from typing import List, Optional, Tuple import mlx.core as mx import mlx.nn as nn from mlx_video.models.ltx.config import ( LTXModelConfig, LTXModelType, LTXRopeType, TransformerConfig, ) from mlx_video.models.ltx.adaln import AdaLayerNormSingle from mlx_video.models.ltx.rope import precompute_freqs_cis from mlx_video.models.ltx.text_projection import PixArtAlphaTextProjection from mlx_video.models.ltx.transformer import ( BasicAVTransformerBlock, Modality, TransformerArgs, ) from mlx_video.utils import to_denoised class TransformerArgsPreprocessor: def __init__( self, patchify_proj: nn.Linear, adaln: AdaLayerNormSingle, caption_projection: PixArtAlphaTextProjection, inner_dim: int, max_pos: List[int], num_attention_heads: int, use_middle_indices_grid: bool, timestep_scale_multiplier: int, positional_embedding_theta: float, rope_type: LTXRopeType, double_precision_rope: bool = False, ): self.patchify_proj = patchify_proj self.adaln = adaln self.caption_projection = caption_projection self.inner_dim = inner_dim self.max_pos = max_pos self.num_attention_heads = num_attention_heads self.use_middle_indices_grid = use_middle_indices_grid self.timestep_scale_multiplier = timestep_scale_multiplier self.positional_embedding_theta = positional_embedding_theta self.rope_type = rope_type self.double_precision_rope = double_precision_rope def _prepare_timestep( self, 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 = self.adaln(timestep.reshape(-1), hidden_dtype=hidden_dtype) # Reshape to (batch, tokens, dim) 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, x: mx.array, attention_mask: Optional[mx.array] = None, ) -> Tuple[mx.array, Optional[mx.array]]: batch_size = x.shape[0] # Context is already processed through embeddings connector in text encoder # 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 def _prepare_attention_mask( self, attention_mask: Optional[mx.array], x_dtype: mx.Dtype, ) -> Optional[mx.array]: if attention_mask is None: return None # Check if already float if attention_mask.dtype in [mx.float16, mx.float32, mx.bfloat16]: return attention_mask # Convert boolean/int mask to float mask # 0 -> -inf (masked), 1 -> 0 (not masked) mask = (attention_mask.astype(x_dtype) - 1) * 1e9 mask = mx.reshape(mask, (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) return mask def _prepare_positional_embeddings( self, positions: mx.array, inner_dim: int, max_pos: List[int], use_middle_indices_grid: bool, num_attention_heads: int, ) -> Tuple[mx.array, mx.array]: pe = precompute_freqs_cis( positions, dim=inner_dim, theta=self.positional_embedding_theta, max_pos=max_pos, use_middle_indices_grid=use_middle_indices_grid, num_attention_heads=num_attention_heads, rope_type=self.rope_type, double_precision=self.double_precision_rope, ) return pe def prepare(self, modality: Modality) -> TransformerArgs: x = self.patchify_proj(modality.latent) timestep, embedded_timestep = self._prepare_timestep(modality.timesteps, x.shape[0], hidden_dtype=x.dtype) context, attention_mask = self._prepare_context(modality.context, x, modality.context_mask) attention_mask = self._prepare_attention_mask(attention_mask, modality.latent.dtype) # Use precomputed positional embeddings if provided (avoids expensive RoPE recomputation) if modality.positional_embeddings is not None: pe = modality.positional_embeddings else: pe = self._prepare_positional_embeddings( positions=modality.positions, inner_dim=self.inner_dim, max_pos=self.max_pos, use_middle_indices_grid=self.use_middle_indices_grid, num_attention_heads=self.num_attention_heads, ) return TransformerArgs( x=x, context=context, context_mask=attention_mask, timesteps=timestep, embedded_timestep=embedded_timestep, positional_embeddings=pe, cross_positional_embeddings=None, cross_scale_shift_timestep=None, cross_gate_timestep=None, enabled=modality.enabled, ) class