Add files

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.11.11
+    hooks:
+      - id: ruff-check
+        args: ["--fix"]
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.15.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-pytz",
+            "types-PyYAML",
+            "types-requests",
+          ]

.python-version

@@ -0,0 +1 @@
+3.10

.vscode/extensions.json

@@ -0,0 +1,8 @@
+{
+    "recommendations": [
+        "ms-python.python",
+        "charliermarsh.ruff",
+        "streetsidesoftware.code-spell-checker",
+        "tamasfe.even-better-toml"
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,17 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "charliermarsh.ruff",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.fixAll.ruff": "explicit",
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "notebook.output.scrolling": true,
+    "notebook.formatOnSave.enabled": true
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🏢
 colorFrom: indigo
 colorTo: blue
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 5.32.0
 app_file: app.py
 pinned: false
 license: mit
@@ -13,4 +13,4 @@ short_description: Scalable and Versatile 3D Generation from images
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-Paper: https://huggingface.co/papers/2412.01506
+Paper: https://huggingface.co/papers/2412.01506

app.py

@@ -1,31 +1,45 @@
-import gradio as gr
-import spaces
-from gradio_litmodel3d import LitModel3D
-
 import os
+import shlex
 import shutil
-os.environ['SPCONV_ALGO'] = 'native'
+import subprocess
 from typing import *
-import torch
-import numpy as np
+
+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"
+        ),
+        check=True,
+    )
+    subprocess.run(
+        shlex.split("pip install wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl"),
+        check=True,
+    )
+
+import gradio as gr
 import imageio
+import numpy as np
+import spaces
+import torch
 from easydict import EasyDict as edict
 from PIL import Image
+
 from trellis.pipelines import TrellisImageTo3DPipeline
 from trellis.representations import Gaussian, MeshExtractResult
-from trellis.utils import render_utils, postprocessing_utils
-
+from trellis.utils import postprocessing_utils, render_utils
 
 MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp")
 os.makedirs(TMP_DIR, exist_ok=True)
 
 
 def start_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     os.makedirs(user_dir, exist_ok=True)
-    
-    
+
+
 def end_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     shutil.rmtree(user_dir)
@@ -48,10 +62,10 @@ def preprocess_image(image: Image.Image) -> Image.Image:
 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.
-        
+
     Returns:
         List[Image.Image]: The preprocessed images.
     """
@@ -62,41 +76,41 @@ def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image
 
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     return {
-        'gaussian': {
+        "gaussian": {
             **gs.init_params,
-            '_xyz': gs._xyz.cpu().numpy(),
-            '_features_dc': gs._features_dc.cpu().numpy(),
-            '_scaling': gs._scaling.cpu().numpy(),
-            '_rotation': gs._rotation.cpu().numpy(),
-            '_opacity': gs._opacity.cpu().numpy(),
+            "_xyz": gs._xyz.cpu().numpy(),
+            "_features_dc": gs._features_dc.cpu().numpy(),
+            "_scaling": gs._scaling.cpu().numpy(),
+            "_rotation": gs._rotation.cpu().numpy(),
+            "_opacity": gs._opacity.cpu().numpy(),
         },
-        'mesh': {
-            'vertices': mesh.vertices.cpu().numpy(),
-            'faces': mesh.faces.cpu().numpy(),
+        "mesh": {
+            "vertices": mesh.vertices.cpu().numpy(),
+            "faces": mesh.faces.cpu().numpy(),
         },
     }
-    
-    
+
+
 def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     gs = Gaussian(
-        aabb=state['gaussian']['aabb'],
-        sh_degree=state['gaussian']['sh_degree'],
-        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
-        scaling_bias=state['gaussian']['scaling_bias'],
-        opacity_bias=state['gaussian']['opacity_bias'],
-        scaling_activation=state['gaussian']['scaling_activation'],
+        aabb=state["gaussian"]["aabb"],
+        sh_degree=state["gaussian"]["sh_degree"],
+        mininum_kernel_size=state["gaussian"]["mininum_kernel_size"],
+        scaling_bias=state["gaussian"]["scaling_bias"],
+        opacity_bias=state["gaussian"]["opacity_bias"],
+        scaling_activation=state["gaussian"]["scaling_activation"],
     )
-    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
-    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
-    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
-    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
-    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
-    
+    gs._xyz = torch.tensor(state["gaussian"]["_xyz"], device="cuda")
+    gs._features_dc = torch.tensor(state["gaussian"]["_features_dc"], device="cuda")
+    gs._scaling = torch.tensor(state["gaussian"]["_scaling"], device="cuda")
+    gs._rotation = torch.tensor(state["gaussian"]["_rotation"], device="cuda")
+    gs._opacity = torch.tensor(state["gaussian"]["_opacity"], device="cuda")
+
     mesh = edict(
-        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
-        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
+        vertices=torch.tensor(state["mesh"]["vertices"], device="cuda"),
+        faces=torch.tensor(state["mesh"]["faces"], device="cuda"),
     )
-    
+
     return gs, mesh
 
