@ -81,6 +89,8 @@ def calculate_ltx2_shift(
 
 
 def prepare_ltx2_mu(batch: Req, server_args: ServerArgs):
+    if is_ltx23_native_variant(server_args.pipeline_config.vae_config.arch_config):
+        return "mu", None
     latent_num_frames = (int(batch.num_frames) - 1) // int(
         server_args.pipeline_config.vae_temporal_compression
     ) + 1
@@ -92,16 +102,49 @@ def prepare_ltx2_mu(batch: Req, server_args: ServerArgs):
     return "mu", calculate_ltx2_shift(video_sequence_length)
 
 
+def build_official_ltx2_sigmas(
+    steps: int,
+    *,
+    max_shift: float = 2.05,
+    base_shift: float = 0.95,
+    stretch: bool = True,
+    terminal: float = 0.1,
+    default_number_of_tokens: int = MAX_SHIFT_ANCHOR,
+) -> list[float]:
+    sigmas = torch.linspace(1.0, 0.0, steps + 1, dtype=torch.float32)
+
+    mm = (max_shift - base_shift) / (MAX_SHIFT_ANCHOR - BASE_SHIFT_ANCHOR)
+    b = base_shift - mm * BASE_SHIFT_ANCHOR
+    sigma_shift = float(default_number_of_tokens) * mm + b
+
+    non_zero_mask = sigmas != 0
+    shifted = torch.where(
+        non_zero_mask,
+        math.exp(sigma_shift) / (math.exp(sigma_shift) + (1.0 / sigmas - 1.0)),
+        torch.zeros_like(sigmas),
+    )
+
+    if stretch:
+        one_minus_z = 1.0 - shifted[non_zero_mask]
+        scale_factor = one_minus_z[-1] / (1.0 - terminal)
+        shifted[non_zero_mask] = 1.0 - (one_minus_z / scale_factor)
+
+    return shifted[:-1].tolist()
+
+
 class LTX2SigmaPreparationStage(PipelineStage):
     """Prepare native LTX-2 sigma schedule before timestep setup."""
 
     def forward(self, batch: Req, server_args: ServerArgs) -> Req:
         batch.extra["ltx2_phase"] = "stage1"
-        batch.sigmas = np.linspace(
-            1.0,
-            1.0 / int(batch.num_inference_steps),
-            int(batch.num_inference_steps),
-        ).tolist()
+        if is_ltx23_native_variant(server_args.pipeline_config.vae_config.arch_config):
+            batch.sigmas = build_official_ltx2_sigmas(int(batch.num_inference_steps))
+        else:
+            batch.sigmas = np.linspace(
+                1.0,
+                1.0 / int(batch.num_inference_steps),
+                int(batch.num_inference_steps),
+            ).tolist()
         return batch
 
 

python/sglang/multimodal_gen/runtime/pipelines_core/__init__.py

@@ -66,6 +66,7 @@ def build_pipeline(
         )
     else:
         logger.info("No pipeline_class_name specified, using model_index.json")
+
         model_info = get_model_info(
             model_path,
             backend=server_args.backend,

python/sglang/multimodal_gen/runtime/pipelines_core/schedule_batch.py

@@ -135,7 +135,7 @@ class Req:
     trajectory_latents: torch.Tensor | None = None
     trajectory_audio_latents: torch.Tensor | None = None
 
-    # Extra parameters that might be needed by specific pipeline implementations
+    # Extra parameters that might be needed by specific pipeline implementations (e.g., LTX2.3 DenoisingAVStage)
     extra: dict[str, Any] = field(default_factory=dict)
 
     is_warmup: bool = False

python/sglang/multimodal_gen/runtime/pipelines_core/stages/decoding_av.py

@@ -25,6 +25,11 @@ class LTX2AVDecodingStage(DecodingStage):
 
         self.video_processor = VideoProcessor(vae_scale_factor=32)
 
+    @staticmethod
+    def _ltx2_should_externally_denorm_video_latents(server_args: ServerArgs) -> bool:
+        arch_config = server_args.pipeline_config.vae_config.arch_config
+        return str(getattr(arch_config, "video_decoder_variant", "ltx_2")) != "ltx_2_3"
+
     def forward(self, batch: Req, server_args: ServerArgs) -> OutputBatch:
         self.load_model()
 
@@ -40,9 +45,10 @@ class LTX2AVDecodingStage(DecodingStage):
         original_dtype = vae_dtype
         self.vae.to(torch.bfloat16)
         latents = latents.to(torch.bfloat16)
-        std = self.vae.latents_std.view(1, -1, 1, 1, 1).to(latents)
-        mean = self.vae.latents_mean.view(1, -1, 1, 1, 1).to(latents)
-        latents = latents * std + mean
+        if self._ltx2_should_externally_denorm_video_latents(server_args):
+            std = self.vae.latents_std.view(1, -1, 1, 1, 1).to(latents)
+            mean = self.vae.latents_mean.view(1, -1, 1, 1, 1).to(latents)
+            latents = latents * std + mean
         latents = server_args.pipeline_config.preprocess_decoding(
             latents, server_args, vae=self.vae
         )

python/sglang/multimodal_gen/runtime/pipelines_core/stages/denoising_av.py

@@ -1,5 +1,7 @@
 import copy
+import json
 import math
+import os
 import time
 from io import BytesIO
 
@@ -10,8 +12,15 @@ import torch
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from diffusers.models.modeling_outputs import AutoencoderKLOutput
 from diffusers.utils.torch_utils import randn_tensor
+from safetensors.torch import load_file as safetensors_load_file
 
+from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import (
+    is_ltx23_native_variant,
+)
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.models.vaes.ltx_2_3_condition_encoder import (
+    LTX23VideoConditionEncoder,
+)
 from sglang.multimodal_gen.runtime.models.vision_utils import (
     load_image,
     normalize,
@@ -46,6 +55,8 @@ class LTX2AVDenoisingStage(DenoisingStage):
             transformer=transformer, scheduler=scheduler, vae=vae, **kwargs
         )
         self.audio_vae = audio_vae
+        self._condition_image_encoder = None
+        self._condition_image_encoder_dir = None
 
