add example for TRELLIS

misc/trellis3d.py

@@ -0,0 +1,252 @@
+"This example originally contributed by @sandeeppatra96 and @patraxo on GitHub"
+import logging
+import tempfile
+import traceback
+
+import modal
+import requests
+from fastapi import HTTPException, Request, Response, status
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+REPO_URL = "https://github.com/microsoft/TRELLIS.git"
+MODEL_NAME = "JeffreyXiang/TRELLIS-image-large"
+TRELLIS_DIR = "/trellis"
+MINUTES = 60
+HOURS = 60 * MINUTES
+
+cuda_version = "12.2.0"
+flavor = "devel"
+os_version = "ubuntu22.04"
+tag = f"{cuda_version}-{flavor}-{os_version}"
+
+
+def clone_repository():
+    import subprocess
+
+    subprocess.run(
+        ["git", "clone", "--recurse-submodules", REPO_URL, TRELLIS_DIR],
+        check=True,
+    )
+
+
+# The specific version of torch==2.4.0 to circumvent the flash attention wheel build error
+
+trellis_image = (
+    modal.Image.from_registry(f"nvidia/cuda:{tag}", add_python="3.10")
+    .apt_install(
+        "git",
+        "ffmpeg",
+        "cmake",
+        "clang",
+        "build-essential",
+        "libgl1-mesa-glx",
+        "libglib2.0-0",
+        "libgomp1",
+        "libxrender1",
+        "libxext6",
+        "ninja-build",
+    )
+    .pip_install("packaging", "ninja", "torch==2.4.0", "wheel", "setuptools")
+    .env(
+        {
+            # "MAX_JOBS": "16", # in case flash attention takes more time to build
+            "HF_HUB_ENABLE_HF_TRANSFER": "1",
+            "CC": "clang",
+            "CXX": "clang++",
+            "CUDAHOSTCXX": "clang++",
+            "CUDA_HOME": "/usr/local/cuda-12.2",
+            "CPATH": "/usr/local/cuda-12.2/targets/x86_64-linux/include",
+            "LIBRARY_PATH": "/usr/local/cuda-12.2/targets/x86_64-linux/lib64",
+            "LD_LIBRARY_PATH": "/usr/local/cuda-12.2/targets/x86_64-linux/lib64",
+            "CFLAGS": "-Wno-narrowing",
+            "CXXFLAGS": "-Wno-narrowing",
+            "ATTN_BACKEND": "flash-attn",  # or 'xformers'
+            "SPCONV_ALGO": "native",  # or 'auto'
+        }
+    )
+    .pip_install("flash-attn==2.6.3", extra_options="--no-build-isolation")
+    .pip_install(
+        "git+https://github.com/EasternJournalist/utils3d.git@9a4eb15e4021b67b12c460c7057d642626897ec8",
+        "numpy",
+        "pillow",
+        "imageio",
+        "onnxruntime",
+        "trimesh",
+        "safetensors",
+        "easydict",
+        "scipy",
+        "tqdm",
+        "einops",
+        "xformers",
+        "hf_transfer",
+        "opencv-python-headless",
+        "largesteps",
+        "spconv-cu118",
+        "rembg",
+        "torchvision",
+        "imageio-ffmpeg",
+        "xatlas",
+        "pyvista",
+        "pymeshfix",
+        "igraph",
+        "git+https://github.com/NVIDIAGameWorks/kaolin.git",
+        "https://huggingface.co/spaces/JeffreyXiang/TRELLIS/resolve/main/wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl",
+        # "git+https://github.com/NVlabs/nvdiffrast.git", # build failed
+        "huggingface-hub",
+        "https://github.com/camenduru/wheels/releases/download/3090/diso-0.1.4-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",
+    )
+    .pip_install("fastapi[standard]==0.115.6")
+    .entrypoint([])
+    .run_function(clone_repository)
+)
+
+app = modal.App(name="example-trellis-3d")
+
+
+@app.cls(
+    image=trellis_image,
+    gpu=modal.gpu.L4(count=1),
+    timeout=1 * HOURS,
+    container_idle_timeout=1 * MINUTES,
+)
+class Model:
+    @modal.enter()
+    @modal.build()
+    def initialize(self):
+        import sys
+
+        sys.path.append(TRELLIS_DIR)
+
+        from trellis.pipelines import TrellisImageTo3DPipeline
+
+        try:
+            self.pipe = TrellisImageTo3DPipeline.from_pretrained(MODEL_NAME)
+            self.pipe.cuda()
+            logger.info("TRELLIS model initialized successfully")
+        except Exception as e:
+            error_msg = f"Error during model initialization: {str(e)}"
