[feat] LTX-2.3 transformer support (config-gated extension of LTX-2)

fastvideo/configs/models/dits/ltx2.py

@@ -22,7 +22,15 @@ class LTX2VideoArchConfig(DiTArchConfig):
     _fsdp_shard_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
     _compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
 
-    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
+    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo).
+    # The ``to_gate_compress`` -> ``to_gate_logits`` rules for the LTX-2.3
+    # gated-attention path are inserted at the front of this dict in
+    # ``__post_init__`` only when ``apply_gated_attention=True``.  Without
+    # that flag the target model has no ``to_gate_logits`` slot, *and* the
+    # same-named ``to_gate_compress`` already lives on the LTX-2.0 VSA-QAT
+    # gate path (plus it is in the default ``lora_target_modules`` list).
+    # Applying the rename unconditionally silently breaks LTX-2.0 VSA
+    # checkpoints and default-target LoRAs.
     param_names_mapping: dict = field(
         default_factory=lambda: {
             r"^model\.diffusion_model\.(.*)$": r"model.\1",
@@ -44,6 +52,9 @@ class LTX2VideoArchConfig(DiTArchConfig):
     attention_type: str = "default"
     rope_type: str = "split"
     double_precision_rope: bool = True
+    # LTX-2.3 gated extensions. All default OFF == LTX-2.0 behavior.
+    cross_attention_adaln: bool = False
+    caption_proj_before_connector: bool = False
 
     positional_embedding_theta: float = 10000.0
     positional_embedding_max_pos: list[int] = field(default_factory=lambda: [20, 2048, 2048])
@@ -64,6 +75,27 @@ class LTX2VideoArchConfig(DiTArchConfig):
     audio_cross_attention_dim: int = 2048
     audio_positional_embedding_max_pos: list[int] = field(default_factory=lambda: [20])
     av_ca_timestep_scale_multiplier: int = 1
+    # LTX-2.3 gated self-attention (distinct from the VSA-QAT to_gate_compress
+    # gate). Default OFF == LTX-2.0 behavior.
+    apply_gated_attention: bool = False
+
+    # Text connector/feature extractor compatibility fields carried in some
+    # transformer configs (used by the LTX-2.3 text stack). Defaults match
+    # the LTX-2.0 connector layout.
+    caption_projection_first_linear: bool = True
+    caption_proj_input_norm: bool = True
+    caption_projection_second_linear: bool = True
+    connector_num_attention_heads: int = 30
+    connector_attention_head_dim: int = 128
+    connector_num_layers: int = 2
+    audio_connector_num_attention_heads: int = 30
+    audio_connector_attention_head_dim: int = 128
+    audio_connector_num_layers: int = 2
+
+    # STG perturbation block index differs across model versions.
+    # LTX-2.0 defaults to block 29; LTX-2.3 (caption_proj_before_connector)
+    # uses block 28. ``None`` resolves in __post_init__.
+    stg_block_idx: int | None = None
 
     def __post_init__(self):
         super().__post_init__()
@@ -72,6 +104,29 @@ def __post_init__(self):
             self.in_channels = self.num_channels_latents * patch_volume
         if self.out_channels is None:
             self.out_channels = self.in_channels
+        if self.stg_block_idx is None:
+            self.stg_block_idx = 28 if self.caption_proj_before_connector else 29
+
+        # LTX-2.3 stores the gated-attention weight under ``to_gate_compress``
+        # upstream; FastVideo's internal name is ``to_gate_logits``.  Only
+        # enable the rename when the gated path is actually configured: the
+        # LTX-2.0 attention module's own ``to_gate_compress`` parameter
+        # (created when the backend is ``VIDEO_SPARSE_ATTN``) and the default
+        # ``to_gate_compress`` LoRA target both share the upstream name, so
+        # an unconditional rename would silently retarget them.  Inserted at
+        # the front so first-match-wins matching fires the rename before the
+        # generic prefix-strip rules.
+        if self.apply_gated_attention:
+            gate_rules = {
+                r"^model\.diffusion_model\.(.*)\.to_gate_compress\.(.*)$": r"model.\1.to_gate_logits.\2",
+                r"^diffusion_model\.(.*)\.to_gate_compress\.(.*)$": r"model.\1.to_gate_logits.\2",
+                r"^model\.(.*)\.to_gate_compress\.(.*)$": r"model.\1.to_gate_logits.\2",
+                r"^(.*)\.to_gate_compress\.(.*)$": r"model.\1.to_gate_logits.\2",
+            }
+            self.param_names_mapping = {
+                **gate_rules,
+                **self.param_names_mapping,
+            }
 
 
 @dataclass

fastvideo/configs/models/encoders/gemma.py

@@ -8,7 +8,13 @@
 
 
 def _is_feature_extractor_linear(n: str, m) -> bool:
-    return n.endswith("feature_extractor_linear")
+    # LTX-2.3 (caption_proj_before_connector) introduces separate
+    # video/audio feature extractor linears; keep the LTX-2.0 name too.
+    return (
+        n.endswith("feature_extractor_linear")
+        or n.endswith("video_feature_extractor_linear")
+        or n.endswith("audio_feature_extractor_linear")
+    )
 
 
 def _is_embeddings(n: str, m) -> bool:
@@ -35,14 +41,26 @@ class LTX2GemmaArchConfig(TextEncoderArchConfig):
 
     feature_extractor_in_features: int = 3840 * 49
     feature_extractor_out_features: int = 3840
+    # LTX-2.3 text-stack connector fields (default OFF == LTX-2.0 behavior).
+    video_feature_extractor_out_features: int | None = None
+    audio_feature_extractor_out_features: int | None = None
+    caption_proj_before_connector: bool = False
+    caption_projection_first_linear: bool = True
+    caption_proj_input_norm: bool = True
+    caption_projection_second_linear: bool = True
 
     connector_num_attention_heads: int = 30
     connector_attention_head_dim: int = 128
     connector_num_layers: int = 2
+    # Separate audio connector geometry (None falls back to the video values).
+    audio_connector_num_attention_heads: int | None = None
+    audio_connector_attention_head_dim: int | None = None
+    audio_connector_num_layers: int | None = None
     connector_positional_embedding_theta: float = 10000.0
     connector_positional_embedding_max_pos: list[int] = field(default_factory=lambda: [4096])
     connector_rope_type: str = "split"
     connector_double_precision_rope: bool = False
+    connector_apply_gated_attention: bool = False
     connector_num_learnable_registers: int | None = 128
 
     _fsdp_shard_conditions: list = field(