     @staticmethod
     def _get_video_latent_num_frames_for_model(
@@ -116,13 +127,6 @@ class LTX2AVDenoisingStage(DenoisingStage):
     ) -> dict[str, object] | None:
         if stage != "stage1":
             return None
-
-        pipeline_ref = getattr(self, "pipeline", None)
-        pipeline = pipeline_ref() if callable(pipeline_ref) else pipeline_ref
-        pipeline_name = getattr(pipeline, "pipeline_name", None)
-        if pipeline_name != "LTX2TwoStagePipeline":
-            return None
-
         return batch.extra.get("ltx2_stage1_guider_params")
 
     @staticmethod
@@ -141,6 +145,56 @@ class LTX2AVDenoisingStage(DenoisingStage):
         factor = rescale_scale * factor + (1.0 - rescale_scale)
         return pred * factor
 
+    @staticmethod
+    def _prepare_ltx2_ti2v_clean_state(
+        latents: torch.Tensor,
+        image_latent: torch.Tensor,
+        num_img_tokens: int,
+        zero_clean_latent: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        latents = latents.clone()
+        conditioned = image_latent[:, :num_img_tokens, :].to(
+            device=latents.device, dtype=latents.dtype
+        )
+        latents[:, :num_img_tokens, :] = conditioned
+        denoise_mask = torch.ones(
+            (latents.shape[0], latents.shape[1], 1),
+            device=latents.device,
+            dtype=torch.float32,
+        )
+        denoise_mask[:, :num_img_tokens, :] = 0.0
+        if zero_clean_latent:
+            clean_latent = torch.zeros_like(latents)
+        else:
+            clean_latent = latents.detach().clone()
+        clean_latent[:, :num_img_tokens, :] = conditioned
+        return latents, denoise_mask, clean_latent
+
+    @staticmethod
+    def _ltx2_velocity_to_x0(
+        sample: torch.Tensor,
+        velocity: torch.Tensor,
+        sigma: float | torch.Tensor,
+    ) -> torch.Tensor:
+        if isinstance(sigma, torch.Tensor):
+            sigma = sigma.to(device=sample.device, dtype=torch.float32)
+            while sigma.ndim < sample.ndim:
+                sigma = sigma.unsqueeze(-1)
+            return (sample.float() - sigma * velocity.float()).to(sample.dtype)
+        return (sample.float() - float(sigma) * velocity.float()).to(sample.dtype)
+
+    @staticmethod
+    def _repeat_batch_dim(tensor: torch.Tensor, target_batch_size: int) -> torch.Tensor:
+        """Repeat along batch dim while preserving any tokenwise timestep layout."""
+        if tensor.shape[0] == int(target_batch_size):
+            return tensor
+        if tensor.shape[0] <= 0 or int(target_batch_size) % int(tensor.shape[0]) != 0:
+            raise ValueError(
+                f"Cannot repeat tensor with batch={tensor.shape[0]} to target_batch_size={target_batch_size}"
+            )
+        repeat_factor = int(target_batch_size) // int(tensor.shape[0])
+        return tensor.repeat(repeat_factor, *([1] * (tensor.ndim - 1)))
+
     @classmethod
     def _ltx2_calculate_guided_x0(
         cls,
@@ -252,6 +306,40 @@ class LTX2AVDenoisingStage(DenoisingStage):
             return True
         return int(getattr(batch, "sp_video_start_frame", 0)) == 0
 
+    def _get_condition_image_encoder(
+        self,
+        server_args: ServerArgs,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> LTX23VideoConditionEncoder | None:
+        arch_config = server_args.pipeline_config.vae_config.arch_config
+        encoder_subdir = str(getattr(arch_config, "condition_encoder_subdir", ""))
+        if not encoder_subdir:
+            return None
+
+        vae_model_path = server_args.model_paths["vae"]
+        encoder_dir = os.path.join(vae_model_path, encoder_subdir)
+        config_path = os.path.join(encoder_dir, "config.json")
+        weights_path = os.path.join(encoder_dir, "model.safetensors")
+        if not os.path.exists(config_path) or not os.path.exists(weights_path):
+            raise ValueError(
+                f"LTX-2 condition encoder files not found under {encoder_dir}"
+            )
+
+        cached_dir = self._condition_image_encoder_dir
+        encoder = self._condition_image_encoder
+        if encoder is None or cached_dir != encoder_dir:
+            with open(config_path, encoding="utf-8") as f:
+                config = json.load(f)
+            encoder = LTX23VideoConditionEncoder(config)
+            encoder.load_state_dict(safetensors_load_file(weights_path), strict=True)
+            self._condition_image_encoder = encoder
+            self._condition_image_encoder_dir = encoder_dir
+
+        encoder = encoder.to(device=device, dtype=dtype)
+        return encoder
+
     def _prepare_ltx2_image_latent(self, batch: Req, server_args: ServerArgs) -> None:
         """Encode `batch.image_path` into packed token latents for LTX-2 TI2V."""
         if (
@@ -276,8 +364,11 @@ class LTX2AVDenoisingStage(DenoisingStage):
         )
 
         img = load_image(image_path)
+        img_array = np.array(img).astype(np.uint8)[..., :3]
+        img_array = self._apply_video_codec_compression(img_array, crf=33)
+        conditioned_img = PIL.Image.fromarray(img_array)
         batch.condition_image = self._resize_center_crop(
-            img, width=int(batch.width), height=int(batch.height)
+            conditioned_img, width=int(batch.width), height=int(batch.height)
         )
 
         latents_device = (
@@ -287,17 +378,22 @@ class LTX2AVDenoisingStage(DenoisingStage):
         )
         encode_dtype = batch.latents.dtype
         original_dtype = PRECISION_TO_TYPE[server_args.pipeline_config.vae_precision]
-        self.vae = self.vae.to(device=latents_device, dtype=encode_dtype)
         vae_autocast_enabled = (
             original_dtype != torch.float32
         ) and not server_args.disable_autocast
+        condition_image_encoder = self._get_condition_image_encoder(
+            server_args, device=latents_device, dtype=encode_dtype
+        )
+        if condition_image_encoder is None:
+            self.vae = self.vae.to(device=latents_device, dtype=encode_dtype)
 
         video_condition = self._resize_center_crop_tensor(
-            img,
+            conditioned_img,
             width=int(batch.width),
             height=int(batch.height),
             device=latents_device,
             dtype=encode_dtype,
+            apply_codec_compression=False,
         )
 
         with torch.autocast(
@@ -306,31 +402,42 @@ class LTX2AVDenoisingStage(DenoisingStage):
             enabled=vae_autocast_enabled,
         ):
             try:
-                if server_args.pipeline_config.vae_tiling:
+                if (
+                    condition_image_encoder is None
+                    and server_args.pipeline_config.vae_tiling
+                ):
                     self.vae.enable_tiling()
             except Exception:
                 pass
             if not vae_autocast_enabled:
                 video_condition = video_condition.to(encode_dtype)
 
