Add LTX-2.3 model architecture with prompt-conditioned adaptive layer normalization (adaln) support. Introduce gating mechanisms in attention modules and update transformer configurations to accommodate new parameters. Refactor video and audio processing to utilize adaptive normalization, improving model flexibility and performance. Update weight loading and initialization logic to support dynamic block structures in the decoder.

mlx_video/generate.py

@@ -344,6 +344,7 @@ def denoise_distilled(
                 context=text_embeddings,
                 context_mask=None,
                 enabled=True,
+                sigma=mx.full((b,), sigma, dtype=dtype),
             )
 
             audio_modality = None
@@ -359,6 +360,7 @@ def denoise_distilled(
                     context=audio_embeddings,
                     context_mask=None,
                     enabled=True,
+                    sigma=mx.full((ab,), sigma, dtype=dtype),
                 )
 
             velocity, audio_velocity = transformer(video=video_modality, audio=audio_modality)
@@ -493,6 +495,8 @@ def denoise_dev(
             else:
                 timesteps = mx.full((b, num_tokens), sigma, dtype=dtype)
 
+            sigma_array = mx.full((b,), sigma, dtype=dtype)
+
             # Positive conditioning pass
             video_modality_pos = Modality(
                 latent=latents_flat,
@@ -502,6 +506,7 @@ def denoise_dev(
                 context_mask=None,
                 enabled=True,
                 positional_embeddings=precomputed_rope,
+                sigma=sigma_array,
             )
             velocity_pos, _ = transformer(video=video_modality_pos, audio=None)
 
@@ -523,6 +528,7 @@ def denoise_dev(
                     context_mask=None,
                     enabled=True,
                     positional_embeddings=precomputed_rope,
+                    sigma=sigma_array,
                 )
                 velocity_neg, _ = transformer(video=video_modality_neg, audio=None)
 
@@ -957,10 +963,18 @@ def generate_video(
 
     mx.random.seed(seed)
 
+    # Read transformer config to detect model version
+    import json
+    transformer_config_path = model_path / "transformer" / "config.json"
+    has_prompt_adaln = False
+    if transformer_config_path.exists():
+        with open(transformer_config_path) as f:
+            has_prompt_adaln = json.load(f).get("has_prompt_adaln", False)
+
     # Load text encoder
     with console.status("[blue]📝 Loading text encoder...[/]", spinner="dots"):
         from mlx_video.models.ltx.text_encoder import LTX2TextEncoder
-        text_encoder = LTX2TextEncoder()
+        text_encoder = LTX2TextEncoder(has_prompt_adaln=has_prompt_adaln)
         text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path)
         mx.eval(text_encoder.parameters())
     console.print("[green]✓[/] Text encoder loaded")
@@ -1084,7 +1098,10 @@ def generate_video(
 
         # Upsample latents
         with console.status("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"):
-            upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors'))
+            upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors"))
+            if not upscaler_files:
+                raise FileNotFoundError(f"No spatial upscaler found in {model_path}")
+            upsampler = load_upsampler(str(upscaler_files[0]))
             mx.eval(upsampler.parameters())
 
             vae_decoder = VideoDecoder.from_pretrained(str(model_path / "vae" / "decoder"))

mlx_video/models/ltx/attention.py

@@ -67,17 +67,8 @@ class Attention(nn.Module):
         dim_head: int = 64,
         norm_eps: float = 1e-6,
         rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
+        has_gate_logits: bool = False,
     ):
-        """Initialize attention module.
-
-        Args:
-            query_dim: Dimension of query input
-            context_dim: Dimension of context (key/value) input. If None, same as query_dim
-            heads: Number of attention heads
-            dim_head: Dimension per head
-            norm_eps: Epsilon for RMS normalization
-            rope_type: Type of rotary position embedding
-        """
         super().__init__()
 
         self.rope_type = rope_type
@@ -99,6 +90,10 @@ class Attention(nn.Module):
         # Output projection
         self.to_out = nn.Linear(inner_dim, query_dim, bias=True)
 
+        # Per-head gating (LTX-2.3)
+        if has_gate_logits:
+            self.to_gate_logits = nn.Linear(query_dim, heads, bias=True)
+
     def __call__(
         self,
         x: mx.array,
@@ -119,6 +114,11 @@ class Attention(nn.Module):
         Returns:
             Attention output of shape (B, seq_len, query_dim)
         """
+        # Compute per-head gate early (from original input)
+        gate = None
+        if hasattr(self, "to_gate_logits"):
+            gate = 2.0 * mx.sigmoid(self.to_gate_logits(x))  # (B, seq, heads)
+
         # Compute Q, K, V
         q = self.to_q(x)
         context = x if context is None else context
@@ -138,5 +138,12 @@ class Attention(nn.Module):
         # Compute attention
         out = scaled_dot_product_attention(q, k, v, self.heads, mask)
 
+        # Apply per-head gating
+        if gate is not None:
+            b, seq_len, _ = out.shape
+            out = mx.reshape(out, (b, seq_len, self.heads, self.dim_head))
+            out = out * gate[..., None]
+            out = mx.reshape(out, (b, seq_len, -1))
+
         # Project output
         return self.to_out(out)

mlx_video/models/ltx/config.py

@@ -131,6 +131,12 @@ class LTXModelConfig(BaseModelConfig):
     # Attention type
     attention_type: AttentionType = AttentionType.DEFAULT
 
+    # LTX-2.3: prompt-conditioned adaptive layer norm
+    # Controls: gate_logits in attention, 9-param scale_shift_table,
+    # prompt_adaln_single, per-block prompt_scale_shift_table,
+    # removal of caption_projection
+    has_prompt_adaln: bool = False
+
     # VAE config
     vae_config: Optional[VideoVAEConfig] = None
 

mlx_video/models/ltx/ltx.py

@@ -26,7 +26,7 @@ class TransformerArgsPreprocessor:
         self,
         patchify_proj: nn.Linear,
         adaln: AdaLayerNormSingle,
-        caption_projection: PixArtAlphaTextProjection,
+        caption_projection: Optional[PixArtAlphaTextProjection],
         inner_dim: int,
         max_pos: List[int],
         num_attention_heads: int,
@@ -35,10 +35,12 @@ class TransformerArgsPreprocessor:
         positional_embedding_theta: float,
         rope_type: LTXRopeType,
         double_precision_rope: bool = False,
+        prompt_adaln: Optional[AdaLayerNormSingle] = None,
     ):
         self.patchify_proj = patchify_proj
         self.adaln = adaln
         self.caption_projection = caption_projection
+        self.prompt_adaln = prompt_adaln
         self.inner_dim = inner_dim
         self.max_pos = max_pos
         self.num_attention_heads = num_attention_heads
@@ -64,6 +66,19 @@ class TransformerArgsPreprocessor:
 
         return timestep_emb, embedded_timestep
 
+    def _prepare_timestep_with_adaln(
+        self,
+        adaln: AdaLayerNormSingle,
+        timestep: mx.array,
+        batch_size: int,
+        hidden_dtype: mx.Dtype = None,
+    ) -> Tuple[mx.array, mx.array]:
+        timestep = timestep * self.timestep_scale_multiplier
+        timestep_emb, embedded_timestep = adaln(timestep.reshape(-1), hidden_dtype=hidden_dtype)
+        timestep_emb = mx.reshape(timestep_emb, (batch_size, -1, timestep_emb.shape[-1]))
+        embedded_timestep = mx.reshape(embedded_timestep, (batch_size, -1, embedded_timestep.shape[-1]))
+        return timestep_emb, embedded_timestep
+
     def _prepare_context(
         self,
         context: mx.array,
@@ -72,8 +87,7 @@ class TransformerArgsPreprocessor:
     ) -> Tuple[mx.array, Optional[mx.array]]:
         batch_size = x.shape[0]
 