 
@@ -170,12 +184,14 @@ def image_to_3d(
             },
             mode=multiimage_algo,
         )
-    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_path = os.path.join(user_dir, 'sample.mp4')
+    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_path = os.path.join(user_dir, "sample.mp4")
     imageio.mimsave(video_path, video, fps=15)
-    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
+    state = pack_state(outputs["gaussian"][0], outputs["mesh"][0])
     torch.cuda.empty_cache()
     return state, video_path
 
@@ -200,8 +216,10 @@ 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_path = os.path.join(user_dir, 'sample.glb')
+    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()
     return glb_path, glb_path
@@ -220,19 +238,21 @@ def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, _ = unpack_state(state)
-    gaussian_path = os.path.join(user_dir, 'sample.ply')
+    gaussian_path = os.path.join(user_dir, "sample.ply")
     gs.save_ply(gaussian_path)
     torch.cuda.empty_cache()
     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")]))
+    multi_case = list(
+        set([i.split("_")[0] for i in os.listdir("assets/example_multi_image")])
+    )
     images = []
     for case in multi_case:
         _images = []
         for i in range(1, 4):
-            img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
+            img = Image.open(f"assets/example_multi_image/{case}_{i}.png")
             W, H = img.size
             img = img.resize((int(W / H * 512), 512))
             _images.append(np.array(img))
@@ -246,12 +266,12 @@ def split_image(image: Image.Image) -> List[Image.Image]:
     """
     image = np.array(image)
     alpha = image[..., 3]
-    alpha = np.any(alpha>0, axis=0)
+    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):
-        images.append(Image.fromarray(image[:, s:e+1]))
+        images.append(Image.fromarray(image[:, s : e + 1]))
     return [preprocess_image(image) for image in images]
 
 
@@ -263,39 +283,67 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     
     ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
     """)
-    
+
     with gr.Row():
         with gr.Column():
             with gr.Tabs() as input_tabs:
                 with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
-                    image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
+                    image_prompt = gr.Image(
+                        label="Image Prompt",
+                        format="png",
+                        image_mode="RGBA",
+                        type="pil",
+                        height=300,
+                    )
                 with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
-                    multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
+                    multiimage_prompt = gr.Gallery(
+                        label="Image Prompt",
+                        format="png",
+                        type="pil",
+                        height=300,
+                        columns=3,
+                    )
                     gr.Markdown("""
                         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.*
                     """)
-            
+
             with gr.Accordion(label="Generation Settings", open=False):
                 seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                 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)
-                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
+                    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",
+                    value="stochastic",
+                )
 
             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)
                 extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
@@ -304,13 +352,19 @@ 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)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, 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()
 
@@ -318,7 +372,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     with gr.Row() as single_image_example:
         examples = gr.Examples(
             examples=[
-                f'assets/example_image/{image}'
+                f"assets/example_image/{image}"
                 for image in os.listdir("assets/example_image")
             ],
             inputs=[image_prompt],
@@ -340,16 +394,20 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     # Handlers
     demo.load(start_session)
     demo.unload(end_session)
-    
+
     single_image_input_tab.select(
-        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
+        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)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
+        lambda: tuple(
+            [True, gr.Row.update(visible=False), gr.Row.update(visible=True)]
+        ),
+        outputs=[is_multiimage, single_image_example, multiimage_example],
     )
-    
+
     image_prompt.upload(
         preprocess_image,
         inputs=[image_prompt],
@@ -367,7 +425,17 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         outputs=[seed],
     ).then(
         image_to_3d,
-        inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
+        inputs=[
+            image_prompt,
+            multiimage_prompt,
+            is_multiimage,
+            seed,
+            ss_guidance_strength,
+            ss_sampling_steps,
+            slat_guidance_strength,
+            slat_sampling_steps,
+            multiimage_algo,
+        ],
         outputs=[output_buf, video_output],
     ).then(
         lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
@@ -387,7 +455,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         lambda: gr.Button(interactive=True),
         outputs=[download_glb],
     )
-    
+
     extract_gs_btn.click(
         extract_gaussian,
         inputs=[output_buf],
@@ -401,14 +469,16 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         lambda: gr.Button(interactive=False),
         outputs=[download_glb],
     )
-    
+
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    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()
+    demo.launch(mcp_server=True)