-            latent_dist: DiagonalGaussianDistribution = self.vae.encode(video_condition)
-            if isinstance(latent_dist, AutoencoderKLOutput):
-                latent_dist = latent_dist.latent_dist
+            if condition_image_encoder is not None:
+                latent = condition_image_encoder(video_condition)
+            else:
+                latent_dist: DiagonalGaussianDistribution = self.vae.encode(
+                    video_condition
+                )
+                if isinstance(latent_dist, AutoencoderKLOutput):
+                    latent_dist = latent_dist.latent_dist
 
-        mode = server_args.pipeline_config.vae_config.encode_sample_mode()
-        if mode == "argmax":
-            latent = latent_dist.mode()
-        elif mode == "sample":
-            if batch.generator is None:
-                raise ValueError("Generator must be provided for VAE sampling.")
-            latent = latent_dist.sample(batch.generator)
+        if condition_image_encoder is None:
+            mode = server_args.pipeline_config.vae_config.encode_sample_mode()
+            if mode == "argmax":
+                latent = latent_dist.mode()
+            elif mode == "sample":
+                if batch.generator is None:
+                    raise ValueError("Generator must be provided for VAE sampling.")
+                latent = latent_dist.sample(batch.generator)
+            else:
+                raise ValueError(f"Unsupported encode_sample_mode: {mode}")
+
+            # Per-channel normalization: normalized = (x - mean) / std
+            mean = self.vae.latents_mean.view(1, -1, 1, 1, 1).to(latent)
+            std = self.vae.latents_std.view(1, -1, 1, 1, 1).to(latent)
+            latent = (latent - mean) / std
         else:
-            raise ValueError(f"Unsupported encode_sample_mode: {mode}")
-
-        # Per-channel normalization: normalized = (x - mean) / std
-        mean = self.vae.latents_mean.view(1, -1, 1, 1, 1).to(latent)
-        std = self.vae.latents_std.view(1, -1, 1, 1, 1).to(latent)
-        latent = (latent - mean) / std
+            latent = latent.to(dtype=encode_dtype)
 
         packed = server_args.pipeline_config.maybe_pack_latents(
             latent, latent.shape[0], batch
@@ -362,9 +469,12 @@ class LTX2AVDenoisingStage(DenoisingStage):
                 batch.height,
             )
 
-        self.vae.to(original_dtype)
+        if condition_image_encoder is None:
+            self.vae.to(original_dtype)
         if server_args.vae_cpu_offload:
             self.vae = self.vae.to("cpu")
+            if condition_image_encoder is not None:
+                self._condition_image_encoder = condition_image_encoder.to("cpu")
 
     @torch.no_grad()
     def forward(self, batch: Req, server_args: ServerArgs) -> Req:
@@ -437,19 +547,15 @@ class LTX2AVDenoisingStage(DenoisingStage):
         if do_ti2v:
             if not (isinstance(latents, torch.Tensor) and latents.ndim == 3):
                 raise ValueError("LTX-2 TI2V expects packed token latents [B, S, D].")
-            latents[:, :num_img_tokens, :] = batch.image_latent[
-                :, :num_img_tokens, :
-            ].to(device=latents.device, dtype=latents.dtype)
-            denoise_mask = torch.ones(
-                (latents.shape[0], latents.shape[1], 1),
-                device=latents.device,
-                dtype=torch.float32,
+            use_zero_clean_latent = is_ltx23_native_variant(
+                server_args.pipeline_config.vae_config.arch_config
+            )
+            latents, denoise_mask, clean_latent = self._prepare_ltx2_ti2v_clean_state(
+                latents=latents,
+                image_latent=batch.image_latent,
+                num_img_tokens=num_img_tokens,
+                zero_clean_latent=use_zero_clean_latent,
             )
-            denoise_mask[:, :num_img_tokens, :] = 0.0
-            clean_latent = latents.detach().clone()
-            clean_latent[:, :num_img_tokens, :] = batch.image_latent[
-                :, :num_img_tokens, :
-            ].to(device=latents.device, dtype=latents.dtype)
         with torch.autocast(
             device_type=current_platform.device_type,
             dtype=target_dtype,
@@ -558,11 +664,12 @@ class LTX2AVDenoisingStage(DenoisingStage):
                                     ],
                                     dim=0,
                                 )
-                                timestep_video = timestep_video.expand(
-                                    int(latent_model_input.shape[0])
+                                cfg_batch_size = int(latent_model_input.shape[0])
+                                timestep_video = self._repeat_batch_dim(
+                                    timestep_video, cfg_batch_size
                                 )
-                                timestep_audio = timestep_audio.expand(
-                                    int(latent_model_input.shape[0])
+                                timestep_audio = self._repeat_batch_dim(
+                                    timestep_audio, cfg_batch_size
                                 )
 
                             with set_forward_context(
@@ -616,11 +723,6 @@ class LTX2AVDenoisingStage(DenoisingStage):
                             audio_latents = audio_scheduler.step(
                                 a_v_pos, t_device, audio_latents, return_dict=False
                             )[0]
-                            if do_ti2v:
-                                latents[:, :num_img_tokens, :] = batch.image_latent[
-                                    :, :num_img_tokens, :
-                                ].to(device=latents.device, dtype=latents.dtype)
-
                             latents = self.post_forward_for_ti2v_task(
                                 batch, server_args, reserved_frames_mask, latents, z
                             )
@@ -714,14 +816,20 @@ class LTX2AVDenoisingStage(DenoisingStage):
                             if a_v_neg is not None:
                                 a_v_neg = a_v_neg.float()
 