-        # Context is already processed through embeddings connector in text encoder
+        if self.caption_projection is not None:
-        # Here we just apply the caption projection
             context = self.caption_projection(context)
         context = mx.reshape(context, (batch_size, -1, x.shape[-1]))
         return context, attention_mask
@@ -134,6 +148,14 @@ class TransformerArgsPreprocessor:
                 num_attention_heads=self.num_attention_heads,
             )
 
+        # Prompt-conditioned timestep (LTX-2.3) - uses raw sigma, not per-token timesteps
+        prompt_timestep = None
+        prompt_embedded_timestep = None
+        if self.prompt_adaln is not None and modality.sigma is not None:
+            prompt_timestep, prompt_embedded_timestep = self._prepare_timestep_with_adaln(
+                self.prompt_adaln, modality.sigma, x.shape[0], hidden_dtype=x.dtype,
+            )
+
         return TransformerArgs(
             x=x,
             context=context,
@@ -145,6 +167,8 @@ class TransformerArgsPreprocessor:
             cross_scale_shift_timestep=None,
             cross_gate_timestep=None,
             enabled=modality.enabled,
+            prompt_timesteps=prompt_timestep,
+            prompt_embedded_timestep=prompt_embedded_timestep,
         )
 
 
@@ -154,7 +178,7 @@ class MultiModalTransformerArgsPreprocessor:
         self,
         patchify_proj: nn.Linear,
         adaln: AdaLayerNormSingle,
-        caption_projection: PixArtAlphaTextProjection,
+        caption_projection: Optional[PixArtAlphaTextProjection],
         cross_scale_shift_adaln: AdaLayerNormSingle,
         cross_gate_adaln: AdaLayerNormSingle,
         inner_dim: int,
@@ -168,6 +192,7 @@ class MultiModalTransformerArgsPreprocessor:
         rope_type: LTXRopeType,
         av_ca_timestep_scale_multiplier: int,
         double_precision_rope: bool = False,
+        prompt_adaln: Optional[AdaLayerNormSingle] = None,
     ):
         self.simple_preprocessor = TransformerArgsPreprocessor(
             patchify_proj=patchify_proj,
@@ -181,6 +206,7 @@ class MultiModalTransformerArgsPreprocessor:
             positional_embedding_theta=positional_embedding_theta,
             rope_type=rope_type,
             double_precision_rope=double_precision_rope,
+            prompt_adaln=prompt_adaln,
         )
         self.cross_scale_shift_adaln = cross_scale_shift_adaln
         self.cross_gate_adaln = cross_gate_adaln
@@ -280,7 +306,13 @@ class LTXModel(nn.Module):
 
     def _init_video(self, config: LTXModelConfig) -> None:
         self.patchify_proj = nn.Linear(config.in_channels, self.inner_dim, bias=True)
-        self.adaln_single = AdaLayerNormSingle(self.inner_dim)
+
+        adaln_coefficient = 9 if config.has_prompt_adaln else 6
+        self.adaln_single = AdaLayerNormSingle(self.inner_dim, embedding_coefficient=adaln_coefficient)
+
+        if config.has_prompt_adaln:
+            self.prompt_adaln_single = AdaLayerNormSingle(self.inner_dim, embedding_coefficient=2)
+        else:
             self.caption_projection = PixArtAlphaTextProjection(
                 in_features=config.caption_channels,
                 hidden_size=self.inner_dim,
@@ -292,9 +324,13 @@ class LTXModel(nn.Module):
 
     def _init_audio(self, config: LTXModelConfig) -> None:
         self.audio_patchify_proj = nn.Linear(config.audio_in_channels, self.audio_inner_dim, bias=True)
-        self.audio_adaln_single = AdaLayerNormSingle(self.audio_inner_dim)
 
-        # Audio caption projection: receives pre-processed embeddings from text encoder's audio_embeddings_connector
+        audio_adaln_coefficient = 9 if config.has_prompt_adaln else 6
+        self.audio_adaln_single = AdaLayerNormSingle(self.audio_inner_dim, embedding_coefficient=audio_adaln_coefficient)
+
+        if config.has_prompt_adaln:
+            self.audio_prompt_adaln_single = AdaLayerNormSingle(self.audio_inner_dim, embedding_coefficient=2)
+        else:
             self.audio_caption_projection = PixArtAlphaTextProjection(
                 in_features=config.audio_caption_channels,
                 hidden_size=self.audio_inner_dim,
@@ -331,7 +367,7 @@ class LTXModel(nn.Module):
             self.video_args_preprocessor = MultiModalTransformerArgsPreprocessor(
                 patchify_proj=self.patchify_proj,
                 adaln=self.adaln_single,
-                caption_projection=self.caption_projection,
+                caption_projection=getattr(self, "caption_projection", None),
                 cross_scale_shift_adaln=self.av_ca_video_scale_shift_adaln_single,
                 cross_gate_adaln=self.av_ca_a2v_gate_adaln_single,
                 inner_dim=self.inner_dim,
@@ -345,11 +381,12 @@ class LTXModel(nn.Module):
                 rope_type=config.rope_type,
                 av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier,
                 double_precision_rope=config.double_precision_rope,
+                prompt_adaln=getattr(self, "prompt_adaln_single", None),
             )
             self.audio_args_preprocessor = MultiModalTransformerArgsPreprocessor(
                 patchify_proj=self.audio_patchify_proj,
                 adaln=self.audio_adaln_single,
-                caption_projection=self.audio_caption_projection,
+                caption_projection=getattr(self, "audio_caption_projection", None),
                 cross_scale_shift_adaln=self.av_ca_audio_scale_shift_adaln_single,
                 cross_gate_adaln=self.av_ca_v2a_gate_adaln_single,
                 inner_dim=self.audio_inner_dim,
@@ -363,12 +400,13 @@ class LTXModel(nn.Module):
                 rope_type=config.rope_type,
                 av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier,
                 double_precision_rope=config.double_precision_rope,
+                prompt_adaln=getattr(self, "audio_prompt_adaln_single", None),
             )
         elif config.model_type.is_video_enabled():
             self.video_args_preprocessor = TransformerArgsPreprocessor(
                 patchify_proj=self.patchify_proj,
                 adaln=self.adaln_single,
-                caption_projection=self.caption_projection,
+                caption_projection=getattr(self, "caption_projection", None),
                 inner_dim=self.inner_dim,
                 max_pos=config.positional_embedding_max_pos,
                 num_attention_heads=self.num_attention_heads,
@@ -377,12 +415,13 @@ class LTXModel(nn.Module):
                 positional_embedding_theta=config.positional_embedding_theta,
                 rope_type=config.rope_type,
                 double_precision_rope=config.double_precision_rope,
+                prompt_adaln=getattr(self, "prompt_adaln_single", None),
             )
         elif config.model_type.is_audio_enabled():
             self.audio_args_preprocessor = TransformerArgsPreprocessor(
                 patchify_proj=self.audio_patchify_proj,
                 adaln=self.audio_adaln_single,
-                caption_projection=self.audio_caption_projection,
+                caption_projection=getattr(self, "audio_caption_projection", None),
                 inner_dim=self.audio_inner_dim,
                 max_pos=config.audio_positional_embedding_max_pos,
                 num_attention_heads=self.audio_num_attention_heads,
@@ -391,13 +430,13 @@ class LTXModel(nn.Module):
                 positional_embedding_theta=config.positional_embedding_theta,
                 rope_type=config.rope_type,
                 double_precision_rope=config.double_precision_rope,
+                prompt_adaln=getattr(self, "audio_prompt_adaln_single", None),
             )
 