MultiModalTransformerArgsPreprocessor: def __init__( self, patchify_proj: nn.Linear, adaln: AdaLayerNormSingle, caption_projection: PixArtAlphaTextProjection, cross_scale_shift_adaln: AdaLayerNormSingle, cross_gate_adaln: AdaLayerNormSingle, inner_dim: int, max_pos: List[int], num_attention_heads: int, cross_pe_max_pos: int, use_middle_indices_grid: bool, audio_cross_attention_dim: int, timestep_scale_multiplier: int, positional_embedding_theta: float, rope_type: LTXRopeType, av_ca_timestep_scale_multiplier: int, double_precision_rope: bool = False, ): self.simple_preprocessor = TransformerArgsPreprocessor( patchify_proj=patchify_proj, adaln=adaln, caption_projection=caption_projection, inner_dim=inner_dim, max_pos=max_pos, num_attention_heads=num_attention_heads, use_middle_indices_grid=use_middle_indices_grid, timestep_scale_multiplier=timestep_scale_multiplier, positional_embedding_theta=positional_embedding_theta, rope_type=rope_type, double_precision_rope=double_precision_rope, ) self.cross_scale_shift_adaln = cross_scale_shift_adaln self.cross_gate_adaln = cross_gate_adaln self.cross_pe_max_pos = cross_pe_max_pos self.audio_cross_attention_dim = audio_cross_attention_dim self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier def prepare(self, modality: Modality) -> TransformerArgs: from dataclasses import replace transformer_args = self.simple_preprocessor.prepare(modality) # Prepare cross-modal positional embeddings cross_pe = self.simple_preprocessor._prepare_positional_embeddings( positions=modality.positions[:, 0:1, :], inner_dim=self.audio_cross_attention_dim, max_pos=[self.cross_pe_max_pos], use_middle_indices_grid=True, num_attention_heads=self.simple_preprocessor.num_attention_heads, ) # Prepare cross-attention timestep embeddings cross_scale_shift_timestep, cross_gate_timestep = self._prepare_cross_attention_timestep( timestep=modality.timesteps, timestep_scale_multiplier=self.simple_preprocessor.timestep_scale_multiplier, batch_size=transformer_args.x.shape[0], hidden_dtype=transformer_args.x.dtype, ) return replace( transformer_args, cross_positional_embeddings=cross_pe, cross_scale_shift_timestep=cross_scale_shift_timestep, cross_gate_timestep=cross_gate_timestep, ) def _prepare_cross_attention_timestep( self, timestep: mx.array, timestep_scale_multiplier: int, batch_size: int, hidden_dtype: mx.Dtype = None, ) -> Tuple[mx.array, mx.array]: timestep = timestep * timestep_scale_multiplier av_ca_factor = self.av_ca_timestep_scale_multiplier / timestep_scale_multiplier scale_shift_timestep, _ = self.cross_scale_shift_adaln(timestep.reshape(-1), hidden_dtype=hidden_dtype) scale_shift_timestep = mx.reshape(scale_shift_timestep, (batch_size, -1, scale_shift_timestep.shape[-1])) gate_timestep, _ = self.cross_gate_adaln(timestep.reshape(-1) * av_ca_factor, hidden_dtype=hidden_dtype) gate_timestep = mx.reshape(gate_timestep, (batch_size, -1, gate_timestep.shape[-1])) return scale_shift_timestep, gate_timestep class LTXModel(nn.Module): def __init__(self, config: LTXModelConfig): super().__init__() self.config = config self.model_type = config.model_type self.use_middle_indices_grid = config.use_middle_indices_grid self.rope_type = config.rope_type self.timestep_scale_multiplier = config.timestep_scale_multiplier self.positional_embedding_theta = config.positional_embedding_theta cross_pe_max_pos = None if config.model_type.is_video_enabled(): self.positional_embedding_max_pos = config.positional_embedding_max_pos self.num_attention_heads = config.num_attention_heads self.inner_dim = config.inner_dim self._init_video(config) if config.model_type.is_audio_enabled(): self.audio_positional_embedding_max_pos = config.audio_positional_embedding_max_pos