-                        # Velocity -> denoised (x0): x0 = x - sigma * v
                         sigma_val = float(sigma.item())
-                        denoised_video = (latents.float() - sigma_val * v_pos).to(
-                            latents.dtype
+                        video_sigma_for_x0: float | torch.Tensor = sigma_val
+                        if do_ti2v and denoise_mask is not None:
+                            video_sigma_for_x0 = sigma.to(
+                                device=latents.device, dtype=torch.float32
+                            ) * denoise_mask.squeeze(-1)
+                        denoised_video = self._ltx2_velocity_to_x0(
+                            latents, v_pos, video_sigma_for_x0
                         )
-                        denoised_audio = (
-                            audio_latents.float() - sigma_val * a_v_pos
-                        ).to(audio_latents.dtype)
+                        denoised_audio = self._ltx2_velocity_to_x0(
+                            audio_latents, a_v_pos, sigma_val
+                        )
+                        denoised_video_cond = denoised_video
+                        denoised_audio_cond = denoised_audio
                         denoised_video_neg = None
                         denoised_audio_neg = None
                         denoised_video_perturbed = None
@@ -737,12 +845,12 @@ class LTX2AVDenoisingStage(DenoisingStage):
                             and v_neg is not None
                             and a_v_neg is not None
                         ):
-                            denoised_video_neg = (
-                                latents.float() - sigma_val * v_neg
-                            ).to(latents.dtype)
-                            denoised_audio_neg = (
-                                audio_latents.float() - sigma_val * a_v_neg
-                            ).to(audio_latents.dtype)
+                            denoised_video_neg = self._ltx2_velocity_to_x0(
+                                latents, v_neg, video_sigma_for_x0
+                            )
+                            denoised_audio_neg = self._ltx2_velocity_to_x0(
+                                audio_latents, a_v_neg, sigma_val
+                            )
                         if stage1_guider_params is not None:
                             video_skip = self._ltx2_should_skip_step(
                                 i, int(stage1_guider_params["video_skip_step"])
@@ -784,12 +892,12 @@ class LTX2AVDenoisingStage(DenoisingStage):
                                             stage1_guider_params["audio_stg_blocks"]
                                         ),
                                     )
-                                denoised_video_perturbed = (
-                                    latents.float() - sigma_val * v_ptb.float()
-                                ).to(latents.dtype)
-                                denoised_audio_perturbed = (
-                                    audio_latents.float() - sigma_val * a_v_ptb.float()
-                                ).to(audio_latents.dtype)
+                                denoised_video_perturbed = self._ltx2_velocity_to_x0(
+                                    latents, v_ptb.float(), video_sigma_for_x0
+                                )
+                                denoised_audio_perturbed = self._ltx2_velocity_to_x0(
+                                    audio_latents, a_v_ptb.float(), sigma_val
+                                )
 
                             need_modality = (
                                 float(stage1_guider_params["video_modality_scale"])
@@ -822,12 +930,12 @@ class LTX2AVDenoisingStage(DenoisingStage):
                                         disable_a2v_cross_attn=True,
                                         disable_v2a_cross_attn=True,
                                     )
-                                denoised_video_modality = (
-                                    latents.float() - sigma_val * v_mod.float()
-                                ).to(latents.dtype)
-                                denoised_audio_modality = (
-                                    audio_latents.float() - sigma_val * a_v_mod.float()
-                                ).to(audio_latents.dtype)
+                                denoised_video_modality = self._ltx2_velocity_to_x0(
+                                    latents, v_mod.float(), video_sigma_for_x0
+                                )
+                                denoised_audio_modality = self._ltx2_velocity_to_x0(
+                                    audio_latents, a_v_mod.float(), sigma_val
+                                )
 
                             if not video_skip:
                                 denoised_video = self._ltx2_calculate_guided_x0(
@@ -934,11 +1042,6 @@ class LTX2AVDenoisingStage(DenoisingStage):
                             audio_latents.float() + v_audio.float() * dt
                         ).to(dtype=audio_latents.dtype)
 
-                        if do_ti2v:
-                            latents[:, :num_img_tokens, :] = batch.image_latent[
-                                :, :num_img_tokens, :
-                            ].to(device=latents.device, dtype=latents.dtype)
-
                         latents = self.post_forward_for_ti2v_task(
                             batch, server_args, reserved_frames_mask, latents, z
                         )

python/sglang/multimodal_gen/runtime/pipelines_core/stages/latent_preparation_av.py

@@ -1,6 +1,9 @@
 import torch
 from diffusers.utils.torch_utils import randn_tensor
 
+from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import (
+    is_ltx23_native_variant,
+)
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.latent_preparation import (
@@ -60,10 +63,112 @@ class LTX2AVLatentPreparationStage(LatentPreparationStage):
     ):
         return torch.float32
 
+    @staticmethod
+    def _packed_video_latent_shape(
+        latent_shape: tuple[int, int, int, int, int],
+        pipeline_config,
+    ) -> tuple[int, int, int]:
+        batch_size, channels, num_frames, height, width = latent_shape
+        patch_size_t = int(pipeline_config.patch_size_t)
+        patch_size = int(pipeline_config.patch_size)
+        return (
+            batch_size,
+            (num_frames // patch_size_t)
+            * (height // patch_size)
+            * (width // patch_size),
+            channels * patch_size_t * patch_size * patch_size,
+        )
+
+    @staticmethod
+    def _packed_audio_latent_shape(
+        latent_shape: tuple[int, int, int, int],
+    ) -> tuple[int, int, int]:
+        batch_size, channels, latent_length, mel_bins = latent_shape
+        return (batch_size, latent_length, channels * mel_bins)
+
     def forward(self, batch: Req, server_args: ServerArgs) -> Req:
-        # 1. Prepare Video Latents using base class logic
-        # This sets batch.latents and batch.raw_latent_shape
-        batch = super().forward(batch, server_args)
+        if not is_ltx23_native_variant(
+            server_args.pipeline_config.vae_config.arch_config
+        ):
+            batch = super().forward(batch, server_args)
+
+            try:
+                generate_audio = batch.generate_audio
+            except AttributeError:
+                generate_audio = True
+            if not generate_audio:
+                batch.audio_latents = None
+                batch.raw_audio_latent_shape = None
+                return batch
+
+            device = get_local_torch_device()
+            dtype = self._get_latent_dtype(batch, server_args)
+            generator = batch.generator
+
+            audio_latents = batch.audio_latents
+            batch_size = batch.batch_size
+            num_frames = batch.num_frames
+
+            if audio_latents is None:
+                shape = server_args.pipeline_config.prepare_audio_latent_shape(
+                    batch, batch_size, num_frames
+                )
+
+                audio_latents = randn_tensor(
+                    shape, generator=generator, device=device, dtype=dtype
+                )
+            else:
+                audio_latents = audio_latents.to(device)
+
+            audio_latents = server_args.pipeline_config.maybe_pack_audio_latents(
+                audio_latents, batch_size, batch
+            )
+
+            batch.audio_latents = audio_latents
+            batch.raw_audio_latent_shape = audio_latents.shape
+            return batch
+
+        # 1. Prepare video latents directly in packed token space.
+        # Official LTX-2.3 pipelines sample noise after patchify; generating unpacked
+        # [B, C, F, H, W] noise and packing afterwards changes token ordering.
+        latent_num_frames = self.adjust_video_length(batch, server_args)
+        batch_size = batch.batch_size
+        dtype = self._get_latent_dtype(batch, server_args)
+        device = get_local_torch_device()
+        generator = batch.generator
+
+        latents = batch.latents
+        num_frames = (
+            latent_num_frames if latent_num_frames is not None else batch.num_frames
+        )
+
+        if latents is None:
+            latent_shape = server_args.pipeline_config.prepare_latent_shape(
+                batch, batch_size, num_frames
+            )
+            latents = randn_tensor(
+                self._packed_video_latent_shape(
+                    latent_shape, server_args.pipeline_config
+                ),
+                generator=generator,
+                device=device,
+                dtype=dtype,
+            )
+
+            latent_ids = server_args.pipeline_config.maybe_prepare_latent_ids(latents)
+            if latent_ids is not None:
+                batch.latent_ids = latent_ids.to(device=device)
+        else:
+            latents = latents.to(device)
+            latents = server_args.pipeline_config.maybe_pack_latents(
+                latents, batch_size, batch
+            )
+
+        if hasattr(self.scheduler, "init_noise_sigma"):
+            latents = latents * self.scheduler.init_noise_sigma
+
+        batch.latents = latents
+        batch.raw_latent_shape = latents.shape
 