     def _init_transformer_blocks(self, config: LTXModelConfig) -> None:
         video_config = config.get_video_config()
         audio_config = config.get_audio_config()
 
-
         self.transformer_blocks = {
             idx: BasicAVTransformerBlock(
                 idx=idx,
@@ -405,6 +444,7 @@ class LTXModel(nn.Module):
                 audio=audio_config,
                 rope_type=config.rope_type,
                 norm_eps=config.norm_eps,
+                has_prompt_adaln=config.has_prompt_adaln,
             )
             for idx in range(config.num_layers)
         }

mlx_video/models/ltx/text_encoder.py

@@ -208,6 +208,7 @@ class ConnectorAttention(nn.Module):
         dim: int = 3840,
         num_heads: int = 30,
         head_dim: int = 128,
+        has_gate_logits: bool = False,
     ):
         super().__init__()
         self.num_heads = num_heads
@@ -218,13 +219,14 @@ class ConnectorAttention(nn.Module):
         self.to_q = nn.Linear(dim, inner_dim, bias=True)
         self.to_k = nn.Linear(dim, inner_dim, bias=True)
         self.to_v = nn.Linear(dim, inner_dim, bias=True)
-        # Direct attribute for MLX parameter tracking (not a list)
         self.to_out = nn.Linear(inner_dim, dim, bias=True)
 
-        # Standard RMSNorm (not Gemma-style) on full inner_dim
         self.q_norm = nn.RMSNorm(inner_dim, eps=1e-6)
         self.k_norm = nn.RMSNorm(inner_dim, eps=1e-6)
 
+        if has_gate_logits:
+            self.to_gate_logits = nn.Linear(dim, num_heads, bias=True)
+
     def __call__(
         self,
         x: mx.array,
@@ -233,12 +235,17 @@ class ConnectorAttention(nn.Module):
     ) -> mx.array:
         batch_size, seq_len, _ = x.shape
 
+        # Compute per-head gate early (from original input)
+        gate = None
+        if hasattr(self, "to_gate_logits"):
+            gate = 2.0 * mx.sigmoid(self.to_gate_logits(x))  # (B, seq, heads)
+
         # Project to Q, K, V
-        q = self.to_q(x)  # (B, seq, inner_dim)
+        q = self.to_q(x)
         k = self.to_k(x)
         v = self.to_v(x)
 
-        # QK normalization on full inner_dim BEFORE reshape (matches PyTorch)
+        # QK normalization on full inner_dim BEFORE reshape
         q = self.q_norm(q)
         k = self.k_norm(k)
 
@@ -248,15 +255,18 @@ class ConnectorAttention(nn.Module):
         v = mx.reshape(v, (batch_size, seq_len, self.num_heads, self.head_dim)).transpose(0, 2, 1, 3)
 
         if pe is not None:
-            # pe: tuple of (cos, sin) each with shape (1, num_heads, seq_len, head_dim//2)
-            # Apply SPLIT RoPE: operates on first half of head dimensions
             q = self._apply_split_rope(q, pe[0], pe[1])
             k = self._apply_split_rope(k, pe[0], pe[1])
 
-        # No mask needed for connector - after register replacement, all positions are valid
         out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=None)
         out = out.transpose(0, 2, 1, 3).reshape(batch_size, seq_len, -1)
 
+        # Apply per-head gating
+        if gate is not None:
+            out = mx.reshape(out, (batch_size, seq_len, self.num_heads, self.head_dim))
+            out = out * gate[..., None]
+            out = mx.reshape(out, (batch_size, seq_len, -1))
+
         return self.to_out(out)
 
     def _apply_split_rope(
@@ -326,9 +336,9 @@ class ConnectorFeedForward(nn.Module):
 
 class ConnectorTransformerBlock(nn.Module):
 
-    def __init__(self, dim: int = 3840, num_heads: int = 30, head_dim: int = 128):
+    def __init__(self, dim: int = 3840, num_heads: int = 30, head_dim: int = 128, has_gate_logits: bool = False):
         super().__init__()
-        self.attn1 = ConnectorAttention(dim, num_heads, head_dim)
+        self.attn1 = ConnectorAttention(dim, num_heads, head_dim, has_gate_logits=has_gate_logits)
         self.ff = ConnectorFeedForward(dim)
 
     def __call__(
@@ -367,6 +377,7 @@ class Embeddings1DConnector(nn.Module):
         num_learnable_registers: int = 128,
         positional_embedding_theta: float = 10000.0,
         positional_embedding_max_pos: list = None,
+        has_gate_logits: bool = False,
     ):
         super().__init__()
         self.dim = dim
@@ -376,9 +387,8 @@ class Embeddings1DConnector(nn.Module):
         self.positional_embedding_theta = positional_embedding_theta
         self.positional_embedding_max_pos = positional_embedding_max_pos or [4096]
 
-        # Use dict with int keys for MLX to track parameters (lists are not tracked)
         self.transformer_1d_blocks = {
-            i: ConnectorTransformerBlock(dim, num_heads, head_dim)
+            i: ConnectorTransformerBlock(dim, num_heads, head_dim, has_gate_logits=has_gate_logits)
             for i in range(num_layers)
         }
 
@@ -572,16 +582,100 @@ def norm_and_concat_hidden_states(
     return normed
 
 
-class GemmaFeaturesExtractor(nn.Module):
+def norm_and_concat_per_token_rms(
+    encoded_text: mx.array,
+    attention_mask: mx.array,
+) -> mx.array:
+    """Per-token RMSNorm normalization for V2 feature extraction (LTX-2.3).
 
