ruff

app.py

@@ -2,18 +2,16 @@ import os
 import shlex
 import shutil
 import subprocess
-from typing import *
+from typing import Literal
 
 os.environ["SPCONV_ALGO"] = "native"
 
 if os.getenv("SPACE_ID"):
-    subprocess.run(
-        shlex.split(
-            "pip install wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"
-        ),
+    subprocess.run(  # noqa: S603
+        shlex.split("pip install wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"),
         check=True,
     )
-    subprocess.run(
+    subprocess.run(  # noqa: S603
         shlex.split("pip install wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl"),
         check=True,
     )
@@ -46,8 +44,7 @@ def end_session(req: gr.Request):
 
 
 def preprocess_image(image: Image.Image) -> Image.Image:
-    """
-    Preprocess the input image.
+    """Preprocess the input image.
 
     Args:
         image (Image.Image): The input image.
@@ -59,9 +56,8 @@ def preprocess_image(image: Image.Image) -> Image.Image:
     return processed_image
 
 
-def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
-    """
-    Preprocess a list of input images.
+def preprocess_images(images: list[tuple[Image.Image, str]]) -> list[Image.Image]:
+    """Preprocess a list of input images.
 
     Args:
         images (List[Tuple[Image.Image, str]]): The input images.
@@ -91,7 +87,7 @@ def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     }
 
 
-def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
+def unpack_state(state: dict) -> tuple[Gaussian, edict, str]:
     gs = Gaussian(
         aabb=state["gaussian"]["aabb"],
         sh_degree=state["gaussian"]["sh_degree"],
@@ -115,16 +111,14 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
 
 
 def get_seed(randomize_seed: bool, seed: int) -> int:
-    """
-    Get the random seed.
-    """
+    """Get the random seed."""
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
 
 @spaces.GPU
 def image_to_3d(
     image: Image.Image,
-    multiimages: List[Tuple[Image.Image, str]],
+    multiimages: list[tuple[Image.Image, str]],
     is_multiimage: bool,
     seed: int,
     ss_guidance_strength: float,
@@ -133,9 +127,8 @@ def image_to_3d(
     slat_sampling_steps: int,
     multiimage_algo: Literal["multidiffusion", "stochastic"],
     req: gr.Request,
-) -> Tuple[dict, str]:
-    """
-    Convert an image to a 3D model.
+) -> tuple[dict, str]:
+    """Convert an image to a 3D model.
 
     Args:
         image (Image.Image): The input image.
@@ -186,9 +179,7 @@ def image_to_3d(
         )
     video = render_utils.render_video(outputs["gaussian"][0], num_frames=120)["color"]
     video_geo = render_utils.render_video(outputs["mesh"][0], num_frames=120)["normal"]
-    video = [
-        np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))
-    ]
+    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
     video_path = os.path.join(user_dir, "sample.mp4")
     imageio.mimsave(video_path, video, fps=15)
     state = pack_state(outputs["gaussian"][0], outputs["mesh"][0])
@@ -202,9 +193,8 @@ def extract_glb(
     mesh_simplify: float,
     texture_size: int,
     req: gr.Request,
-) -> Tuple[str, str]:
-    """
-    Extract a GLB file from the 3D model.
+) -> tuple[str, str]:
+    """Extract a GLB file from the 3D model.
 
     Args:
         state (dict): The state of the generated 3D model.
@@ -216,9 +206,7 @@ def extract_glb(
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, mesh = unpack_state(state)
-    glb = postprocessing_utils.to_glb(
-        gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False
-    )
+    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
     glb_path = os.path.join(user_dir, "sample.glb")
     glb.export(glb_path)
     torch.cuda.empty_cache()
@@ -226,9 +214,8 @@ def extract_glb(
 
 
 @spaces.GPU
-def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-    """
-    Extract a Gaussian file from the 3D model.
+def extract_gaussian(state: dict, req: gr.Request) -> tuple[str, str]:
+    """Extract a Gaussian file from the 3D model.
 