         # 2. Prepare Audio Latents (optional)
         # Default to True if not specified
@@ -76,28 +181,24 @@ class LTX2AVLatentPreparationStage(LatentPreparationStage):
             batch.raw_audio_latent_shape = None
             return batch
 
-        device = get_local_torch_device()
-        dtype = self._get_latent_dtype(batch, server_args)
-        generator = batch.generator
-
         audio_latents = batch.audio_latents
-        batch_size = batch.batch_size
-        num_frames = batch.num_frames
 
         if audio_latents is None:
-            shape = server_args.pipeline_config.prepare_audio_latent_shape(
-                batch, batch_size, num_frames
+            latent_shape = server_args.pipeline_config.prepare_audio_latent_shape(
+                batch, batch_size, batch.num_frames
             )
 
             audio_latents = randn_tensor(
-                shape, generator=generator, device=device, dtype=dtype
+                self._packed_audio_latent_shape(latent_shape),
+                generator=generator,
+                device=device,
+                dtype=dtype,
             )
         else:
             audio_latents = audio_latents.to(device)
-
-        audio_latents = server_args.pipeline_config.maybe_pack_audio_latents(
-            audio_latents, batch_size, batch
-        )
+            audio_latents = server_args.pipeline_config.maybe_pack_audio_latents(
+                audio_latents, batch_size, batch
+            )
 
         # Store in batch
         batch.audio_latents = audio_latents

python/sglang/multimodal_gen/runtime/utils/model_overlay.py

@@ -24,8 +24,13 @@ from sglang.utils import load_diffusion_overlay_registry_from_env
 logger = init_logger(__name__)
 
 # Built-in diffusion model overlay registry.
-# Keep this empty until concrete overlay repos are ready to ship.
-BUILTIN_MODEL_OVERLAY_REGISTRY: dict[str, dict[str, Any]] = {}
+BUILTIN_MODEL_OVERLAY_REGISTRY: dict[str, dict[str, Any]] = {
+    "Lightricks/LTX-2.3": {
+        "overlay_repo_id": "MickJ/LTX-2.3-overlay",
+        "overlay_revision": "main",
+        "bundled_overlay_subdir": "ltx_2_3",
+    },
+}
 
 
 MODEL_OVERLAY_METADATA_PATTERNS = [
@@ -42,6 +47,43 @@ MODEL_OVERLAY_METADATA_PATTERNS = [
 _MODEL_OVERLAY_REGISTRY_CACHE: dict[str, dict[str, Any]] | None = None
 
 
+def _compute_overlay_fingerprint(overlay_dir: str) -> str:
+    hasher = hashlib.sha256()
+    for root, dir_names, file_names in os.walk(overlay_dir):
+        dir_names[:] = sorted(
+            d for d in dir_names if d != "__pycache__" and not d.endswith(".egg-info")
+        )
+        for file_name in sorted(file_names):
+            if file_name.endswith((".safetensors", ".bin", ".pth", ".pt")):
+                continue
+            file_path = os.path.join(root, file_name)
+            rel_path = os.path.relpath(file_path, overlay_dir).replace(os.sep, "/")
+            hasher.update(rel_path.encode("utf-8"))
+            with open(file_path, "rb") as f:
+                hasher.update(hashlib.sha256(f.read()).digest())
+    return hasher.hexdigest()
+
+
+def _resolve_bundled_overlay_dir(overlay_spec: dict[str, Any]) -> str | None:
+    bundled_overlay_subdir = overlay_spec.get("bundled_overlay_subdir")
+    if not bundled_overlay_subdir:
+        return None
+    bundled_overlay_dir = os.path.abspath(
+        os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "..",
+            "model_overlays",
+            str(bundled_overlay_subdir),
+        )
+    )
+    if not os.path.isdir(bundled_overlay_dir):
+        return None
+    if load_overlay_manifest_if_present(bundled_overlay_dir) is None:
+        return None
+    return bundled_overlay_dir
+
+
 def get_diffusion_cache_root() -> str:
     return os.path.expanduser(
         os.getenv("SGLANG_DIFFUSION_CACHE_ROOT", "~/.cache/sgl_diffusion")
@@ -300,6 +342,15 @@ def download_overlay_metadata(
     *,
     snapshot_download_fn: Callable[..., str],
 ) -> str:
+    bundled_overlay_dir = _resolve_bundled_overlay_dir(overlay_spec)
+    if bundled_overlay_dir is not None:
+        logger.info(
+            "Using bundled overlay metadata for %s from %s",
+            source_model_id,
+            bundled_overlay_dir,
+        )
+        return bundled_overlay_dir
+
     overlay_repo_id = str(overlay_spec["overlay_repo_id"])
     if os.path.exists(overlay_repo_id):
         logger.info(
@@ -419,6 +470,7 @@ def materialize_overlay_model(
     materializer_version = str(manifest.get("materializer_version", "v1"))
     overlay_repo_id = str(overlay_spec["overlay_repo_id"])
     overlay_revision = str(overlay_spec.get("overlay_revision", "main"))
+    overlay_fingerprint = _compute_overlay_fingerprint(overlay_dir)
     cache_key = hashlib.sha256(
         json.dumps(
             {
@@ -426,6 +478,7 @@ def materialize_overlay_model(
                 "overlay_repo_id": overlay_repo_id,
                 "overlay_revision": overlay_revision,
                 "materializer_version": materializer_version,
+                "overlay_fingerprint": overlay_fingerprint,
             },
             sort_keys=True,
         ).encode("utf-8")
@@ -502,6 +555,7 @@ def materialize_overlay_model(
                     "overlay_repo_id": overlay_repo_id,
                     "overlay_revision": overlay_revision,
                     "materializer_version": materializer_version,
+                    "overlay_fingerprint": overlay_fingerprint,
                 },
                 f,
                 indent=2,