-    def __init__(self, input_dim: int = 188160, output_dim: int = 3840):
+    Args:
+        encoded_text: (B, T, D, L) stacked hidden states
+        attention_mask: (B, T) binary mask (1=valid, 0=padding)
+
+    Returns:
+        (B, T, D*L) normalized tensor with padding zeroed out.
+    """
+    b, t, d, num_layers = encoded_text.shape
+    dtype = encoded_text.dtype
+
+    # Per-token RMSNorm across hidden dimension: variance = mean(x^2) over dim D
+    variance = mx.mean(encoded_text.astype(mx.float32) ** 2, axis=2, keepdims=True)  # (B, T, 1, L)
+    normed = encoded_text.astype(mx.float32) * mx.rsqrt(variance + 1e-6)
+    normed = normed.astype(dtype)
+
+    # Flatten layers: (B, T, D*L)
+    normed = mx.reshape(normed, (b, t, d * num_layers))
+
+    # Zero out padded positions
+    mask_3d = attention_mask[:, :, None].astype(mx.bool_)  # (B, T, 1)
+    normed = mx.where(mask_3d, normed, mx.zeros_like(normed))
+
+    return normed
+
+
+def _rescale_norm(x: mx.array, target_dim: int, source_dim: int) -> mx.array:
+    """Rescale normalization: x * sqrt(target_dim / source_dim)."""
+    return x * math.sqrt(target_dim / source_dim)
+
+
+class GemmaFeaturesExtractor(nn.Module):
+    """V1 feature extractor (LTX-2): 8 * (x - mean) / range normalization."""
+
+    def __init__(self, input_dim: int = 188160, output_dim: int = 3840, bias: bool = False):
         super().__init__()
-        self.aggregate_embed = nn.Linear(input_dim, output_dim, bias=False)
+        self.aggregate_embed = nn.Linear(input_dim, output_dim, bias=bias)
 
     def __call__(self, x: mx.array) -> mx.array:
         return self.aggregate_embed(x)
 
 
+class GemmaFeaturesExtractorV2(nn.Module):
+    """V2 feature extractor (LTX-2.3): per-token RMSNorm + rescale normalization."""
+
+    def __init__(
+        self,
+        flat_dim: int,
+        embedding_dim: int,
+        video_output_dim: int,
+        audio_output_dim: int,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.embedding_dim = embedding_dim  # Gemma hidden_dim (3840), used for rescale
+        self.video_aggregate_embed = nn.Linear(flat_dim, video_output_dim, bias=bias)
+        self.audio_aggregate_embed = nn.Linear(flat_dim, audio_output_dim, bias=bias)
+
+    def __call__(
+        self,
+        hidden_states: List[mx.array],
+        attention_mask: mx.array,
+        mode: str = "video",
+    ) -> mx.array:
+        """Extract features with per-token RMSNorm + rescale.
+
+        Args:
+            hidden_states: List of hidden states from all Gemma layers
+            attention_mask: Binary attention mask (B, T)
+            mode: "video" or "audio" to select which aggregate embed to use
+
+        Returns:
+            Projected features
+        """
+        # Stack hidden states: (B, T, D, L)
+        encoded = mx.stack(hidden_states, axis=-1)
+
+        # Per-token RMSNorm + flatten
+        normed = norm_and_concat_per_token_rms(encoded, attention_mask)
+        normed = normed.astype(encoded.dtype)
+
+        if mode == "video":
+            target_dim = self.video_aggregate_embed.weight.shape[0]
+            return self.video_aggregate_embed(_rescale_norm(normed, target_dim, self.embedding_dim))
+        else:
+            target_dim = self.audio_aggregate_embed.weight.shape[0]
+            return self.audio_aggregate_embed(_rescale_norm(normed, target_dim, self.embedding_dim))
+
+
 
 
 
@@ -603,38 +697,53 @@ class LTX2TextEncoder(nn.Module):
         hidden_dim: int = 3840,
         audio_dim: int = 2048,
         num_layers: int = 49,  # 48 transformer layers + 1 embedding
+        has_prompt_adaln: bool = False,
     ):
         super().__init__()
         self.hidden_dim = hidden_dim
         self.audio_dim = audio_dim
         self.num_layers = num_layers
+        self.has_prompt_adaln = has_prompt_adaln
         self.language_model = None
 
-        # Feature extractor: 3840*49 -> 3840
+        feature_input_dim = hidden_dim * num_layers
-        self.feature_extractor = GemmaFeaturesExtractor(
+
-            input_dim=hidden_dim * num_layers,
+        if has_prompt_adaln:
-            output_dim=hidden_dim,
+            # LTX-2.3: V2 feature extractor with per-token RMSNorm + rescale
+            video_output_dim = 4096
+            audio_output_dim = 2048
+            self.feature_extractor_v2 = GemmaFeaturesExtractorV2(
+                flat_dim=feature_input_dim,       # 3840 * 49 = 188160 (concatenated)
+                embedding_dim=hidden_dim,          # 3840 (Gemma hidden_dim, for rescale)
+                video_output_dim=video_output_dim,
+                audio_output_dim=audio_output_dim,
+                bias=True,
             )
 
-        # Video embeddings connector: 2-layer transformer
+            # Deeper connectors with matching dims and gate_logits
             self.video_embeddings_connector = Embeddings1DConnector(
-            dim=hidden_dim,
+                dim=video_output_dim, num_heads=32, head_dim=128,
-            num_heads=30,
+                num_layers=8, num_learnable_registers=128,
-            head_dim=128,
+                positional_embedding_max_pos=[4096], has_gate_logits=True,
-            num_layers=2,
-            num_learnable_registers=128,
-            positional_embedding_max_pos=[4096],  # Match PyTorch
             )
-
-        # Audio embeddings connector: separate 2-layer transformer (same architecture as video)
-        # Both connectors process the feature extractor output independently
             self.audio_embeddings_connector = Embeddings1DConnector(
-            dim=hidden_dim,
+                dim=audio_output_dim, num_heads=32, head_dim=64,
-            num_heads=30,
+                num_layers=8, num_learnable_registers=128,
-            head_dim=128,
+                positional_embedding_max_pos=[4096], has_gate_logits=True,
-            num_layers=2,
+            )
-            num_learnable_registers=128,
+        else:
-            positional_embedding_max_pos=[4096],  # Match PyTorch
+            # LTX-2: shared feature extractor, 3840-dim connectors
+            self.feature_extractor = GemmaFeaturesExtractor(feature_input_dim, hidden_dim)
+
+            self.video_embeddings_connector = Embeddings1DConnector(
+                dim=hidden_dim, num_heads=30, head_dim=128,
+                num_layers=2, num_learnable_registers=128,
+                positional_embedding_max_pos=[4096],
+            )
+            self.audio_embeddings_connector = Embeddings1DConnector(
+                dim=hidden_dim, num_heads=30, head_dim=128,
+                num_layers=2, num_learnable_registers=128,
+                positional_embedding_max_pos=[4096],
             )
 