self.audio_num_attention_heads = config.audio_num_attention_heads self.audio_inner_dim = config.audio_inner_dim self._init_audio(config) # Initialize cross-modal components if config.model_type.is_video_enabled() and config.model_type.is_audio_enabled(): cross_pe_max_pos = max( config.positional_embedding_max_pos[0], config.audio_positional_embedding_max_pos[0], ) self.av_ca_timestep_scale_multiplier = config.av_ca_timestep_scale_multiplier self.audio_cross_attention_dim = config.audio_cross_attention_dim self._init_audio_video(config) self._init_preprocessors(config, cross_pe_max_pos) self._init_transformer_blocks(config) 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) self.caption_projection = PixArtAlphaTextProjection( in_features=config.caption_channels, hidden_size=self.inner_dim, ) self.scale_shift_table = mx.zeros((2, self.inner_dim)) self.norm_out = nn.LayerNorm(self.inner_dim, eps=config.norm_eps, affine=False) self.proj_out = nn.Linear(self.inner_dim, config.out_channels) 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 self.audio_caption_projection = PixArtAlphaTextProjection( in_features=config.audio_caption_channels, hidden_size=self.audio_inner_dim, ) # Output components self.audio_scale_shift_table = mx.zeros((2, self.audio_inner_dim)) self.audio_norm_out = nn.LayerNorm(self.audio_inner_dim, eps=config.norm_eps, affine=False) self.audio_proj_out = nn.Linear(self.audio_inner_dim, config.audio_out_channels) def _init_audio_video(self, config: LTXModelConfig) -> None: num_scale_shift_values = 4 self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle( self.inner_dim, embedding_coefficient=num_scale_shift_values, ) self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, embedding_coefficient=num_scale_shift_values, ) self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle( self.inner_dim, embedding_coefficient=1, ) self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, embedding_coefficient=1, ) def _init_preprocessors(self, config: LTXModelConfig, cross_pe_max_pos: Optional[int]) -> None: if config.model_type.is_video_enabled() and config.model_type.is_audio_enabled(): # Multi-modal preprocessors self.video_args_preprocessor = MultiModalTransformerArgsPreprocessor( patchify_proj=self.patchify_proj, adaln=self.adaln_single, caption_projection=self.caption_projection, 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, max_pos=config.positional_embedding_max_pos, num_attention_heads=self.num_attention_heads, cross_pe_max_pos=cross_pe_max_pos, use_middle_indices_grid=config.use_middle_indices_grid, audio_cross_attention_dim=config.audio_cross_attention_dim, timestep_scale_multiplier=config.timestep_scale_multiplier, positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier, double_precision_rope=config.double_precision_rope, ) self.audio_args_preprocessor = MultiModalTransformerArgsPreprocessor( patchify_proj=self.audio_patchify_proj, adaln=self.audio_adaln_single, caption_projection=self.audio_caption_projection, 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, max_pos=config.audio_positional_embedding_max_pos, num_attention_heads=self.audio_num_attention_heads, cross_pe_max_pos=cross_pe_max_pos, use_middle_indices_grid=config.use_middle_indices_grid, audio_cross_attention_dim=config.audio_cross_attention_dim, timestep_scale_multiplier=config.timestep_scale_multiplier, positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier, double_precision_rope=config.double_precision_rope, ) 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, inner_dim=self.inner_dim, max_pos=config.positional_embedding_max_pos, num_attention_heads=self.num_attention_heads, use_middle_indices_grid=config.use_middle_indices_grid, timestep_scale_multiplier=config.timestep_scale_multiplier, positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, double_precision_rope=config.double_precision_rope, ) 