python/sglang/multimodal_gen/test/server/perf_baselines.json

@@ -2504,6 +2504,54 @@
             "expected_e2e_ms": 8091.46,
             "expected_avg_denoise_ms": 141.37,
             "expected_median_denoise_ms": 142.63
+        },
+        "ltx_2.3_one_stage_ti2v": {
+            "stages_ms": {
+                "InputValidationStage": 3.27,
+                "TextEncodingStage": 1766.71,
+                "LTX2TextConnectorStage": 27.34,
+                "LTX2SigmaPreparationStage": 0.14,
+                "TimestepPreparationStage": 15.78,
+                "LTX2AVLatentPreparationStage": 0.25,
+                "LTX2AVDenoisingStage": 23757.65,
+                "LTX2AVDecodingStage": 1171.65,
+                "per_frame_generation": null
+            },
+            "denoise_step_ms": {
+                "0": 709.91,
+                "1": 704.93,
+                "2": 707.02,
+                "3": 702.54,
+                "4": 702.07,
+                "5": 746.58,
+                "6": 774.04,
+                "7": 766.37,
+                "8": 751.37,
+                "9": 734.67,
+                "10": 710.79,
+                "11": 689.84,
+                "12": 688.12,
+                "13": 689.74,
+                "14": 687.5,
+                "15": 699.65,
+                "16": 697.09,
+                "17": 686.87,
+                "18": 691.43,
+                "19": 712.52,
+                "20": 705.43,
+                "21": 727.21,
+                "22": 706.73,
+                "23": 705.63,
+                "24": 714.95,
+                "25": 707.69,
+                "26": 748.35,
+                "27": 738.02,
+                "28": 738.05,
+                "29": 726.96
+            },
+            "expected_e2e_ms": 26916.58,
+            "expected_avg_denoise_ms": 715.73,
+            "expected_median_denoise_ms": 707.35
         }
     }
 }

python/sglang/multimodal_gen/test/server/testcase_configs.py

@@ -935,7 +935,6 @@ TWO_GPU_CASES_A = [
             model_path="Lightricks/LTX-2",
             modality="video",
             num_gpus=2,
-            dit_layerwise_offload=True,
             extras=["--pipeline-class-name LTX2TwoStagePipeline"],
         ),
         T2V_sampling_params,
@@ -1040,6 +1039,15 @@ TWO_GPU_CASES_B = [
         ),
         TI2I_sampling_params,
     ),
+    DiffusionTestCase(
+        "ltx_2.3_one_stage_ti2v",
+        DiffusionServerArgs(
+            model_path="Lightricks/LTX-2.3",
+            modality="video",
+            num_gpus=2,
+        ),
+        TI2V_sampling_params,
+    ),
 ]
 
 if not current_platform.is_hip():

