Feature/formatting

README.md

@@ -163,6 +163,32 @@ If you find this work helpful, please consider citing our technical report:
 }
 ```
 
+## Contributing
+
+We welcome contributions to Cube! Here's how you can set up your development environment and follow our code style guidelines:
+
+### Development Setup
+
+1. Clone the repository and install development dependencies:
+
+```bash
+git clone https://github.com/Roblox/cube.git
+cd cube
+pip install -e ".[dev]"
+```
+
+### Code Formatting and Linting
+
+We use [ruff](https://github.com/astral-sh/ruff) for both code formatting and linting to maintain consistent code style and quality.
+
+To run the formatter and linter:
+
+```bash
+./format.sh
+```
+
+Please format your code before submitting pull requests.
+
 ## Acknowledgements
 
 We would like to thank the contributors of [TRELLIS](https://github.com/microsoft/TRELLIS), [CraftsMan3D](https://github.com/wyysf-98/CraftsMan3D), [threestudio](https://github.com/threestudio-project/threestudio), [Hunyuan3D-2](https://github.com/Tencent/Hunyuan3D-2), [minGPT](https://github.com/karpathy/minGPT), [dinov2](https://github.com/facebookresearch/dinov2), [OptVQ](https://github.com/zbr17/OptVQ), [1d-tokenizer](https://github.com/bytedance/1d-tokenizer)

cube3d/generate.py

@@ -13,6 +13,7 @@
 )
 from cube3d.renderer import renderer
 
+
 def generate_mesh(
     engine,
     prompt,
@@ -127,7 +128,7 @@ def generate_mesh(
         engine = Engine(
             args.config_path, args.gpt_ckpt_path, args.shape_ckpt_path, device=device
         )
-    
+
     # Generate meshes based on input source
     obj_path = generate_mesh(
         engine,

cube3d/inference/engine.py

@@ -194,7 +194,7 @@ def run_gpt(
                 embed.device,
             )
         with torch.autocast(self.device.type, dtype=torch.bfloat16):
-            for i in tqdm(range(self.max_new_tokens), desc=f"generating"):
+            for i in tqdm(range(self.max_new_tokens), desc="generating"):
                 curr_pos_id = torch.tensor([i], dtype=torch.long, device=embed.device)
                 logits = self.gpt_model(
                     embed_buffer,
@@ -276,7 +276,7 @@ def t2s(
             guidance_scale (float, optional): The scale of guidance for the GPT model. Default is 3.0.
             resolution_base (float, optional): The base resolution for the shape decoder. Default is 8.0.
             chunk_size (int, optional): The chunk size for processing the shape decoding. Default is 100,000.
-            top_p (float, optional): The cumulative probability threshold for nucleus sampling. 
+            top_p (float, optional): The cumulative probability threshold for nucleus sampling.
                                     If None, argmax selection is performed (deterministic generation). Otherwise, smallest set of tokens with cumulative probability ≥ top_p are kept (stochastic generation).
         Returns:
             mesh_v_f: The generated 3D mesh vertices and faces.
@@ -304,9 +304,9 @@ def __init__(
             device (torch.device): The device to run the inference on (e.g., CPU or CUDA).
         """
 
-        assert (
-            device.type == "cuda"
-        ), "EngineFast is only supported on cuda devices, please use Engine on non-cuda devices"
+        assert device.type == "cuda", (
+            "EngineFast is only supported on cuda devices, please use Engine on non-cuda devices"
+        )
 
         super().__init__(config_path, gpt_ckpt_path, shape_ckpt_path, device)
 
@@ -428,11 +428,11 @@ def _set_curr_pos_id(self, pos: int):
         )
 
     def run_gpt(
-        self, 
-        prompts: list[str], 
-        use_kv_cache: bool, 
+        self,
+        prompts: list[str],
+        use_kv_cache: bool,
         guidance_scale: float = 3.0,
-        top_p: float = None
+        top_p: float = None,
     ):
         """
         Runs the GPT model to generate text based on the provided prompts.
@@ -479,9 +479,7 @@ def run_gpt(
             next_embed = next_embed.repeat(2, 1, 1)
             self.embed_buffer[:, input_seq_len, :].copy_(next_embed.squeeze(1))
 
-            for i in tqdm(
-                range(1, self.max_new_tokens), desc=f"generating"
-            ):
+            for i in tqdm(range(1, self.max_new_tokens), desc="generating"):
                 self._set_curr_pos_id(i)
                 self.graph.replay()
 

cube3d/inference/logits_postprocesses.py

@@ -30,9 +30,9 @@ def top_p_filtering(logits, top_p: float = 1.0):
 
 
 def process_logits(
-        logits,
-        top_p: float = None,
-    ):
+    logits,
+    top_p: float = None,
+):
     """
     Process logits by optionally applying nucleus (top-p) filtering and token selection.
 

cube3d/inference/utils.py

@@ -1,5 +1,4 @@
-import logging
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from omegaconf import DictConfig, OmegaConf

cube3d/model/autoencoder/one_d_autoencoder.py

@@ -1,5 +1,4 @@
 import logging
-import sys
 from dataclasses import dataclass, field
 from functools import partial
 from typing import List, Optional, Tuple
@@ -632,7 +631,7 @@ def extract_geometry(
 
         progress_bar = tqdm(
             range(0, xyz_samples.shape[0], chunk_size),
-            desc=f"extracting geometry",
+            desc="extracting geometry",
             unit="chunk",
         )
         for start in progress_bar:

cube3d/model/autoencoder/spherical_vq.py

@@ -1,4 +1,3 @@
-import sys
 from typing import Literal, Optional
 
 import torch
@@ -62,7 +61,6 @@ def get_codebook(self):
         return self.norm(self.cb_norm(self.codebook.weight))
 
     @torch.no_grad()
-
     def lookup_codebook(self, q: torch.Tensor):
         """
         Perform a lookup in the codebook and process the result.

cube3d/renderer/blender_script.py

@@ -556,7 +556,7 @@ def enable_gpus(device_type, use_cpus=False):
         if device_type == "CPU":
             return []
         else:
-            raise RuntimeError(f"No devices detected, set use_cpus to True")
+            raise RuntimeError("No devices detected, set use_cpus to True")
 
     assert device_type in [
         "CUDA",

format.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+set -e
+
+python -m ruff format .
+python -m ruff check . --fix
+# Sort imports
+python -m ruff check . --select I --fix

pyproject.toml

@@ -30,9 +30,30 @@ dependencies = [
 ]
 [project.optional-dependencies]
 meshlab = ["pymeshlab"]
-lint = ["ruff==0.9.10"]
+dev = ["ruff==0.9.10"]
 
 [tool.setuptools.packages.find]
 where = ["cube3d"]
 include = ["cube/*"]
 namespaces = false
+
+[tool.ruff]
+line-length = 88
+indent-width = 4
+
+exclude = [
+    ".git",
+    ".ruff_cache",
+    ".venv",
+    "build",
+    "dist",
+    "*.ipynb"
+]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+line-ending = "auto"
+
+[tool.ruff.lint]
+ignore = ["E741", "E722"]