+            logger.error(error_msg)
+            logger.error(f"Traceback: {traceback.format_exc()}")
+            raise
+
+    def process_image(
+        self,
+        image_url: str,
+        simplify: float,
+        texture_size: int,
+        sparse_sampling_steps: int,
+        sparse_sampling_cfg: float,
+        slat_sampling_steps: int,
+        slat_sampling_cfg: int,
+        seed: int,
+        output_format: str,
+    ):
+        import io
+        import os
+
+        from PIL import Image
+
+        try:
+            response = requests.get(image_url)
+            if response.status_code != 200:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail="Failed to download image from provided URL",
+                )
+
+            image = Image.open(io.BytesIO(response.content))
+
+            logger.info("Starting model inference...")
+            outputs = self.pipe.run(
+                image,
+                seed=seed,
+                sparse_structure_sampler_params={
+                    "steps": sparse_sampling_steps,
+                    "cfg_strength": sparse_sampling_cfg,
+                },
+                slat_sampler_params={
+                    "steps": slat_sampling_steps,
+                    "cfg_strength": slat_sampling_cfg,
+                },
+            )
+            logger.info("Model inference completed successfully")
+
+            if output_format == "glb":
+                from trellis.utils import postprocessing_utils
+
+                glb = postprocessing_utils.to_glb(
+                    outputs["gaussian"][0],
+                    outputs["mesh"][0],
+                    simplify=simplify,
+                    texture_size=texture_size,
+                )
+
+                temp_glb = tempfile.NamedTemporaryFile(
+                    suffix=".glb", delete=False
+                )
+                temp_path = temp_glb.name
+                logger.info(f"Exporting mesh to: {temp_path}")
+                glb.export(temp_path)
+                temp_glb.close()
+
+                try:
+                    with open(temp_path, "rb") as file:
+                        content = file.read()
+                        if os.path.exists(temp_path):
+                            os.unlink(temp_path)
+                            logger.info("Temp file cleaned up")
+                        return Response(
+                            content=content,
+                            media_type="model/gltf-binary",
+                            headers={
+                                "Content-Disposition": "attachment; filename=output.glb",
+                            },
+                        )
+                except Exception as e:
+                    if os.path.exists(temp_path):
+                        os.unlink(temp_path)
+                    raise e
+
+            else:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail=f"Unsupported output format: {output_format}",
+                )
+
+        except Exception as e:
+            error_msg = f"Error during processing: {str(e)}"
+            logger.error(error_msg)
+            logger.error(f"Traceback: {traceback.format_exc()}")
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                detail=error_msg,
+            )
+
+    @modal.web_endpoint(method="GET", docs=True)
+    async def generate(
+        self,
+        request: Request,
+        image_url: str,
+        simplify: float = 0.95,
+        texture_size: int = 1024,
+        sparse_sampling_steps: int = 12,
+        sparse_sampling_cfg: float = 7.5,
+        slat_sampling_steps: int = 12,
+        slat_sampling_cfg: int = 3,
+        seed: int = 42,
+        output_format: str = "glb",
+    ):
+        return self.process_image(
+            image_url,
+            simplify,
+            texture_size,
+            sparse_sampling_steps,
+            sparse_sampling_cfg,
+            slat_sampling_steps,
+            slat_sampling_cfg,
+            seed,
+            output_format,
+        )