         self.processor = None
@@ -669,81 +778,9 @@ class LTX2TextEncoder(nn.Module):
                 transformer_weights = mx.load(str(transformer_files[0]))
 
         if transformer_weights:
-            # Load feature extractor (aggregate_embed)
+            self._load_feature_extractors(transformer_weights, is_reformatted)
-            # Reformatted key: "aggregate_embed.weight"
+            self._load_connector("video_embeddings_connector", transformer_weights, is_reformatted)
-            # Monolithic key: "text_embedding_projection.aggregate_embed.weight"
+            self._load_connector("audio_embeddings_connector", transformer_weights, is_reformatted)
-            agg_key = "aggregate_embed.weight" if is_reformatted else "text_embedding_projection.aggregate_embed.weight"
-            if agg_key in transformer_weights:
-                self.feature_extractor.aggregate_embed.weight = transformer_weights[agg_key]
-
-            # Load video_embeddings_connector weights
-            connector_weights = {}
-            if is_reformatted:
-                # Reformatted: keys are already sanitized with "video_embeddings_connector." prefix
-                for key, value in transformer_weights.items():
-                    if key.startswith("video_embeddings_connector."):
-                        new_key = key.replace("video_embeddings_connector.", "")
-                        connector_weights[new_key] = value
-            else:
-                # Monolithic: keys have "model.diffusion_model.video_embeddings_connector." prefix
-                for key, value in transformer_weights.items():
-                    if key.startswith("model.diffusion_model.video_embeddings_connector."):
-                        new_key = key.replace("model.diffusion_model.video_embeddings_connector.", "")
-                        connector_weights[new_key] = value
-
-            if connector_weights:
-                # Map weight names to our structure (only needed for monolithic/raw PyTorch keys)
-                mapped_weights = {}
-                for key, value in connector_weights.items():
-                    new_key = key
-                    if not is_reformatted:
-                        # Map ff.net.0.proj -> ff.proj_in (GEGLU projection)
-                        new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.")
-                        # Map ff.net.2 -> ff.proj_out (output Linear)
-                        new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.")
-                        # Map to_out.0 -> to_out (Sequential -> direct)
-                        new_key = new_key.replace(".to_out.0.", ".to_out.")
-                    mapped_weights[new_key] = value
-
-                self.video_embeddings_connector.load_weights(
-                    list(mapped_weights.items()), strict=False
-                )
-
-                # Manually load learnable_registers (it's a plain mx.array, not a parameter)
-                if "learnable_registers" in connector_weights:
-                    self.video_embeddings_connector.learnable_registers = connector_weights["learnable_registers"]
-
-            # Load audio_embeddings_connector weights (same structure as video connector)
-            audio_connector_weights = {}
-            if is_reformatted:
-                for key, value in transformer_weights.items():
-                    if key.startswith("audio_embeddings_connector."):
-                        new_key = key.replace("audio_embeddings_connector.", "")
-                        audio_connector_weights[new_key] = value
-            else:
-                for key, value in transformer_weights.items():
-                    if key.startswith("model.diffusion_model.audio_embeddings_connector."):
-                        new_key = key.replace("model.diffusion_model.audio_embeddings_connector.", "")
-                        audio_connector_weights[new_key] = value
-
-            if audio_connector_weights:
-                # Map weight names to our structure (same as video connector)
-                mapped_audio_weights = {}
-                for key, value in audio_connector_weights.items():
-                    new_key = key
-                    if not is_reformatted:
-                        new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.")
-                        new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.")
-                        new_key = new_key.replace(".to_out.0.", ".to_out.")
-                    mapped_audio_weights[new_key] = value
-
-                self.audio_embeddings_connector.load_weights(
-                    list(mapped_audio_weights.items()), strict=False
-                )
-
-                # Manually load learnable_registers (it's a plain mx.array, not a parameter)
-                if "learnable_registers" in audio_connector_weights:
-                    self.audio_embeddings_connector.learnable_registers = audio_connector_weights["learnable_registers"]
         else:
             print("WARNING: No transformer weights found for text projection connectors. "
                   "Text conditioning will use uninitialized weights!")
@@ -763,6 +800,63 @@ class LTX2TextEncoder(nn.Module):
 
         print("Text encoder loaded successfully")
 
+    def _load_feature_extractors(self, weights: dict, is_reformatted: bool):
+        """Load feature extractor weights for both LTX-2 and LTX-2.3."""
+        if self.has_prompt_adaln:
+            # LTX-2.3: V2 feature extractor with separate video/audio aggregate embeds
+            for attr, prefix in [
+                ("video_aggregate_embed", "video_aggregate_embed"),
+                ("audio_aggregate_embed", "audio_aggregate_embed"),
+            ]:
+                w_key = f"{prefix}.weight"
+                b_key = f"{prefix}.bias"
+                if w_key in weights:
+                    submodule = getattr(self.feature_extractor_v2, attr)
+                    submodule.weight = weights[w_key]
+                    if b_key in weights:
+                        submodule.bias = weights[b_key]
+        else:
+            # LTX-2: single aggregate_embed
+            agg_key = "aggregate_embed.weight" if is_reformatted else "text_embedding_projection.aggregate_embed.weight"
+            if agg_key in weights:
+                self.feature_extractor.aggregate_embed.weight = weights[agg_key]
+
+    def _load_connector(self, name: str, weights: dict, is_reformatted: bool):
+        """Load a connector's weights (video or audio)."""
+        connector = getattr(self, name)
+
+        # Extract connector-specific weights
+        connector_weights = {}
+        if is_reformatted:
+            prefix = f"{name}."
+            for key, value in weights.items():
+                if key.startswith(prefix):
+                    connector_weights[key[len(prefix):]] = value
+        else:
+            mono_prefix = f"model.diffusion_model.{name}."
+            for key, value in weights.items():
+                if key.startswith(mono_prefix):
+                    connector_weights[key[len(mono_prefix):]] = value
+
+        if not connector_weights:
+            return
+
+        # Sanitize key names (only needed for monolithic/raw PyTorch keys)
+        mapped = {}
+        for key, value in connector_weights.items():
+            new_key = key
+            if not is_reformatted:
+                new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.")
+                new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.")
+                new_key = new_key.replace(".to_out.0.", ".to_out.")
+            mapped[new_key] = value
+
+        connector.load_weights(list(mapped.items()), strict=False)
+
+        # Manually load learnable_registers (plain mx.array, not tracked as parameter)
+        if "learnable_registers" in connector_weights:
+            connector.learnable_registers = connector_weights["learnable_registers"]
+
     def encode(
         self,
         prompt: str,
@@ -795,18 +889,37 @@ class LTX2TextEncoder(nn.Module):
         attention_mask = mx.array(inputs["attention_mask"])
 