python/sglang/multimodal_gen/test/unit/test_model_overlay_ltx23.py

@@ -0,0 +1,392 @@
+import json
+import os
+import tempfile
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+pytest.importorskip("triton.compiler")
+
+from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import (
+    is_ltx23_native_variant,
+)
+from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
+from sglang.multimodal_gen.model_overlays.ltx_2_3._overlay.materialize import (
+    _build_transformer_config,
+    _build_vae_config,
+    _rename_connector_key,
+    _repack_ltx23_image_encoder_weights,
+    _repack_ltx23_video_decoder_weights,
+)
+from sglang.multimodal_gen.registry import get_model_info
+from sglang.multimodal_gen.runtime.pipelines.ltx_2_pipeline import (
+    _resolve_ltx2_two_stage_component_paths,
+    build_official_ltx2_sigmas,
+    prepare_ltx2_mu,
+)
+from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
+from sglang.multimodal_gen.runtime.pipelines_core.stages.decoding_av import (
+    LTX2AVDecodingStage,
+)
+from sglang.multimodal_gen.runtime.pipelines_core.stages.denoising_av import (
+    LTX2AVDenoisingStage,
+)
+from sglang.multimodal_gen.runtime.utils.model_overlay import (
+    resolve_model_overlay_target,
+)
+
+
+def _make_req(**sampling_kwargs) -> Req:
+    return Req(
+        sampling_params=SamplingParams(**sampling_kwargs),
+        prompt="prompt",
+        prompt_embeds=[torch.zeros(1, 1, 1)],
+    )
+
+
+def test_ltx23_builtin_overlay_target_is_hf_repo():
+    target = resolve_model_overlay_target("Lightricks/LTX-2.3")
+    assert target is not None
+
+    source_model_id, overlay_spec = target
+    assert source_model_id == "Lightricks/LTX-2.3"
+    assert str(overlay_spec["overlay_repo_id"]) == "MickJ/LTX-2.3-overlay"
+    assert str(overlay_spec["overlay_revision"]) == "main"
+    assert str(overlay_spec["bundled_overlay_subdir"]) == "ltx_2_3"
+
+
+def test_ltx23_model_info_resolves_to_native_pipeline_and_sampling_params():
+    model_info = get_model_info("Lightricks/LTX-2.3", backend="sglang")
+
+    assert model_info is not None
+    assert model_info.pipeline_cls.__name__ == "LTX2Pipeline"
+    assert model_info.sampling_param_cls.__name__ == "LTX23SamplingParams"
+
+
+def test_ltx23_sampling_defaults_use_cuda_generator():
+    sampling_params = SamplingParams.from_pretrained(
+        "Lightricks/LTX-2.3",
+        backend="sglang",
+    )
+
+    assert sampling_params.generator_device == "cuda"
+    assert sampling_params.guidance_scale == 3.0
+    assert sampling_params.num_inference_steps == 30
+
+
+def test_ltx2_sampling_defaults_keep_cpu_generator():
+    sampling_params = SamplingParams.from_pretrained(
+        "Lightricks/LTX-2",
+        backend="sglang",
+    )
+
+    assert sampling_params.generator_device == "cpu"
+
+
+def test_ltx23_build_request_extra_sets_stage1_guider_defaults():
+    sampling_params = SamplingParams.from_pretrained(
+        "Lightricks/LTX-2.3",
+        backend="sglang",
+    )
+
+    assert sampling_params.build_request_extra()["ltx2_stage1_guider_params"] == {
+        "video_cfg_scale": 3.0,
+        "video_stg_scale": 1.0,
+        "video_rescale_scale": 0.7,
+        "video_modality_scale": 3.0,
+        "video_skip_step": 0,
+        "video_stg_blocks": [28],
+        "audio_cfg_scale": 7.0,
+        "audio_stg_scale": 1.0,
+        "audio_rescale_scale": 0.7,
+        "audio_modality_scale": 3.0,
+        "audio_skip_step": 0,
+        "audio_stg_blocks": [28],
+    }
+
+
+def test_sampling_params_apply_request_extra_populates_req_extra():
+    sampling_params = SamplingParams.from_pretrained(
+        "Lightricks/LTX-2.3",
+        backend="sglang",
+    )
+    req = Req(sampling_params=sampling_params, prompt="prompt")
+
+    sampling_params.apply_request_extra(req)
+
+    assert req.extra["ltx2_stage1_guider_params"]["video_cfg_scale"] == 3.0
+    assert req.extra["ltx2_stage1_guider_params"]["audio_cfg_scale"] == 7.0
+
+
+def test_ltx23_uses_official_sigma_schedule():
+    sigmas = build_official_ltx2_sigmas(30)
+
+    assert len(sigmas) == 30
+    assert sigmas[0] == pytest.approx(1.0)
+    assert sigmas[1] == pytest.approx(0.99495703, abs=1e-6)
+    assert sigmas[-1] == pytest.approx(0.1, abs=1e-6)
+
+
+def test_ltx23_native_variant_uses_explicit_marker_only():
+    assert is_ltx23_native_variant(SimpleNamespace(ltx_variant="ltx_2_3")) is True
+    assert is_ltx23_native_variant(SimpleNamespace(ltx_variant="ltx_2")) is False
+
+
+def test_prepare_ltx2_mu_respects_variant_marker():
+    ltx23_server_args = SimpleNamespace(
+        pipeline_config=SimpleNamespace(
+            vae_config=SimpleNamespace(
+                arch_config=SimpleNamespace(ltx_variant="ltx_2_3")
+            )
+        )
+    )
+    legacy_server_args = SimpleNamespace(
+        pipeline_config=SimpleNamespace(
+            vae_config=SimpleNamespace(
+                arch_config=SimpleNamespace(ltx_variant="ltx_2")
+            ),
+            vae_temporal_compression=8,
+            vae_scale_factor=32,
+        )
+    )
+
+    assert prepare_ltx2_mu(
+        _make_req(num_frames=121, height=512, width=768),
+        ltx23_server_args,
+    ) == ("mu", None)
+
+    key, mu = prepare_ltx2_mu(
+        _make_req(num_frames=121, height=512, width=768),
+        legacy_server_args,
+    )
+    assert key == "mu"
+    assert isinstance(mu, float)
+    assert mu > 0.0
+
+
+def test_ltx23_ti2v_clean_latent_uses_zero_background():
+    latents = torch.arange(24, dtype=torch.float32).view(1, 6, 4)
+    image_latent = torch.full((1, 2, 4), 99.0)
+
+    conditioned, denoise_mask, clean_latent = (
+        LTX2AVDenoisingStage._prepare_ltx2_ti2v_clean_state(
+            latents=latents,
+            image_latent=image_latent,
+            num_img_tokens=2,
+            zero_clean_latent=True,
+        )
+    )
+
+    assert torch.equal(conditioned[:, :2], image_latent)
+    assert torch.equal(clean_latent[:, :2], image_latent)
+    assert torch.equal(clean_latent[:, 2:], torch.zeros_like(clean_latent[:, 2:]))
+    assert torch.equal(denoise_mask[:, :2], torch.zeros_like(denoise_mask[:, :2]))
+    assert torch.equal(denoise_mask[:, 2:], torch.ones_like(denoise_mask[:, 2:]))
+
+
+def test_ltx2_ti2v_clean_latent_keeps_legacy_background_when_requested():
+    latents = torch.arange(24, dtype=torch.float32).view(1, 6, 4)
+    image_latent = torch.full((1, 2, 4), 99.0)
+
+    conditioned, _, clean_latent = LTX2AVDenoisingStage._prepare_ltx2_ti2v_clean_state(
+        latents=latents,
+        image_latent=image_latent,
+        num_img_tokens=2,
+        zero_clean_latent=False,
+    )
+
+    assert torch.equal(conditioned[:, :2], image_latent)
+    assert torch.equal(clean_latent[:, :2], image_latent)
+    assert torch.equal(clean_latent[:, 2:], latents[:, 2:])
+
+
+def test_ltx23_velocity_to_x0_supports_tokenwise_sigma():
+    sample = torch.tensor([[[1.0, 2.0], [3.0, 4.0]]], dtype=torch.float32)
+    velocity = torch.tensor([[[0.5, 0.5], [1.0, 1.0]]], dtype=torch.float32)