pyproject.toml

@@ -0,0 +1,84 @@
+[project]
+name = "trellis"
+version = "0.1.0"
+description = ""
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "easydict>=1.13",
+    "flash-attn",
+    "gradio[mcp]>=5.32.0",
+    "hf-transfer>=0.1.9",
+    "hf-xet>=1.1.2",
+    "igraph>=0.11.8",
+    "imageio[ffmpeg]>=2.37.0",
+    "onnxruntime>=1.22.0",
+    "opencv-python-headless>=4.11.0.86",
+    "pymeshfix>=0.17.1",
+    "pyvista>=0.45.2",
+    "rembg>=2.0.66",
+    "scipy>=1.15.3",
+    "spaces>=0.36.0",
+    "spconv-cu120>=2.3.6",
+    "torch==2.4.0",
+    "torchvision>=0.19.0",
+    "transformers>=4.52.3",
+    "trimesh>=4.6.10",
+    "utils3d",
+    "xatlas>=0.0.10",
+    "xformers>=0.0.27.post2",
+]
+
+[tool.uv.sources]
+flash-attn = { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post2/flash_attn-2.7.0.post2+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl" }
+utils3d = { git = "https://github.com/EasternJournalist/utils3d.git", rev = "9a4eb15e4021b67b12c460c7057d642626897ec8" }
+diff-gaussian-rasterization = { path = "wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl" }
+nvdiffrast = { path = "wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl" }
+
+[dependency-groups]
+dev = [
+    "diff-gaussian-rasterization",
+    "nvdiffrast",
+    "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