         _, all_hidden_states = self.language_model(inputs=input_ids, input_embeddings=None, attention_mask=attention_mask, output_hidden_states=True)
+
+        if self.has_prompt_adaln:
+            # LTX-2.3: V2 feature extraction (per-token RMSNorm + rescale)
+            video_features = self.feature_extractor_v2(all_hidden_states, attention_mask, mode="video")
+            additive_mask = (attention_mask - 1).astype(video_features.dtype)
+            additive_mask = additive_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9
+
+            video_embeddings, _ = self.video_embeddings_connector(video_features, additive_mask)
+
+            if return_audio_embeddings:
+                audio_features = self.feature_extractor_v2(all_hidden_states, attention_mask, mode="audio")
+                audio_mask = (attention_mask - 1).astype(audio_features.dtype)
+                audio_mask = audio_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9
+                audio_embeddings, _ = self.audio_embeddings_connector(audio_features, audio_mask)
+                return video_embeddings, audio_embeddings
+            else:
+                return video_embeddings, attention_mask
+        else:
+            # LTX-2: V1 feature extraction (8 * (x - mean) / range)
             concat_hidden = norm_and_concat_hidden_states(
                 all_hidden_states, attention_mask, padding_side="left"
             )
-        features = self.feature_extractor(concat_hidden)
+
-        additive_mask = (attention_mask - 1).astype(features.dtype)
+            video_features = self.feature_extractor(concat_hidden)
+            additive_mask = (attention_mask - 1).astype(video_features.dtype)
             additive_mask = additive_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9
 
-        video_embeddings, _ = self.video_embeddings_connector(features, additive_mask)
+            video_embeddings, _ = self.video_embeddings_connector(video_features, additive_mask)
 
             if return_audio_embeddings:
-            # Process features through audio connector independently (same input as video)
+                audio_embeddings, _ = self.audio_embeddings_connector(video_features, additive_mask)
-            audio_embeddings, _ = self.audio_embeddings_connector(features, additive_mask)
                 return video_embeddings, audio_embeddings
             else:
                 return video_embeddings, attention_mask

mlx_video/models/ltx/transformer.py

@@ -20,6 +20,8 @@ class Modality:
     context_mask: Optional[mx.array] = None
     # Optional precomputed positional embeddings (RoPE) to avoid recomputation
     positional_embeddings: Optional[Tuple[mx.array, mx.array]] = None
+    # Raw sigma value (scalar per batch) for prompt adaln (LTX-2.3)
+    sigma: Optional[mx.array] = None
 
 
 @dataclass(frozen=True)
@@ -34,6 +36,9 @@ class TransformerArgs:
     cross_scale_shift_timestep: Optional[mx.array]
     cross_gate_timestep: Optional[mx.array]
     enabled: bool
+    # LTX-2.3: prompt-conditioned timestep embeddings for cross-attention
+    prompt_timesteps: Optional[mx.array] = None
+    prompt_embedded_timestep: Optional[mx.array] = None
 
 
 class BasicAVTransformerBlock(nn.Module):
@@ -50,20 +55,13 @@ class BasicAVTransformerBlock(nn.Module):
         audio: Optional[TransformerConfig] = None,
         rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
         norm_eps: float = 1e-6,
+        has_prompt_adaln: bool = False,
     ):
-        """Initialize transformer block.
-
-        Args:
-            idx: Block index
-            video: Video modality configuration
-            audio: Audio modality configuration
-            rope_type: Type of rotary position embedding
-            norm_eps: Epsilon for normalization
-        """
         super().__init__()
 
         self.idx = idx
         self.norm_eps = norm_eps
+        self.has_prompt_adaln = has_prompt_adaln
 
         # Video components
         if video is not None:
@@ -74,6 +72,7 @@ class BasicAVTransformerBlock(nn.Module):
                 context_dim=None,  # Self-attention
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             self.attn2 = Attention(
                 query_dim=video.dim,
@@ -82,10 +81,15 @@ class BasicAVTransformerBlock(nn.Module):
                 dim_head=video.d_head,
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             self.ff = FeedForward(video.dim, dim_out=video.dim)
-            # 6 scale-shift parameters: 3 for attention, 3 for MLP
+            # 9 params for LTX-2.3 (self-attn + cross-attn + FFN), 6 for LTX-2
-            self.scale_shift_table = mx.zeros((6, video.dim))
+            num_ada_params = 9 if has_prompt_adaln else 6
+            self.scale_shift_table = mx.zeros((num_ada_params, video.dim))
+
+            if has_prompt_adaln:
+                self.prompt_scale_shift_table = mx.zeros((2, video.dim))
 
         # Audio components
         if audio is not None:
@@ -96,6 +100,7 @@ class BasicAVTransformerBlock(nn.Module):
                 context_dim=None,
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             self.audio_attn2 = Attention(
                 query_dim=audio.dim,
@@ -104,9 +109,14 @@ class BasicAVTransformerBlock(nn.Module):
                 dim_head=audio.d_head,
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             self.audio_ff = FeedForward(audio.dim, dim_out=audio.dim)
-            self.audio_scale_shift_table = mx.zeros((6, audio.dim))
+            num_audio_ada_params = 9 if has_prompt_adaln else 6
+            self.audio_scale_shift_table = mx.zeros((num_audio_ada_params, audio.dim))
+
+            if has_prompt_adaln:
+                self.audio_prompt_scale_shift_table = mx.zeros((2, audio.dim))
 
         # Cross-modal attention (when both video and audio are enabled)
         if audio is not None and video is not None:
@@ -118,6 +128,7 @@ class BasicAVTransformerBlock(nn.Module):
                 dim_head=audio.d_head,
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             # Video-to-Audio: Q from audio, K/V from video
             self.video_to_audio_attn = Attention(
@@ -127,6 +138,7 @@ class BasicAVTransformerBlock(nn.Module):
                 dim_head=audio.d_head,
                 rope_type=rope_type,
                 norm_eps=norm_eps,
+                has_gate_logits=has_prompt_adaln,
             )
             # Scale-shift tables for cross-attention
             self.scale_shift_table_a2v_ca_audio = mx.zeros((5, audio.dim))
@@ -254,6 +266,18 @@ class BasicAVTransformerBlock(nn.Module):
             vx = vx + self.attn1(norm_vx, pe=video.positional_embeddings) * vgate_msa
 