     Args:
         state (dict): The state of the generated 3D model.
@@ -244,10 +231,8 @@ def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     return gaussian_path, gaussian_path
 
 
-def prepare_multi_example() -> List[Image.Image]:
-    multi_case = list(
-        set([i.split("_")[0] for i in os.listdir("assets/example_multi_image")])
-    )
+def prepare_multi_example() -> list[Image.Image]:
+    multi_case = list(set([i.split("_")[0] for i in os.listdir("assets/example_multi_image")]))
     images = []
     for case in multi_case:
         _images = []
@@ -260,17 +245,15 @@ def prepare_multi_example() -> List[Image.Image]:
     return images
 
 
-def split_image(image: Image.Image) -> List[Image.Image]:
-    """
-    Split an image into multiple views.
-    """
+def split_image(image: Image.Image) -> list[Image.Image]:
+    """Split an image into multiple views."""
     image = np.array(image)
     alpha = image[..., 3]
     alpha = np.any(alpha > 0, axis=0)
     start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
     end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
     images = []
-    for s, e in zip(start_pos, end_pos):
+    for s, e in zip(start_pos, end_pos, strict=False):
         images.append(Image.fromarray(image[:, s : e + 1]))
     return [preprocess_image(image) for image in images]
 
@@ -280,7 +263,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
     * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
     * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
-    
+
     ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
     """)
 
@@ -304,8 +287,8 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                         columns=3,
                     )
                     gr.Markdown("""
-                        Input different views of the object in separate images. 
-                        
+                        Input different views of the object in separate images.
+
                         *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
                     """)
 
@@ -314,20 +297,12 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                 randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                 gr.Markdown("Stage 1: Sparse Structure Generation")
                 with gr.Row():
-                    ss_guidance_strength = gr.Slider(
-                        0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1
-                    )
-                    ss_sampling_steps = gr.Slider(
-                        1, 50, label="Sampling Steps", value=12, step=1
-                    )
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
                 gr.Markdown("Stage 2: Structured Latent Generation")
                 with gr.Row():
-                    slat_guidance_strength = gr.Slider(
-                        0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1
-                    )
-                    slat_sampling_steps = gr.Slider(
-                        1, 50, label="Sampling Steps", value=12, step=1
-                    )
+                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
+                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
                 multiimage_algo = gr.Radio(
                     ["stochastic", "multidiffusion"],
                     label="Multi-image Algorithm",
@@ -337,12 +312,8 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
             generate_btn = gr.Button("Generate")
 
             with gr.Accordion(label="GLB Extraction Settings", open=False):
-                mesh_simplify = gr.Slider(
-                    0.9, 0.98, label="Simplify", value=0.95, step=0.01
-                )
-                texture_size = gr.Slider(
-                    512, 2048, label="Texture Size", value=1024, step=512
-                )
+                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
+                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
 
             with gr.Row():
                 extract_glb_btn = gr.Button("Extract GLB", interactive=False)
@@ -352,18 +323,12 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                         """)
 
         with gr.Column():
-            video_output = gr.Video(
-                label="Generated 3D Asset", autoplay=True, loop=True, height=300
-            )
+            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
             model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300)
 
             with gr.Row():
-                download_glb = gr.DownloadButton(
-                    label="Download GLB", interactive=False
-                )
-                download_gs = gr.DownloadButton(
-                    label="Download Gaussian", interactive=False
-                )
+                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
+                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
 
     is_multiimage = gr.State(False)
     output_buf = gr.State()
@@ -371,10 +336,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     # Example images at the bottom of the page
     with gr.Row() as single_image_example:
         examples = gr.Examples(
-            examples=[
-                f"assets/example_image/{image}"
-                for image in os.listdir("assets/example_image")
-            ],
+            examples=[f"assets/example_image/{image}" for image in os.listdir("assets/example_image")],
             inputs=[image_prompt],
             fn=preprocess_image,
             outputs=[image_prompt],
@@ -396,15 +358,11 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     demo.unload(end_session)
 