+    sigma = torch.tensor([[0.0, 0.5]], dtype=torch.float32)
+
+    denoised = LTX2AVDenoisingStage._ltx2_velocity_to_x0(sample, velocity, sigma)
+
+    expected = torch.tensor([[[1.0, 2.0], [2.5, 3.5]]], dtype=torch.float32)
+    assert torch.allclose(denoised, expected)
+
+
+def test_ltx23_connector_repack_renames_qk_norm_keys():
+    assert (
+        _rename_connector_key(
+            "model.diffusion_model.video_embeddings_connector.transformer_1d_blocks.0.attn1.q_norm.weight"
+        )
+        == "video_connector.transformer_blocks.0.attn1.norm_q.weight"
+    )
+    assert (
+        _rename_connector_key(
+            "model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.1.attn1.k_norm.weight"
+        )
+        == "audio_connector.transformer_blocks.1.attn1.norm_k.weight"
+    )
+
+
+def test_ltx23_transformer_config_forces_sdpa_for_v2a_cross_attention():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        donor_dir = os.path.join(tmpdir, "donor")
+        os.makedirs(os.path.join(donor_dir, "transformer"), exist_ok=True)
+        with open(os.path.join(donor_dir, "transformer", "config.json"), "w") as f:
+            json.dump({"_class_name": "OldClass", "num_layers": 1}, f)
+
+        config = _build_transformer_config(donor_dir)
+
+    assert config["_class_name"] == "LTX2VideoTransformer3DModel"
+    assert config["force_sdpa_v2a_cross_attention"] is True
+
+
+def test_ltx23_vae_config_adds_required_markers():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        auxiliary_dir = os.path.join(tmpdir, "aux")
+        config_donor_dir = os.path.join(tmpdir, "donor")
+        os.makedirs(os.path.join(auxiliary_dir, "vae"), exist_ok=True)
+        os.makedirs(os.path.join(config_donor_dir, "vae"), exist_ok=True)
+
+        with open(os.path.join(auxiliary_dir, "vae", "config.json"), "w") as f:
+            json.dump(
+                {
+                    "_class_name": "AutoencoderKLLTX2Video",
+                    "scaling_factor": 1.0,
+                    "patch_size": 4,
+                    "decoder_causal": False,
+                    "timestep_conditioning": False,
+                    "encoder_spatial_padding_mode": "zeros",
+                    "decoder_spatial_padding_mode": "reflect",
+                },
+                f,
+            )
+        with open(os.path.join(config_donor_dir, "vae", "config.json"), "w") as f:
+            json.dump(
+                {
+                    "vae": {
+                        "decoder_blocks": [["res_x", {"num_layers": 2}]],
+                        "decoder_base_channels": 128,
+                        "patch_size": 4,
+                        "spatial_padding_mode": "zeros",
+                    }
+                },
+                f,
+            )
+
+        config = _build_vae_config(auxiliary_dir, config_donor_dir)
+
+    assert config["ltx_variant"] == "ltx_2_3"
+    assert config["condition_encoder_subdir"] == "ltx23_image_encoder"
+    assert config["video_decoder_variant"] == "ltx_2_3"
+    assert config["video_decoder_config"]["decoder_base_channels"] == 128
+
+
+def test_ltx23_repack_image_encoder_keeps_only_encoder_tensors():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        source_path = os.path.join(tmpdir, "source.safetensors")
+        output_path = os.path.join(tmpdir, "output.safetensors")
+        save_file(
+            {
+                "encoder.conv_in.conv.weight": torch.ones(1),
+                "decoder.conv_in.conv.weight": torch.full((1,), 2.0),
+                "per_channel_statistics.mean-of-means": torch.full((2,), 3.0),
+            },
+            source_path,
+        )
+
+        _repack_ltx23_image_encoder_weights(source_path, output_path)
+
+        with safe_open(output_path, framework="pt") as f:
+            assert sorted(f.keys()) == [
+                "conv_in.conv.weight",
+                "per_channel_statistics.mean-of-means",
+            ]
+
+
+def test_ltx23_repack_video_decoder_keeps_decoder_and_stats():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        auxiliary_path = os.path.join(tmpdir, "aux.safetensors")
+        donor_path = os.path.join(tmpdir, "donor.safetensors")
+        output_path = os.path.join(tmpdir, "output.safetensors")
+        save_file(
+            {
+                "encoder.conv_in.conv.weight": torch.full((1,), 5.0),
+            },
+            auxiliary_path,
+        )
+        save_file(
+            {
+                "decoder.conv_in.conv.weight": torch.ones(1),
+                "per_channel_statistics.mean-of-means": torch.full((2,), 3.0),
+                "per_channel_statistics.std-of-means": torch.full((2,), 4.0),
+            },
+            donor_path,
+        )
+
+        _repack_ltx23_video_decoder_weights(auxiliary_path, donor_path, output_path)
+
+        with safe_open(output_path, framework="pt") as f:
+            assert sorted(f.keys()) == [
+                "decoder.conv_in.conv.weight",
+                "decoder.per_channel_statistics.mean_of_means",
+                "decoder.per_channel_statistics.std_of_means",
+                "encoder.conv_in.conv.weight",
+                "latents_mean",
+                "latents_std",
+            ]
+
+
+def test_ltx23_decode_skips_external_denorm():
+    ltx23_server_args = SimpleNamespace(
+        pipeline_config=SimpleNamespace(
+            vae_config=SimpleNamespace(
+                arch_config=SimpleNamespace(video_decoder_variant="ltx_2_3")
+            )
+        )
+    )
+    legacy_server_args = SimpleNamespace(
+        pipeline_config=SimpleNamespace(
+            vae_config=SimpleNamespace(
+                arch_config=SimpleNamespace(video_decoder_variant="ltx_2")
+            )
+        )
+    )
+
+    assert (
+        LTX2AVDecodingStage._ltx2_should_externally_denorm_video_latents(
+            ltx23_server_args
+        )
+        is False
+    )
+    assert (
+        LTX2AVDecodingStage._ltx2_should_externally_denorm_video_latents(
+            legacy_server_args
+        )
+        is True
+    )
+
+
+def test_ltx2_two_stage_component_auto_resolution_preserves_legacy_candidates(tmp_path):
+    legacy_spatial = tmp_path / "ltx-2-spatial-upscaler-x2-1.0.safetensors"
+    legacy_lora = tmp_path / "ltx-2-19b-distilled-lora-384.safetensors"
+    legacy_spatial.touch()
+    legacy_lora.touch()
+
+    resolved = _resolve_ltx2_two_stage_component_paths(str(tmp_path), {})
+
+    assert resolved["spatial_upsampler"] == str(legacy_spatial)
+    assert resolved["distilled_lora"] == str(legacy_lora)
+
+
+def test_ltx23_two_stage_component_auto_resolution_prefers_23_assets(tmp_path):
+    spatial = tmp_path / "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"
+    lora = tmp_path / "ltx-2.3-22b-distilled-lora-384.safetensors"
+    spatial.touch()
+    lora.touch()
+
+    resolved = _resolve_ltx2_two_stage_component_paths(str(tmp_path), {})
+
+    assert resolved["spatial_upsampler"] == str(spatial)
+    assert resolved["distilled_lora"] == str(lora)