             # Cross-attention with text context
+            if self.has_prompt_adaln:
+                # LTX-2.3: Q modulated by timestep (indices 6-8), context modulated by prompt_adaln
+                vshift_q, vscale_q, vgate_q = self.get_ada_values(
+                    self.scale_shift_table, vx.shape[0], video.timesteps, slice(6, 9)
+                )
+                vprompt_shift_kv, vprompt_scale_kv = self.get_ada_values(
+                    self.prompt_scale_shift_table, vx.shape[0], video.prompt_timesteps, slice(0, 2)
+                )
+                attn_input = rms_norm(vx, eps=self.norm_eps) * (1 + vscale_q) + vshift_q
+                encoder_hidden_states = video.context * (1 + vprompt_scale_kv) + vprompt_shift_kv
+                vx = vx + self.attn2(attn_input, context=encoder_hidden_states, mask=video.context_mask) * vgate_q
+            else:
                 vx = vx + self.attn2(
                     rms_norm(vx, eps=self.norm_eps),
                     context=video.context,
@@ -271,6 +295,18 @@ class BasicAVTransformerBlock(nn.Module):
             ax = ax + self.audio_attn1(norm_ax, pe=audio.positional_embeddings) * agate_msa
 
             # Cross-attention with text context
+            if self.has_prompt_adaln:
+                # LTX-2.3: Q modulated by timestep (indices 6-8), context modulated by prompt_adaln
+                ashift_q, ascale_q, agate_q = self.get_ada_values(
+                    self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(6, 9)
+                )
+                aprompt_shift_kv, aprompt_scale_kv = self.get_ada_values(
+                    self.audio_prompt_scale_shift_table, ax.shape[0], audio.prompt_timesteps, slice(0, 2)
+                )
+                attn_input_a = rms_norm(ax, eps=self.norm_eps) * (1 + ascale_q) + ashift_q
+                encoder_hidden_states_a = audio.context * (1 + aprompt_scale_kv) + aprompt_shift_kv
+                ax = ax + self.audio_attn2(attn_input_a, context=encoder_hidden_states_a, mask=audio.context_mask) * agate_q
+            else:
                 ax = ax + self.audio_attn2(
                     rms_norm(ax, eps=self.norm_eps),
                     context=audio.context,
@@ -341,7 +377,7 @@ class BasicAVTransformerBlock(nn.Module):
         # Process video feed-forward
         if run_vx:
             vshift_mlp, vscale_mlp, vgate_mlp = self.get_ada_values(
-                self.scale_shift_table, vx.shape[0], video.timesteps, slice(3, None)
+                self.scale_shift_table, vx.shape[0], video.timesteps, slice(3, 6)
             )
             vx_scaled = rms_norm(vx, eps=self.norm_eps) * (1 + vscale_mlp) + vshift_mlp
             vx = vx + self.ff(vx_scaled) * vgate_mlp
@@ -349,7 +385,7 @@ class BasicAVTransformerBlock(nn.Module):
         # Process audio feed-forward
         if run_ax:
             ashift_mlp, ascale_mlp, agate_mlp = self.get_ada_values(
-                self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(3, None)
+                self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(3, 6)
             )
             ax_scaled = rms_norm(ax, eps=self.norm_eps) * (1 + ascale_mlp) + ashift_mlp
             ax = ax + self.audio_ff(ax_scaled) * agate_mlp

mlx_video/models/ltx/upsampler.py

@@ -301,7 +301,6 @@ def upsample_latents(
     latent_std: mx.array,
     debug: bool = False,
 ) -> mx.array:
-
     # Un-normalize: latent * std + mean
     latent_mean = latent_mean.reshape(1, -1, 1, 1, 1)
     latent_std = latent_std.reshape(1, -1, 1, 1, 1)
@@ -350,19 +349,18 @@ def load_upsampler(weights_path: str) -> LatentUpsampler:
     for key, value in raw_weights.items():
         new_key = key
 
+        # LTX-2.3 upsampler uses sequential indexing: upsampler.0.* -> upsampler.conv.*
+        if key.startswith("upsampler.0."):
+            new_key = key.replace("upsampler.0.", "upsampler.conv.")
+
         # Conv3d weights: PyTorch (O, I, D, H, W) -> MLX (O, D, H, W, I)
-        if "conv" in key and "weight" in key and value.ndim == 5:
+        if "weight" in new_key and value.ndim == 5:
             value = mx.transpose(value, (0, 2, 3, 4, 1))
 
         # Conv2d weights: PyTorch (O, I, H, W) -> MLX (O, H, W, I)
-        if "conv" in key and "weight" in key and value.ndim == 4:
+        if "weight" in new_key and value.ndim == 4:
             value = mx.transpose(value, (0, 2, 3, 1))
 
-        # Map upsampler.conv to upsampler.conv (SpatialRationalResampler)
-        # Keys: upsampler.conv.weight, upsampler.conv.bias, upsampler.blur_down.kernel
-        if key.startswith("upsampler."):
-            new_key = key  # Keep as is for SpatialRationalResampler
-
         sanitized[new_key] = value
 
     # Load weights

mlx_video/models/ltx/video_vae/decoder.py

@@ -250,6 +250,18 @@ class LTX2VideoDecoder(nn.Module):
     - conv_out: 128 -> 48 (3 * 4^2 for patch_size=4)
     """
 
+    # Block definitions: ("res", channels, num_layers) or ("d2s", in_channels, reduction, stride)
+    # stride is (D, H, W) tuple
+    DEFAULT_BLOCKS = [
+        ("res", 1024, 5),
+        ("d2s", 1024, 2, (2, 2, 2)),
+        ("res", 512, 5),
+        ("d2s", 512, 2, (2, 2, 2)),
+        ("res", 256, 5),
+        ("d2s", 256, 2, (2, 2, 2)),
+        ("res", 128, 5),
+    ]
+
     def __init__(
         self,
         in_channels: int = 128,
@@ -258,6 +270,7 @@ class LTX2VideoDecoder(nn.Module):
         num_layers_per_block: int = 5,
         spatial_padding_mode: PaddingModeType = PaddingModeType.REFLECT,
         timestep_conditioning: bool = True,
+        decoder_blocks: list = None,
     ):
         super().__init__()
 
@@ -272,13 +285,17 @@ class LTX2VideoDecoder(nn.Module):
         # Per-channel statistics for denormalization (loaded from weights)
         self.per_channel_statistics = PerChannelStatistics(latent_channels=in_channels)
 
-        # Initial conv: 128 -> 1024
+        blocks = decoder_blocks or self.DEFAULT_BLOCKS
+        first_ch = blocks[0][1]
+        last_ch = blocks[-1][1]
+
+        # Initial conv: in_channels -> first block channels
         class ConvInWrapper(nn.Module):
             def __init__(self_inner):
                 super().__init__()
                 self_inner.conv = CausalConv3d(
                     in_channels=in_channels,
-                    out_channels=1024,
+                    out_channels=first_ch,
                     kernel_size=3,
                     stride=1,
                     padding=1,
@@ -288,45 +305,32 @@ class LTX2VideoDecoder(nn.Module):
                 return self_inner.conv(x, causal=causal)
         self.conv_in = ConvInWrapper()
 