requirements.txt

@@ -1,26 +1,456 @@
---extra-index-url https://download.pytorch.org/whl/cu121
-
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml -o requirements.txt
+aiofiles==24.1.0
+    # via gradio
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.9.0
+    # via
+    #   gradio
+    #   httpx
+    #   mcp
+    #   sse-starlette
+    #   starlette
+attrs==25.3.0
+    # via
+    #   jsonschema
+    #   referencing
+ccimport==0.4.4
+    # via
+    #   pccm
+    #   spconv-cu120
+certifi==2025.4.26
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.2
+    # via requests
+click==8.2.1
+    # via
+    #   typer
+    #   uvicorn
+coloredlogs==15.0.1
+    # via onnxruntime
+contourpy==1.3.2
+    # via matplotlib
+cumm-cu120==0.4.11
+    # via spconv-cu120
+cycler==0.12.1
+    # via matplotlib
+easydict==1.13
+    # via trellis (pyproject.toml)
+einops==0.8.1
+    # via flash-attn
+exceptiongroup==1.3.0
+    # via anyio
+fastapi==0.115.12
+    # via gradio
+ffmpy==0.5.0
+    # via gradio
+filelock==3.18.0
+    # via
+    #   huggingface-hub
+    #   torch
+    #   transformers
+    #   triton
+fire==0.7.0
+    # via
+    #   cumm-cu120
+    #   pccm
+    #   spconv-cu120
+flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post2/flash_attn-2.7.0.post2+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+    # via trellis (pyproject.toml)
+flatbuffers==25.2.10
+    # via onnxruntime
+fonttools==4.58.1
+    # via matplotlib
+fsspec==2025.5.1
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+glcontext==3.0.0
+    # via moderngl
+gradio==5.32.0
+    # via
+    #   trellis (pyproject.toml)
+    #   spaces
+gradio-client==1.10.2
+    # via gradio
+groovy==0.1.2
+    # via gradio
+h11==0.16.0
+    # via
+    #   httpcore
+    #   uvicorn
+hf-transfer==0.1.9
+    # via trellis (pyproject.toml)
+hf-xet==1.1.2
+    # via
+    #   trellis (pyproject.toml)
+    #   huggingface-hub
+httpcore==1.0.9
+    # via httpx
+httpx==0.28.1
+    # via
+    #   gradio
+    #   gradio-client
+    #   mcp
+    #   safehttpx
+    #   spaces
+httpx-sse==0.4.0
+    # via mcp
+huggingface-hub==0.32.3
+    # via
+    #   gradio
+    #   gradio-client
+    #   tokenizers
+    #   transformers
+humanfriendly==10.0
+    # via coloredlogs
+idna==3.10
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+igraph==0.11.8
+    # via trellis (pyproject.toml)
+imageio==2.37.0
+    # via
+    #   trellis (pyproject.toml)
+    #   scikit-image
+imageio-ffmpeg==0.5.1
+    # via imageio
+jinja2==3.1.6
+    # via
+    #   gradio
+    #   torch
+jsonschema==4.24.0
+    # via rembg
+jsonschema-specifications==2025.4.1
+    # via jsonschema
+kiwisolver==1.4.8
+    # via matplotlib
+lark==1.2.2
+    # via pccm
+lazy-loader==0.4
+    # via scikit-image
+llvmlite==0.44.0
+    # via numba
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==3.0.2
+    # via
+    #   gradio
+    #   jinja2
+matplotlib==3.10.3
+    # via
+    #   pyvista
+    #   vtk
+mcp==1.9.0
+    # via gradio
+mdurl==0.1.2
+    # via markdown-it-py
+moderngl==5.12.0
+    # via utils3d
+mpmath==1.3.0
+    # via sympy
+networkx==3.4.2
+    # via
+    #   scikit-image
+    #   torch
+ninja==1.11.1.4
+    # via ccimport
+numba==0.61.2
+    # via pymatting
+numpy==2.2.6
+    # via
+    #   contourpy
+    #   cumm-cu120
+    #   gradio
+    #   imageio
+    #   matplotlib
+    #   numba
+    #   onnxruntime
+    #   opencv-python-headless
+    #   pandas
+    #   plyfile
+    #   pymatting
+    #   pymeshfix
+    #   pyvista
+    #   rembg
+    #   scikit-image
+    #   scipy
+    #   spconv-cu120
+    #   tifffile
+    #   torchvision
+    #   transformers
+    #   trimesh
+    #   utils3d
+    #   xformers
+nvidia-cublas-cu12==12.1.3.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-nccl-cu12==2.20.5
+    # via torch
+nvidia-nvjitlink-cu12==12.9.41
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
+onnxruntime==1.22.0
+    # via trellis (pyproject.toml)
+opencv-python-headless==4.11.0.86
+    # via
+    #   trellis (pyproject.toml)
+    #   rembg
+orjson==3.10.18
+    # via gradio
+packaging==25.0
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   lazy-loader
+    #   matplotlib
+    #   onnxruntime
+    #   pooch
+    #   scikit-image
+    #   spaces
+    #   transformers
+pandas==2.2.3
+    # via gradio
+pccm==0.4.16
+    # via
+    #   cumm-cu120
+    #   spconv-cu120
+pillow==10.4.0
+    # via
+    #   gradio
+    #   imageio
+    #   matplotlib
+    #   pymatting
+    #   pyvista
+    #   rembg
+    #   scikit-image
+    #   torchvision
+platformdirs==4.3.8