     single_image_input_tab.select(
-        lambda: tuple(
-            [False, gr.Row.update(visible=True), gr.Row.update(visible=False)]
-        ),
+        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
         outputs=[is_multiimage, single_image_example, multiimage_example],
     )
     multiimage_input_tab.select(
-        lambda: tuple(
-            [True, gr.Row.update(visible=False), gr.Row.update(visible=True)]
-        ),
+        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
         outputs=[is_multiimage, single_image_example, multiimage_example],
     )
 
@@ -476,9 +434,7 @@ if __name__ == "__main__":
     pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
     pipeline.cuda()
     try:
-        pipeline.preprocess_image(
-            Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))
-        )  # Preload rembg
+        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))  # Preload rembg
     except:
         pass
     demo.launch(mcp_server=True)

pyproject.toml

@@ -42,43 +42,44 @@ dev = [
     "setuptools>=80.8.0",
 ]
 
-#[tool.ruff]
-#line-length = 119
-#
-#[tool.ruff.lint]
-#select = ["ALL"]
-#ignore = [
-#    "COM812", # missing-trailing-comma
-#    "D203",   # one-blank-line-before-class
-#    "D213",   # multi-line-summary-second-line
-#    "E501",   # line-too-long
-#    "SIM117", # multiple-with-statements
-#    #
-#    "D100",    # undocumented-public-module
-#    "D101",    # undocumented-public-class
-#    "D102",    # undocumented-public-method
-#    "D103",    # undocumented-public-function
-#    "D104",    # undocumented-public-package
-#    "D105",    # undocumented-magic-method
-#    "D107",    # undocumented-public-init
-#    "EM101",   # raw-string-in-exception
-#    "FBT001",  # boolean-type-hint-positional-argument
-#    "FBT002",  # boolean-default-value-positional-argument
-#    "PD901",   # pandas-df-variable-name
-#    "PGH003",  # blanket-type-ignore
-#    "PLR0913", # too-many-arguments
-#    "PLR0915", # too-many-statements
-#    "TRY003",  # raise-vanilla-args
-#]
-#unfixable = [
-#    "F401", # unused-import
-#]
-#
-#[tool.ruff.lint.pydocstyle]
-#convention = "google"
-#
-#[tool.ruff.lint.per-file-ignores]
-#"*.ipynb" = ["T201", "T203"]
-#
-#[tool.ruff.format]
-#docstring-code-format = true
+[tool.ruff]
+line-length = 119
+exclude = ["trellis", "extensions"]
+
+[tool.ruff.lint]
+select = ["ALL"]
+ignore = [
+    "COM812", # missing-trailing-comma
+    "D203",   # one-blank-line-before-class
+    "D213",   # multi-line-summary-second-line
+    "E501",   # line-too-long
+    "SIM117", # multiple-with-statements
+    #
+    "D100",    # undocumented-public-module
+    "D101",    # undocumented-public-class
+    "D102",    # undocumented-public-method
+    "D103",    # undocumented-public-function
+    "D104",    # undocumented-public-package
+    "D105",    # undocumented-magic-method
+    "D107",    # undocumented-public-init
+    "EM101",   # raw-string-in-exception
+    "FBT001",  # boolean-type-hint-positional-argument
+    "FBT002",  # boolean-default-value-positional-argument
+    "PD901",   # pandas-df-variable-name
+    "PGH003",  # blanket-type-ignore
+    "PLR0913", # too-many-arguments
+    "PLR0915", # too-many-statements
+    "TRY003",  # raise-vanilla-args
+]
+unfixable = [
+    "F401", # unused-import
+]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.per-file-ignores]
+"*.ipynb" = ["T201", "T203"]
+
+[tool.ruff.format]
+docstring-code-format = true