Add audio encoder sanitization and configuration inference to LTX-2 model conversion process; update conversion print statements for new encoder step

mlx_video/models/ltx_2/convert.py

@@ -189,6 +189,36 @@ def sanitize_audio_decoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
     return sanitized
 
 
+def sanitize_audio_encoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
+    """Sanitize audio VAE encoder keys: strip prefix, transpose Conv2d."""
+    sanitized = {}
+    for key, value in weights.items():
+        new_key = None
+
+        if key.startswith(AUDIO_ENCODER_PREFIX):
+            new_key = key[len(AUDIO_ENCODER_PREFIX):]
+        elif key.startswith(AUDIO_STATS_PREFIX):
+            if "mean-of-means" in key:
+                new_key = "per_channel_statistics.mean_of_means"
+            elif "std-of-means" in key:
+                new_key = "per_channel_statistics.std_of_means"
+            else:
+                continue
+        elif key == "latents_mean":
+            new_key = "per_channel_statistics.mean_of_means"
+        elif key == "latents_std":
+            new_key = "per_channel_statistics.std_of_means"
+        else:
+            continue
+
+        # Conv2d: PyTorch (O, I, H, W) -> MLX (O, H, W, I)
+        if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4:
+            value = mx.transpose(value, (0, 2, 3, 1))
+
+        sanitized[new_key] = value
+    return sanitized
+
+
 def sanitize_vocoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]:
     """Sanitize vocoder keys: strip prefix, transpose Conv1d/ConvTranspose1d."""
     sanitized = {}
@@ -553,6 +583,31 @@ def infer_audio_vae_config(weights: Dict[str, mx.array]) -> dict:
     }
 
 
+def infer_audio_encoder_config(weights: Dict[str, mx.array]) -> dict:
+    """Return audio encoder config (mirrors decoder but with encoder-specific fields)."""
+    return {
+        "attn_resolutions": [],
+        "attn_type": "vanilla",
+        "causality_axis": "height",
+        "ch": 128,
+        "ch_mult": [1, 2, 4],
+        "dropout": 0.0,
+        "in_channels": 2,
+        "double_z": True,
+        "is_causal": True,
+        "mel_bins": 64,
+        "mel_hop_length": 160,
+        "mid_block_add_attention": False,
+        "n_fft": 1024,
+        "norm_type": "pixel",
+        "num_res_blocks": 2,
+        "resamp_with_conv": True,
+        "resolution": 256,
+        "sample_rate": 16000,
+        "z_channels": 8,
+    }
+
+
 def infer_vocoder_config(weights: Dict[str, mx.array]) -> dict:
     """Infer vocoder config from weights."""
     # Check for bwe_generator (LTX-2.3 BigVGAN vocoder)
@@ -597,7 +652,7 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     print("\nExtracting components...")
 
     # 1. Transformer
-    print("  [1/6] Transformer...")
+    print("  [1/7] Transformer...")
     transformer_weights = sanitize_transformer(all_weights)
     num_shards = save_sharded(transformer_weights, output_path / "transformer")
     config = infer_transformer_config(transformer_weights)
@@ -606,7 +661,7 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     print(f"    {len(transformer_weights)} keys, {t_params:,} params, {num_shards} shards")
 
     # 2. VAE Decoder
-    print("  [2/6] VAE Decoder...")
+    print("  [2/7] VAE Decoder...")
     vae_decoder_weights = sanitize_vae_decoder(all_weights)
     save_single(vae_decoder_weights, output_path / "vae" / "decoder")
     config = infer_vae_decoder_config(vae_decoder_weights, variant)
@@ -615,7 +670,7 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     print(f"    {len(vae_decoder_weights)} keys, {d_params:,} params")
 
     # 3. VAE Encoder
-    print("  [3/6] VAE Encoder...")
+    print("  [3/7] VAE Encoder...")
     vae_encoder_weights = sanitize_vae_encoder(all_weights)
     save_single(vae_encoder_weights, output_path / "vae" / "encoder")
     config = infer_vae_encoder_config(vae_encoder_weights)
@@ -624,7 +679,7 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     print(f"    {len(vae_encoder_weights)} keys, {e_params:,} params")
 
     # 4. Audio VAE Decoder
-    print("  [4/6] Audio VAE Decoder...")
+    print("  [4/7] Audio VAE Decoder...")
     audio_decoder_weights = sanitize_audio_decoder(all_weights)
     save_single(audio_decoder_weights, output_path / "audio_vae" / "decoder")
     config = infer_audio_vae_config(audio_decoder_weights)
@@ -632,8 +687,17 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     a_params = sum(v.size for v in audio_decoder_weights.values())
     print(f"    {len(audio_decoder_weights)} keys, {a_params:,} params")
 
-    # 5. Vocoder
+    # 5. Audio VAE Encoder
-    print("  [5/6] Vocoder...")
+    print("  [5/7] Audio VAE Encoder...")
+    audio_encoder_weights = sanitize_audio_encoder(all_weights)
+    save_single(audio_encoder_weights, output_path / "audio_vae" / "encoder")
+    config = infer_audio_encoder_config(audio_encoder_weights)
+    save_config(config, output_path / "audio_vae" / "encoder")
+    ae_params = sum(v.size for v in audio_encoder_weights.values())
+    print(f"    {len(audio_encoder_weights)} keys, {ae_params:,} params")
+
+    # 6. Vocoder
+    print("  [6/7] Vocoder...")
     vocoder_weights = sanitize_vocoder(all_weights)
     save_single(vocoder_weights, output_path / "vocoder")
     config = infer_vocoder_config(vocoder_weights)
@@ -641,8 +705,8 @@ def convert(source: str, output_path: Path, variant: str = "distilled"):
     v_params = sum(v.size for v in vocoder_weights.values())
     print(f"    {len(vocoder_weights)} keys, {v_params:,} params")
 
-    # 6. Text Projections
+    # 7. Text Projections
-    print("  [6/6] Text Projections...")
+    print("  [7/7] Text Projections...")
     text_proj_weights = extract_text_projections(all_weights)
     tp_dir = output_path / "text_projections"
     tp_dir.mkdir(parents=True, exist_ok=True)