+    # via pooch
+plyfile==1.1
+    # via utils3d
+pooch==1.8.2
+    # via
+    #   pyvista
+    #   rembg
+portalocker==3.1.1
+    # via pccm
+protobuf==6.31.1
+    # via onnxruntime
+psutil==5.9.8
+    # via
+    #   imageio
+    #   spaces
+pybind11==2.13.6
+    # via
+    #   ccimport
+    #   cumm-cu120
+    #   pccm
+    #   spconv-cu120
+pydantic==2.11.5
+    # via
+    #   fastapi
+    #   gradio
+    #   mcp
+    #   pydantic-settings
+    #   spaces
+pydantic-core==2.33.2
+    # via pydantic
+pydantic-settings==2.9.1
+    # via mcp
+pydub==0.25.1
+    # via gradio
+pygments==2.19.1
+    # via rich
+pymatting==1.1.14
+    # via rembg
+pymeshfix==0.17.1
+    # via trellis (pyproject.toml)
+pyparsing==3.2.3
+    # via matplotlib
+python-dateutil==2.9.0.post0
+    # via
+    #   matplotlib
+    #   pandas
+python-dotenv==1.1.0
+    # via pydantic-settings
+python-multipart==0.0.20
+    # via
+    #   gradio
+    #   mcp
+pytz==2025.2
+    # via pandas
+pyvista==0.45.2
+    # via
+    #   trellis (pyproject.toml)
+    #   pymeshfix
+pyyaml==6.0.2
+    # via
+    #   gradio
+    #   huggingface-hub
+    #   transformers
+referencing==0.36.2
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+regex==2024.11.6
+    # via transformers
+rembg==2.0.66
+    # via trellis (pyproject.toml)
+requests==2.32.3
+    # via
+    #   ccimport
+    #   huggingface-hub
+    #   pooch
+    #   spaces
+    #   transformers
+rich==14.0.0
+    # via typer
+rpds-py==0.25.1
+    # via
+    #   jsonschema
+    #   referencing
+ruff==0.11.12
+    # via gradio
+safehttpx==0.1.6
+    # via gradio
+safetensors==0.5.3
+    # via transformers
+scikit-image==0.25.2
+    # via rembg
+scipy==1.15.3
+    # via
+    #   trellis (pyproject.toml)
+    #   pymatting
+    #   rembg
+    #   scikit-image
+    #   utils3d
+scooby==0.10.1
+    # via pyvista
+semantic-version==2.10.0
+    # via gradio
+setuptools==80.9.0
+    # via imageio-ffmpeg
+shellingham==1.5.4
+    # via typer
+six==1.17.0
+    # via python-dateutil
+sniffio==1.3.1
+    # via anyio
+spaces==0.36.0
+    # via trellis (pyproject.toml)
+spconv-cu120==2.3.6
+    # via trellis (pyproject.toml)
+sse-starlette==2.3.6
+    # via mcp
+starlette==0.46.2
+    # via
+    #   fastapi
+    #   gradio
+    #   mcp
+sympy==1.14.0
+    # via
+    #   onnxruntime
+    #   torch
+termcolor==3.1.0
+    # via fire
+texttable==1.7.0
+    # via igraph
+tifffile==2025.5.10
+    # via scikit-image
+tokenizers==0.21.1
+    # via transformers
+tomlkit==0.13.2
+    # via gradio
 torch==2.4.0
+    # via
+    #   trellis (pyproject.toml)
+    #   flash-attn
+    #   torchvision
+    #   xformers
 torchvision==0.19.0
-pillow==10.4.0
-imageio==2.36.1
-imageio-ffmpeg==0.5.1
+    # via trellis (pyproject.toml)
 tqdm==4.67.1
-easydict==1.13
-opencv-python-headless==4.10.0.84
-scipy==1.14.1
-rembg==2.0.60
-onnxruntime==1.20.1
-trimesh==4.5.3
-xatlas==0.0.9
-pyvista==0.44.2
-pymeshfix==0.17.0
-igraph==0.11.8
-git+https://github.com/EasternJournalist/utils3d.git@9a4eb15e4021b67b12c460c7057d642626897ec8
+    # via
+    #   huggingface-hub
+    #   rembg
+    #   transformers
+transformers==4.52.4
+    # via trellis (pyproject.toml)
+trimesh==4.6.10
+    # via trellis (pyproject.toml)
+triton==3.0.0
+    # via torch
+typer==0.16.0
+    # via gradio
+typing-extensions==4.13.2
+    # via
+    #   anyio
+    #   exceptiongroup
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   pyvista
+    #   referencing
+    #   rich
+    #   spaces
+    #   torch
+    #   typer
+    #   typing-inspection
+    #   uvicorn
+typing-inspection==0.4.1
+    # via
+    #   pydantic
+    #   pydantic-settings
+tzdata==2025.2
+    # via pandas
+urllib3==2.4.0
+    # via requests
+utils3d @ git+https://github.com/EasternJournalist/utils3d.git@9a4eb15e4021b67b12c460c7057d642626897ec8
+    # via trellis (pyproject.toml)
+uvicorn==0.34.2
+    # via
+    #   gradio
+    #   mcp
+vtk==9.4.2
+    # via pyvista
+websockets==15.0.1
+    # via gradio-client
+xatlas==0.0.10
+    # via trellis (pyproject.toml)
 xformers==0.0.27.post2
-spconv-cu120==2.3.6
-transformers==4.46.3
-gradio_litmodel3d==0.0.1
-https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post2/flash_attn-2.7.0.post2+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
-https://huggingface.co/spaces/JeffreyXiang/TRELLIS/resolve/main/wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl?download=true
-https://huggingface.co/spaces/JeffreyXiang/TRELLIS/resolve/main/wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl?download=true
+    # via trellis (pyproject.toml)