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, inner_dim=self.audio_inner_dim, max_pos=config.audio_positional_embedding_max_pos, num_attention_heads=self.audio_num_attention_heads, use_middle_indices_grid=config.use_middle_indices_grid, timestep_scale_multiplier=config.timestep_scale_multiplier, positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, double_precision_rope=config.double_precision_rope, ) 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, video=video_config, audio=audio_config, rope_type=config.rope_type, norm_eps=config.norm_eps, ) for idx in range(config.num_layers) } def _process_transformer_blocks( self, video: Optional[TransformerArgs], audio: Optional[TransformerArgs], ) -> Tuple[Optional[TransformerArgs], Optional[TransformerArgs]]: """Process through all transformer blocks.""" for block in self.transformer_blocks.values(): video, audio = block(video=video, audio=audio) return video, audio def _process_output( self, scale_shift_table: mx.array, norm_out: nn.LayerNorm, proj_out: nn.Linear, x: mx.array, embedded_timestep: mx.array, ) -> mx.array: # scale_shift_table: (2, dim) -> expand to (1, 1, 2, dim) # embedded_timestep: (B, 1, dim) -> expand to (B, 1, 1, dim) table_expanded = scale_shift_table[None, None, :, :] # (1, 1, 2, dim) timestep_expanded = embedded_timestep[:, :, None, :] # (B, 1, 1, dim) # Combine: (1, 1, 2, dim) + (B, 1, 1, dim) broadcasts to (B, 1, 2, dim) scale_shift_values = table_expanded + timestep_expanded # Extract shift and scale (first index is shift, second is scale) shift = scale_shift_values[:, :, 0, :] # (B, 1, dim) scale = scale_shift_values[:, :, 1, :] # (B, 1, dim) x = norm_out(x) x = x * (1 + scale) + shift # Broadcasts (B, 1, dim) to (B, seq, dim) x = proj_out(x) return x def __call__( self, video: Optional[Modality] = None, audio: Optional[Modality] = None, ) -> Tuple[Optional[mx.array], Optional[mx.array]]: # Validate inputs if not self.model_type.is_video_enabled() and video is not None: raise ValueError("Video is not enabled for this model") if not self.model_type.is_audio_enabled() and audio is not None: raise ValueError("Audio is not enabled for this model") # Preprocess arguments video_args = self.video_args_preprocessor.prepare(video) if video is not None else None audio_args = self.audio_args_preprocessor.prepare(audio) if audio is not None else None # Process transformer blocks video_out, audio_out = self._process_transformer_blocks( video=video_args, audio=audio_args, ) # Process outputs vx = ( self._process_output( self.scale_shift_table, self.norm_out, self.proj_out, video_out.x, video_out.embedded_timestep, ) if video_out is not None else None ) ax = ( self._process_output( self.audio_scale_shift_table, self.audio_norm_out, self.audio_proj_out, audio_out.x, audio_out.embedded_timestep, ) if audio_out is not None else None ) return vx, ax def sanitize(self, weights: dict) -> dict: sanitized = {} for key, value in weights.items(): new_key = key # Handle common remappings # transformer_blocks.X -> transformer_blocks[X] if "transformer_blocks." in new_key: # Keep as-is for now, MLX handles this pass sanitized[new_key] = value return sanitized class X0Model(nn.Module): def __init__(self, velocity_model: LTXModel): super().__init__() self.velocity_model = velocity_model def __call__( self, video: Optional[Modality] = None, audio: Optional[Modality] = None, ) -> Tuple[Optional[mx.array], Optional[mx.array]]: vx, ax = self.velocity_model(video, audio) denoised_video = to_denoised(video.latent, vx, video.timesteps) if vx is not None else None denoised_audio = to_denoised(audio.latent, ax, audio.timesteps) if ax is not None else None return denoised_video, denoised_audio