Moved Volinga as an external method (#2282)

* Moved Volinga as an external method

* Update external_methods.py for black

* Formatted with black

---------

Co-authored-by: jperezh <jperezh@arquimea.com>

docs/extensions/unreal_engine.md

@@ -10,14 +10,9 @@
 
 NeRFStudio models can be used in Unreal Engine if they are converted to an NVOL file. NVOL is a new standard file format to store NeRFs in a fast and efficient way. NVOL files can be obtained from NeRFStudio checkpoints files (.ckpt) using the [Volinga Suite](https://volinga.ai/).
 
-:::{admonition} Note
-:class: warning
-Volinga Suite is not yet publicly available. If you are interested in converting your models to NVOL, sign up for the beta at [volinga.ai](https://volinga.ai).
-:::
-
 
 ## Exporting your model to NVOL
-Currently NVOL file only supports Volinga model (which is based on nerfacto). You can train your model using the following command:
+Currently NVOL file only supports Volinga model (which is based on nerfacto). To use Volinga model you will need to install [Volinga extension for NeRFStudio](https://github.com/Volinga/volinga-model). You can train your model using the following command:
 
 ```bash
 ns-train volinga --data /path/to/your/data --vis viewer

nerfstudio/configs/external_methods.py

@@ -121,6 +121,24 @@ To enable NeRFPlayer, you must install it first by running:
     )
 )
 
+# Volinga
+external_methods.append(
+    ExternalMethod(
+        """[bold yellow]Volinga[/bold yellow]
+For more information visit: https://docs.nerf.studio/en/latest/extensions/unreal_engine.html
+
+To enable Volinga, you must install it first by running:
+  [grey]pip install git+https://github.com/Volinga/volinga-model[/grey]""",
+        configurations=[
+            (
+                "volinga",
+                "Real-time rendering model from Volinga. Directly exportable to NVOL format at https://volinga.ai/",
+            ),
+        ],
+        pip_package="git+https://github.com/Volinga/volinga-model",
+    )
+)
+
 
 @dataclass
 class ExternalMethodTrainerConfig(TrainerConfig):

nerfstudio/configs/method_configs.py

@@ -67,7 +67,6 @@ method_configs: Dict[str, TrainerConfig] = {}
 descriptions = {
     "nerfacto": "Recommended real-time model tuned for real captures. This model will be continually updated.",
     "depth-nerfacto": "Nerfacto with depth supervision.",
-    "volinga": "Real-time rendering model from Volinga. Directly exportable to NVOL format at https://volinga.ai/",
     "instant-ngp": "Implementation of Instant-NGP. Recommended real-time model for unbounded scenes.",
     "instant-ngp-bounded": "Implementation of Instant-NGP. Recommended for bounded real and synthetic scenes",
     "mipnerf": "High quality model for bounded scenes. (slow)",
@@ -233,47 +232,6 @@ method_configs["depth-nerfacto"] = TrainerConfig(
     vis="viewer",
 )
 
-method_configs["volinga"] = TrainerConfig(
-    method_name="volinga",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-            camera_optimizer=CameraOptimizerConfig(
-                mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
-            ),
-        ),
-        model=NerfactoModelConfig(
-            eval_num_rays_per_chunk=1 << 15,
-            hidden_dim=32,
-            hidden_dim_color=32,
-            hidden_dim_transient=32,
-            num_nerf_samples_per_ray=24,
-            proposal_net_args_list=[
-                {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 128, "use_linear": True},
-                {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256, "use_linear": True},
-            ],
-        ),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
-
 method_configs["instant-ngp"] = TrainerConfig(
     method_name="instant-ngp",
     steps_per_eval_batch=500,

tests/test_train.py

@@ -21,7 +21,6 @@ BLACKLIST = [
     "instant-ngp",
     "instant-ngp-bounded",
     "nerfacto-big",
-    "volinga",
     "phototourism",
     "depth-nerfacto",
     "neus",