add from pretrained

mlx_video/models/ltx/video_vae/decoder.py

@@ -385,28 +385,38 @@ class LTX2VideoDecoder(nn.Module):
         return sanitized
 
     @classmethod
-    def from_pretrained(cls, model_path: Path, timestep_conditioning: Optional[bool] = None, strict: bool = True) -> "LTX2VideoDecoder":
-        from safetensors import safe_open
+    def from_pretrained(cls, model_path: Path, strict: bool = True) -> "LTX2VideoDecoder":
+        """Load a pretrained decoder from a directory with config.json and weights.
+
+        Args:
+            model_path: Path to directory containing config.json and safetensors files,
+                        or path to a single safetensors file.
+            strict: Whether to require all weight keys to match.
+
+        Returns:
+            Loaded LTX2VideoDecoder instance
+        """
         import json
-        weights = mx.load(str(model_path))
 
-        # Read config from safetensors metadata to auto-detect timestep_conditioning
-        if timestep_conditioning is None:
-            try:
-                with safe_open(str(model_path), framework="numpy") as f:
-                    metadata = f.metadata()
-                    if metadata and "config" in metadata:
-                        configs = json.loads(metadata["config"])
-                        vae_config = configs.get("vae", {})
-                        timestep_conditioning = vae_config.get("timestep_conditioning", False)
-                        print(f"  Auto-detected timestep_conditioning={timestep_conditioning} from weights")
-                    else:
-                        timestep_conditioning = False
-            except Exception as e:
-                print(f"  Could not read config from metadata: {e}, defaulting to timestep_conditioning=False")
-                timestep_conditioning = False
+        model_path = Path(model_path)
 
-        model = cls(timestep_conditioning=timestep_conditioning)
+        if model_path.is_dir():
+            # Load config from directory
+            config_path = model_path / "config.json"
+            if config_path.exists():
+                with open(config_path) as f:
+                    config_dict = json.load(f)
+
+            # Load weights from directory
+            weight_files = sorted(model_path.glob("*.safetensors"))
+            if not weight_files:
+                raise FileNotFoundError(f"No safetensors files found in {model_path}")
+            weights = {}
+            for wf in weight_files:
+                weights.update(mx.load(str(wf)))
+
+
+        model = cls(timestep_conditioning=config_dict.get("timestep_conditioning", False))
         weights = model.sanitize(weights)
         model.load_weights(list(weights.items()), strict=strict)
         return model