Add support for NVIDIA Blackwell GPUs (sm_120, RTX 50-series)

- Bump gsplat to >=1.5.3 (sm_120-capable release line) and nerfacc to >=0.5.3
- Drop EOL Python 3.8/3.9: requires-python >=3.10, CI publish/doc workflows
- torch.load(..., weights_only=False) for torch>=2.6 checkpoint compatibility
- Use torch.amp custom_fwd/custom_bwd with device_type="cuda" (torch>=2.4 API)
- Replace deprecated PIL private encoder API in pil_to_numpy with np.asarray
  (fixes Pillow>=11 compatibility)
- Docs: add Torch 2.8+ / CUDA 12.8 install tab for Blackwell GPUs

Tested on RTX 5060 (sm_120) with torch 2.11.0+cu128: splatfacto and
nerfacto train end-to-end and ns-render produces correct output.

.github/workflows/doc.yml

@@ -15,7 +15,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
         with:
-          python-version: '3.9'
+          python-version: '3.10'
       - name: Install dependencies
         run: |
           pip install uv

.github/workflows/publish.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v1
         with:
-          python-version: '3.8'
+          python-version: '3.10'
       - name: Install dependencies
         run: |
           python -m pip install build twine

docs/quickstart/installation.md

@@ -5,7 +5,7 @@
 ::::::{tab-set}
 :::::{tab-item} Linux
 
-Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
+Nerfstudio requires `python >= 3.10`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
 
 :::::
 :::::{tab-item} Windows
@@ -51,7 +51,7 @@ For example:
 When updating, or if you close your terminal before you finish the installation and run your first `splatfacto`, you have to re-do this environment activation step.
 :::
 
-Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
+Nerfstudio requires `python >= 3.10`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
 
 :::::
 ::::::
@@ -59,7 +59,7 @@ Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependen
 ## Create environment
 
 ```bash
-conda create --name nerfstudio -y python=3.8
+conda create --name nerfstudio -y python=3.10
 conda activate nerfstudio
 python -m pip install --upgrade pip
 
@@ -79,6 +79,28 @@ pip uninstall torch torchvision functorch tinycudann
 ```
 
 ::::{tab-set}
+:::{tab-item} Torch 2.8+ with CUDA 12.8 (Blackwell / RTX 50-series)
+
+Required for NVIDIA Blackwell GPUs (RTX 50-series, compute capability sm_120), which need CUDA 12.8 or newer:
+
+```bash
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
+```
+
+To build the necessary CUDA extensions, `cuda-toolkit` 12.8 is also required. We
+recommend installing with conda:
+
+```bash
+conda install -c "nvidia/label/cuda-12.8.0" cuda-toolkit
+```
+
+When building CUDA extensions (gsplat, tiny-cuda-nn, ...) for a Blackwell GPU, make sure the architecture list includes sm_120:
+
+```bash
+export TORCH_CUDA_ARCH_LIST="12.0+PTX"
+```
+
+:::
 :::{tab-item} Torch 2.1.2 with CUDA 11.8 (recommended)
 
 Install PyTorch 2.1.2 with CUDA 11.8:

nerfstudio/data/utils/data_utils.py

@@ -36,24 +36,7 @@ def pil_to_numpy(im: PILImage) -> np.ndarray:
     # Load in image completely (PIL defaults to lazy loading)
     im.load()
 
-    # Unpack data
-    e = Image._getencoder(im.mode, "raw", im.mode)
-    e.setimage(im.im)
-
-    # NumPy buffer for the result
-    shape, typestr = Image._conv_type_shape(im)
-    data = np.empty(shape, dtype=np.dtype(typestr))
-    mem = data.data.cast("B", (data.data.nbytes,))
-
-    bufsize, s, offset = 65536, 0, 0
-    while not s:
-        _, s, d = e.encode(bufsize)
-        mem[offset : offset + len(d)] = d
-        offset += len(d)
-    if s < 0:
-        raise RuntimeError("encoder error %d in tobytes" % s)
-
-    return data
+    return np.asarray(im)
 
 
 def get_image_mask_tensor_from_path(filepath: Union[Path, IO[bytes]], scale_factor: float = 1.0) -> torch.Tensor:

nerfstudio/engine/trainer.py

@@ -429,7 +429,7 @@ class Trainer:
                 load_step = sorted(int(x[x.find("-") + 1 : x.find(".")]) for x in os.listdir(load_dir))[-1]
             load_path: Path = load_dir / f"step-{load_step:09d}.ckpt"
             assert load_path.exists(), f"Checkpoint {load_path} does not exist"
-            loaded_state = torch.load(load_path, map_location="cpu")
+            loaded_state = torch.load(load_path, map_location="cpu", weights_only=False)
             self._start_step = loaded_state["step"] + 1
             # load the checkpoints for pipeline, optimizers, and gradient scalar
             self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])
@@ -440,7 +440,7 @@ class Trainer:
             CONSOLE.print(f"Done loading Nerfstudio checkpoint from {load_path}")
         elif load_checkpoint is not None:
             assert load_checkpoint.exists(), f"Checkpoint {load_checkpoint} does not exist"
-            loaded_state = torch.load(load_checkpoint, map_location="cpu")
+            loaded_state = torch.load(load_checkpoint, map_location="cpu", weights_only=False)
             self._start_step = loaded_state["step"] + 1
             # load the checkpoints for pipeline, optimizers, and gradient scalar
             self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])

nerfstudio/field_components/activations.py

@@ -21,21 +21,21 @@ from typing import TYPE_CHECKING
 import torch
 from jaxtyping import Float
 from torch import Tensor
+from torch.amp import custom_bwd, custom_fwd
 from torch.autograd import Function
-from torch.cuda.amp import custom_bwd, custom_fwd
 
 
 class _TruncExp(Function):
     # Implementation from torch-ngp:
     # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
     @staticmethod
-    @custom_fwd(cast_inputs=torch.float32)
+    @custom_fwd(cast_inputs=torch.float32, device_type="cuda")
     def forward(ctx, x):
         ctx.save_for_backward(x)
         return torch.exp(x)
 
     @staticmethod
-    @custom_bwd
+    @custom_bwd(device_type="cuda")
     def backward(ctx, g):
         x = ctx.saved_tensors[0]
         return g * torch.exp(x.clamp(-15, 15))

nerfstudio/scripts/downloads/download_data.py

@@ -514,7 +514,7 @@ class Mill19Download(DatasetDownload):
             split_filepaths = []
             for image_path, new_image_path in copied_images.items():
                 metadata_path = image_path.parent.parent / "metadata" / f"{image_path.stem}.pt"
-                metadata = torch.load(metadata_path, map_location="cpu")
+                metadata = torch.load(metadata_path, map_location="cpu", weights_only=False)
                 c2w = torch.eye(4)
                 c2w[:3] = metadata["c2w"]
                 file_path = str(Path("images") / f"{new_image_path.name}")

nerfstudio/utils/eval_utils.py

@@ -59,7 +59,7 @@ def eval_load_checkpoint(config: TrainerConfig, pipeline: Pipeline) -> Tuple[Pat
         load_step = config.load_step
     load_path = config.load_dir / f"step-{load_step:09d}.ckpt"
     assert load_path.exists(), f"Checkpoint {load_path} does not exist"
-    loaded_state = torch.load(load_path, map_location="cpu")
+    loaded_state = torch.load(load_path, map_location="cpu", weights_only=False)
     pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])
     CONSOLE.print(f":white_check_mark: Done loading checkpoint from {load_path}")
     return load_path, load_step

pyproject.toml

@@ -8,7 +8,7 @@ version = "1.1.5"
 description = "All-in-one repository for state-of-the-art NeRFs"
 readme = "README.md"
 license = { text="Apache 2.0"}
-requires-python = ">=3.8.0"
+requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Programming Language :: Python",
@@ -31,7 +31,7 @@ dependencies = [
     "mediapy>=1.1.0",
     "msgpack>=1.0.4",
     "msgpack_numpy>=0.4.8",
-    "nerfacc==0.5.2",
+    "nerfacc>=0.5.3",
     "open3d>=0.16.0",
     "opencv-python-headless==4.10.0.84",
     "Pillow>=10.3.0",
@@ -64,7 +64,7 @@ dependencies = [
     "xatlas",
     "trimesh>=3.20.2",
     "timm==0.6.7",
-    "gsplat==1.4.0",
+    "gsplat>=1.5.3",
     "pytorch-msssim",
     "pathos",
     "packaging",