-        # Up blocks: alternating ResBlockGroup and DepthToSpaceUpsample
+        # Build up blocks from config
-        # Use dict with int keys for MLX to track parameters properly
+        self.up_blocks = {}
-        self.up_blocks = {
+        for idx, block_def in enumerate(blocks):
-            0: ResBlockGroup(1024, num_layers_per_block, spatial_padding_mode, timestep_conditioning),
+            block_type = block_def[0]
-            1: DepthToSpaceUpsample(
+            ch = block_def[1]
+            if block_type == "res":
+                num_layers = block_def[2] if len(block_def) > 2 else num_layers_per_block
+                self.up_blocks[idx] = ResBlockGroup(ch, num_layers, spatial_padding_mode, timestep_conditioning)
+            elif block_type == "d2s":
+                reduction = block_def[2] if len(block_def) > 2 else 2
+                stride = block_def[3] if len(block_def) > 3 else (2, 2, 2)
+                self.up_blocks[idx] = DepthToSpaceUpsample(
                     dims=3,
-                in_channels=1024,
+                    in_channels=ch,
-                stride=(2, 2, 2),
+                    stride=stride,
                     residual=True,
-                out_channels_reduction_factor=2,
+                    out_channels_reduction_factor=reduction,
                     spatial_padding_mode=spatial_padding_mode,
-            ),
+                )
-            2: ResBlockGroup(512, num_layers_per_block, spatial_padding_mode, timestep_conditioning),
-            3: DepthToSpaceUpsample(
-                dims=3,
-                in_channels=512,
-                stride=(2, 2, 2),
-                residual=True,
-                out_channels_reduction_factor=2,
-                spatial_padding_mode=spatial_padding_mode,
-            ),
-            4: ResBlockGroup(256, num_layers_per_block, spatial_padding_mode, timestep_conditioning),
-            5: DepthToSpaceUpsample(
-                dims=3,
-                in_channels=256,
-                stride=(2, 2, 2),
-                residual=True,
-                out_channels_reduction_factor=2,
-                spatial_padding_mode=spatial_padding_mode,
-            ),
-            6: ResBlockGroup(128, num_layers_per_block, spatial_padding_mode, timestep_conditioning),
-        }
 
         final_out_channels = out_channels * patch_size * patch_size
         class ConvOutWrapper(nn.Module):
             def __init__(self_inner):
                 super().__init__()
                 self_inner.conv = CausalConv3d(
-                    in_channels=128,
+                    in_channels=last_ch,
                     out_channels=final_out_channels,
                     kernel_size=3,
                     stride=1,
@@ -342,9 +346,9 @@ class LTX2VideoDecoder(nn.Module):
         if timestep_conditioning:
             self.timestep_scale_multiplier = mx.array(1000.0)
             self.last_time_embedder = PixArtAlphaTimestepEmbedder(
-                embedding_dim=128 * 2  # 256, matches (2, 128) table
+                embedding_dim=last_ch * 2
             )
-            self.last_scale_shift_table = mx.zeros((2, 128))
+            self.last_scale_shift_table = mx.zeros((2, last_ch))
 
     def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
         # Build decoder weights dict with key remapping
@@ -418,11 +422,96 @@ class LTX2VideoDecoder(nn.Module):
             weights.update(mx.load(str(wf)))
 
 
-        model = cls(timestep_conditioning=config_dict.get("timestep_conditioning", False))
+        # Infer block structure from weights
+        decoder_blocks = cls._infer_blocks(weights)
+
+        model = cls(
+            timestep_conditioning=config_dict.get("timestep_conditioning", False),
+            decoder_blocks=decoder_blocks,
+        )
         weights = model.sanitize(weights)
         model.load_weights(list(weights.items()), strict=strict)
         return model
 
+    @staticmethod
+    def _infer_blocks(weights: dict) -> list:
+        """Infer decoder block structure from weight keys."""
+        block_indices = set()
+        for k in weights:
+            if "up_blocks." in k:
+                idx_str = k.split("up_blocks.")[1].split(".")[0]
+                if idx_str.isdigit():
+                    block_indices.add(int(idx_str))
+
+        if not block_indices:
+            return None
+
+        # First pass: collect block info
+        raw_blocks = []
+        for idx in sorted(block_indices):
+            has_conv = any(f"up_blocks.{idx}.conv." in k for k in weights)
+            res_indices = set()
+            for k in weights:
+                prefix = f"up_blocks.{idx}.res_blocks."
+                if prefix in k:
+                    res_idx = k.split(prefix)[1].split(".")[0]
+                    if res_idx.isdigit():
+                        res_indices.add(int(res_idx))
+
+            if has_conv and not res_indices:
+                # D2S block - get conv shape
+                for k, v in weights.items():
+                    if f"up_blocks.{idx}.conv." in k and "weight" in k:
+                        in_ch = v.shape[-1] if v.ndim == 5 else v.shape[1]
+                        conv_out_ch = v.shape[0]
+                        raw_blocks.append(("d2s", in_ch, conv_out_ch))
+                        break
+            elif res_indices:
+                num_res = max(res_indices) + 1
+                for k, v in weights.items():
+                    if f"up_blocks.{idx}.res_blocks.0.conv1" in k and "weight" in k:
+                        ch = v.shape[0]
+                        raw_blocks.append(("res", ch, num_res))
+                        break
+
+        # Second pass: determine d2s strides using the channel progression
+        # For each d2s block, the next res block tells us the expected output channels
+        blocks = []
+        for i, block in enumerate(raw_blocks):
+            if block[0] == "res":
+                blocks.append(block)
+            elif block[0] == "d2s":
+                in_ch, conv_out_ch = block[1], block[2]
+                # Find next res block's channels
+                next_ch = None
+                for j in range(i + 1, len(raw_blocks)):
+                    if raw_blocks[j][0] == "res":
+                        next_ch = raw_blocks[j][1]
+                        break
+
+                if next_ch is None:
+                    next_ch = in_ch // 2  # fallback
+
+                # out_ch = in_ch // reduction
+                reduction = in_ch // next_ch if next_ch > 0 else 2
+
+                # conv_out = next_ch * multiplier → multiplier = conv_out / next_ch
+                multiplier = conv_out_ch // next_ch if next_ch > 0 else 8
+
+                # Determine stride from multiplier
+                if multiplier == 8:
+                    stride = (2, 2, 2)
+                elif multiplier == 4:
+                    stride = (1, 2, 2)
+                elif multiplier == 2:
+                    stride = (2, 1, 1)
+                else:
+                    stride = (2, 2, 2)
+
+                blocks.append(("d2s", in_ch, reduction, stride))
+
+        return blocks if blocks else None
+
   
 
     def pixel_norm(self, x: mx.array, eps: float = 1e-8) -> mx.array: