[diffusion] model: support Hunyuan3D-2 (#18170)

Co-authored-by: yingluosanqian <yingluosanqian@gmail.com>
Co-authored-by: daiweitao <dwti614707404@163.com>
Co-authored-by: Mick <mickjagger19@icloud.com>

python/pyproject.toml

@@ -114,6 +114,9 @@ diffusion = [
   "cache-dit==1.2.3",
   "addict==2.4.0",
   "av==16.1.0",
+  "scikit-image==0.25.2",
+  "trimesh>=4.0.0",
+  "xatlas",
 ]
 
 tracing = [

python/pyproject_npu.toml

@@ -79,7 +79,10 @@ diffusion = [
     "opencv-python==4.10.0.84",
     "remote-pdb",
     "cache-dit==1.2.1",
-    "addict"
+    "addict",
+    "scikit-image==0.25.2",
+    "trimesh>=4.0.0",
+    "xatlas",
 ]
 
 tracing = [

python/pyproject_other.toml

@@ -98,7 +98,10 @@ diffusion_hip = [
   "vsa==0.0.4",
   "runai_model_streamer>=0.15.5",
   "cache-dit==1.1.8",
-  "addict"
+  "addict",
+  "scikit-image==0.25.2",
+  "trimesh>=4.0.0",
+  "xatlas",
 ]
 
 # For Intel Gaudi(device : hpu) follow the installation guide
@@ -128,7 +131,10 @@ diffusion_musa = [
   "vsa==0.0.4",
   "runai_model_streamer>=0.15.5",
   "cache-dit==1.1.8",
-  "addict"
+  "addict",
+  "scikit-image==0.25.2",
+  "trimesh>=4.0.0",
+  "xatlas",
 ]
 
 test = [

python/sglang/cli/utils.py

@@ -29,8 +29,20 @@ def get_is_diffusion_model(model_path: str) -> bool:
     Returns False on any failure (network error, 404, offline mode, etc.)
     so that the caller falls through to the standard LLM server path.
     """
+    try:
+        from sglang.multimodal_gen.registry import (
+            is_known_non_diffusers_multimodal_model,
+        )
+    except ImportError:
+        is_known_non_diffusers_multimodal_model = lambda _: False
+
     if os.path.isdir(model_path):
-        return _is_diffusers_model_dir(model_path)
+        if _is_diffusers_model_dir(model_path):
+            return True
+        return is_known_non_diffusers_multimodal_model(model_path)
+
+    if is_known_non_diffusers_multimodal_model(model_path):
+        return True
 
     try:
         if envs.SGLANG_USE_MODELSCOPE.get():

python/sglang/multimodal_gen/configs/models/dits/__init__.py

@@ -1,5 +1,6 @@
 # Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
 
+from sglang.multimodal_gen.configs.models.dits.hunyuan3d import Hunyuan3DDiTConfig
 from sglang.multimodal_gen.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from sglang.multimodal_gen.configs.models.dits.mova_audio import MOVAAudioConfig
 from sglang.multimodal_gen.configs.models.dits.mova_video import MOVAVideoConfig
@@ -7,7 +8,8 @@ from sglang.multimodal_gen.configs.models.dits.wanvideo import WanVideoConfig
 
 __all__ = [
     "HunyuanVideoConfig",
+    "WanVideoConfig",
+    "Hunyuan3DDiTConfig",
     "MOVAAudioConfig",
     "MOVAVideoConfig",
-    "WanVideoConfig",
 ]

python/sglang/multimodal_gen/configs/models/dits/hunyuan3d.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from sglang.multimodal_gen.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+@dataclass
+class Hunyuan3DDiTArchConfig(DiTArchConfig):
+    """Architecture config for Hunyuan3D DiT (Flux-style for Hunyuan3D-2.0)."""
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"(.*)\.img_mlp\.0\.(.*)$": r"\1.img_mlp.fc_in.\2",
+            r"(.*)\.img_mlp\.2\.(.*)$": r"\1.img_mlp.fc_out.\2",
+            r"(.*)\.txt_mlp\.0\.(.*)$": r"\1.txt_mlp.fc_in.\2",
+            r"(.*)\.txt_mlp\.2\.(.*)$": r"\1.txt_mlp.fc_out.\2",
+        }
+    )
+
+    in_channels: int = 64
+    hidden_size: int = 1024
+    num_attention_heads: int = 16
+    num_layers: int = 16
+    num_single_layers: int = 32
+    mlp_ratio: float = 4.0
+    context_in_dim: int = 1536
+    axes_dim: tuple[int, ...] = (64,)
+    theta: int = 10000
+    qkv_bias: bool = True
+    guidance_embed: bool = False
+    time_factor: float = 1000.0
+
+    def __post_init__(self) -> None:
+        if self.num_channels_latents == 0:
+            self.num_channels_latents = self.in_channels
+        super().__post_init__()
+
+
+@dataclass
+class Hunyuan3DDiTConfig(DiTConfig):
+    """DiT configuration for Hunyuan3D shape generation (Flux-style)."""
+
+    arch_config: Hunyuan3DDiTArchConfig = field(default_factory=Hunyuan3DDiTArchConfig)
+    subfolder: str = "hunyuan3d-dit-v2-0"

python/sglang/multimodal_gen/configs/models/vaes/__init__.py

@@ -1,6 +1,7 @@
 # Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
 
 from sglang.multimodal_gen.configs.models.vaes.dac import DacVAEConfig
+from sglang.multimodal_gen.configs.models.vaes.hunyuan3d import Hunyuan3DVAEConfig
 from sglang.multimodal_gen.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from sglang.multimodal_gen.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -8,4 +9,5 @@ __all__ = [
     "DacVAEConfig",
     "HunyuanVAEConfig",
     "WanVAEConfig",
+    "Hunyuan3DVAEConfig",
 ]

python/sglang/multimodal_gen/configs/models/vaes/hunyuan3d.py

@@ -0,0 +1,22 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from sglang.multimodal_gen.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class Hunyuan3DVAEArchConfig(VAEArchConfig):
+    """Architecture config for Hunyuan3D VAE."""
+
+    latent_shape: tuple[int, ...] = (1024, 64)
+    scale_factor: float = 1.0
+
+
+@dataclass
+class Hunyuan3DVAEConfig(VAEConfig):
+    """VAE configuration for Hunyuan3D."""
+
+    arch_config: Hunyuan3DVAEArchConfig = field(default_factory=Hunyuan3DVAEArchConfig)
+    subfolder: str = "hunyuan3d-dit-v2-0"
+    load_encoder: bool = False
+    load_decoder: bool = True

python/sglang/multimodal_gen/configs/pipeline_configs/__init__.py

@@ -19,6 +19,9 @@ from sglang.multimodal_gen.configs.pipeline_configs.hunyuan import (
     FastHunyuanConfig,
     HunyuanConfig,
 )
+from sglang.multimodal_gen.configs.pipeline_configs.hunyuan3d import (
+    Hunyuan3D2PipelineConfig,
+)
 from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import LTX2PipelineConfig
 from sglang.multimodal_gen.configs.pipeline_configs.mova import MOVAPipelineConfig
 from sglang.multimodal_gen.configs.pipeline_configs.wan import (
@@ -34,6 +37,7 @@ __all__ = [
     "DiffusersGenericPipelineConfig",
     "HunyuanConfig",
     "FastHunyuanConfig",
+    "Hunyuan3D2PipelineConfig",
     "FluxPipelineConfig",
     "Flux2PipelineConfig",
     "Flux2KleinPipelineConfig",

python/sglang/multimodal_gen/configs/pipeline_configs/base.py

@@ -53,6 +53,7 @@ class ModelTaskType(Enum):
     T2I = auto()  # Text to Image
     I2I = auto()  # Image to Image
     TI2I = auto()  # Image to Image or Text-Image to Image
+    I2M = auto()  # Image to Mesh
 
     def is_image_gen(self) -> bool:
         return (
@@ -62,7 +63,11 @@ class ModelTaskType(Enum):
         )
 
     def requires_image_input(self) -> bool:
-        return self == ModelTaskType.I2V or self == ModelTaskType.I2I
+        return (
+            self == ModelTaskType.I2V
+            or self == ModelTaskType.I2I
+            or self == ModelTaskType.I2M
+        )
 
     def accepts_image_input(self) -> bool:
         return (
@@ -70,9 +75,12 @@ class ModelTaskType(Enum):
             or self == ModelTaskType.I2I
             or self == ModelTaskType.TI2I
             or self == ModelTaskType.TI2V
+            or self == ModelTaskType.I2M
         )
 
     def data_type(self) -> DataType:
+        if self == ModelTaskType.I2M:
+            return DataType.MESH
         if self.is_image_gen():
             return DataType.IMAGE
         else:

python/sglang/multimodal_gen/configs/pipeline_configs/hunyuan3d.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+from typing import Optional
+
+from sglang.multimodal_gen.configs.models import DiTConfig, VAEConfig
+from sglang.multimodal_gen.configs.models.dits.hunyuan3d import Hunyuan3DDiTConfig
+from sglang.multimodal_gen.configs.models.vaes.hunyuan3d import Hunyuan3DVAEConfig
+from sglang.multimodal_gen.configs.pipeline_configs.base import (
+    ModelTaskType,
+    PipelineConfig,
+)
+
+
+@dataclass
+class Hunyuan3D2PipelineConfig(PipelineConfig):
+    """Pipeline configuration for Hunyuan3D image-to-mesh generation."""
+
+    task_type: ModelTaskType = ModelTaskType.I2M
+
+    # Subfolder paths
+    shape_subfolder: str = "hunyuan3d-dit-v2-0"
+    paint_subfolder: str = "hunyuan3d-paint-v2-0"
+    delight_subfolder: str = "hunyuan3d-delight-v2-0"
+
+    # DiT configuration
+    dit_config: DiTConfig = field(default_factory=Hunyuan3DDiTConfig)
+    dit_precision: str = "fp16"
+
+    # VAE configuration
+    vae_config: VAEConfig = field(default_factory=Hunyuan3DVAEConfig)
+    vae_precision: str = "fp32"
+
+    # Shape model configuration
+    shape_model_path: Optional[str] = None
+    shape_use_safetensors: bool = True
+    shape_variant: Optional[str] = "fp16"
+    shape_num_inference_steps: int = 50
+    guidance_scale: float = 5.0
+    shape_box_v: float = 1.01
+    shape_octree_resolution: int = 384
+    shape_mc_level: float = 0.0
+    shape_mc_algo: Optional[str] = "mc"
+    shape_num_chunks: int = 8000
+    shape_output_type: str = "trimesh"
+
+    # Delight model configuration
+    delight_enable: bool = True
+    delight_prompt: str = ""
+    delight_negative_prompt: str = ""
+    delight_strength: float = 1.0
+    delight_num_inference_steps: int = 50
+    delight_guidance_scale: float = 1.0
+    delight_cfg_image: float = 1.5
+
+    # Paint model configuration
+    paint_enable: bool = True
+    paint_num_inference_steps: int = 30
+    paint_guidance_scale: float = 2.0
+    paint_resolution: int = 512
+    paint_render_size: int = 2048
+    paint_texture_size: int = 2048
+    paint_use_remesh: bool = True
+    paint_save_glb: bool = True
+    paint_turbo_mode: bool = False
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True
+
+    def prepare_latent_shape(self, batch, batch_size, num_frames):
+        latent_shape = self.vae_config.arch_config.latent_shape
+        shape = (batch_size, *latent_shape)
+        return shape

python/sglang/multimodal_gen/configs/sample/hunyuan3d.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Sampling parameters for Hunyuan3D generation."""
+
+from dataclasses import dataclass
+
+from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
+
+
+@dataclass
+class Hunyuan3DSamplingParams(SamplingParams):
+    """Sampling parameters for Hunyuan3D image-to-mesh generation."""
+
+    negative_prompt: str = ""
+
+    shape_num_inference_steps: int = 50
+    guidance_scale: float = 5.0
+
+    paint_num_inference_steps: int = 30
+    paint_guidance_scale: float = 2.0
+
+    def __post_init__(self):
+        if self.prompt is None:
+            self.prompt = ""
+
+        if self.num_inference_steps is None:
+            self.num_inference_steps = self.shape_num_inference_steps
+
+        self.guidance_scale = max(5.0, min(self.guidance_scale, 6.5))
+        super().__post_init__()

python/sglang/multimodal_gen/configs/sample/sampling_params.py

@@ -66,12 +66,14 @@ def _sanitize_filename(name: str, replacement: str = "_", max_length: int = 150)
 class DataType(Enum):
     IMAGE = auto()
     VIDEO = auto()
+    MESH = auto()
 
     def get_default_extension(self) -> str:
         if self == DataType.IMAGE:
             return "png"
-        else:
+        if self == DataType.VIDEO:
             return "mp4"
+        return "glb"
 
 
 @dataclass
@@ -170,7 +172,7 @@ class SamplingParams:
         # add extension if needed
         if not any(
             self.output_file_name.endswith(ext)
-            for ext in [".mp4", ".jpg", ".png", ".webp"]
+            for ext in [".mp4", ".jpg", ".png", ".webp", ".obj", ".glb"]
         ):
             self.output_file_name = (
                 f"{self.output_file_name}.{self.data_type.get_default_extension()}"
@@ -357,6 +359,19 @@ class SamplingParams:
         if not isinstance(self.prompt, str):
             raise TypeError(f"`prompt` must be a string, but got {type(self.prompt)}")
 
+        if self.guidance_scale is None:
+            try:
+                from sglang.multimodal_gen.configs.pipeline_configs.hunyuan3d import (
+                    Hunyuan3D2PipelineConfig,
+                )
+
+                if isinstance(pipeline_config, Hunyuan3D2PipelineConfig):
+                    self.guidance_scale = pipeline_config.guidance_scale
+                else:
+                    self.guidance_scale = 1.0
+            except ImportError:
+                self.guidance_scale = 1.0
+
         self.data_type = server_args.pipeline_config.task_type.data_type()
 
         if self.output_path is None and server_args.output_path is not None:

python/sglang/multimodal_gen/csrc/render/hunyuan3d_rasterizer/__init__.py

@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Custom CUDA rasterizer for Hunyuan3D texture generation.
+
+This module provides JIT-compiled CUDA rasterization for fast mesh rendering.
+Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+"""
+
+from __future__ import annotations
+
+import os
+from typing import List, Tuple
+
+import torch
+
+_abs_path = os.path.dirname(os.path.abspath(__file__))
+_custom_rasterizer_kernel = None
+
+
+def _load_custom_rasterizer():
+    """JIT compile and load the custom rasterizer kernel."""
+    global _custom_rasterizer_kernel
+
+    if _custom_rasterizer_kernel is not None:
+        return _custom_rasterizer_kernel
+
+    from torch.utils.cpp_extension import load
+
+    _custom_rasterizer_kernel = load(
+        name="custom_rasterizer_kernel",
+        sources=[
+            f"{_abs_path}/rasterizer.cpp",
+            f"{_abs_path}/rasterizer_gpu.cu",
+        ],
+        extra_cflags=["-O3"],
+        extra_cuda_cflags=["-O3", "--use_fast_math"],
+        verbose=False,
+    )
+    return _custom_rasterizer_kernel
+
+
+def rasterize(
+    pos: torch.Tensor,
+    tri: torch.Tensor,
+    resolution: Tuple[int, int],
+    clamp_depth: torch.Tensor = None,
+    use_depth_prior: int = 0,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Rasterize mesh to get face indices and barycentric coordinates."""
+    kernel = _load_custom_rasterizer()
+
+    if clamp_depth is None:
+        clamp_depth = torch.zeros(0, device=pos.device)
+
+    # pos should be [N, 4], remove batch dim if present
+    if pos.dim() == 3:
+        pos = pos[0]
+
+    findices, barycentric = kernel.rasterize_image(
+        pos, tri, clamp_depth, resolution[1], resolution[0], 1e-6, use_depth_prior
+    )
+    return findices, barycentric
+
+
+def interpolate(
+    col: torch.Tensor,
+    findices: torch.Tensor,
+    barycentric: torch.Tensor,
+    tri: torch.Tensor,
+) -> torch.Tensor:
+    """Interpolate vertex attributes using barycentric coordinates."""
+    # Handle zero indices (background)
+    f = findices - 1 + (findices == 0)
+    vcol = col[0, tri.long()[f.long()]]
+    result = barycentric.view(*barycentric.shape, 1) * vcol
+    result = torch.sum(result, axis=-2)
+    return result.view(1, *result.shape)
+
+
+__all__ = ["rasterize", "interpolate"]

python/sglang/multimodal_gen/csrc/render/hunyuan3d_rasterizer/rasterizer.cpp

@@ -0,0 +1,140 @@
+// SPDX-License-Identifier: Apache-2.0
+// Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+// Original license: TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+
+#include "rasterizer.h"
+
+void rasterizeTriangleCPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) {
+    float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0]));
+    float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0]));
+    float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1]));
+    float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1]));
+
+    for (int px = x_min; px < x_max + 1; ++px) {
+        if (px < 0 || px >= width)
+            continue;
+        for (int py = y_min; py < y_max + 1; ++py) {
+            if (py < 0 || py >= height)
+                continue;
+            float vt[2] = {px + 0.5f, py + 0.5f};
+            float baryCentricCoordinate[3];
+            calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate);
+            if (isBarycentricCoordInBounds(baryCentricCoordinate)) {
+                int pixel = py * width + px;
+                if (zbuffer == 0) {
+                    zbuffer[pixel] = (INT64)(idx + 1);
+                    continue;
+                }
+
+                float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2];
+                float depth_thres = 0;
+                if (d) {
+                    depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation;
+                }
+                
+                int z_quantize = depth * (2<<17);
+                INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1);
+                if (depth < depth_thres)
+                    continue;
+                zbuffer[pixel] = std::min(zbuffer[pixel], token);
+            }
+        }
+    }
+}
+
+void barycentricFromImgcoordCPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces,
+    float* barycentric_map, int pix)
+{
+    INT64 f = zbuffer[pix] % MAXINT;
+    if (f == (MAXINT-1)) {
+        findices[pix] = 0;
+        barycentric_map[pix * 3] = 0;
+        barycentric_map[pix * 3 + 1] = 0;
+        barycentric_map[pix * 3 + 2] = 0;
+        return;
+    }
+    findices[pix] = f;
+    f -= 1;
+    float barycentric[3] = {0, 0, 0};
+    if (f >= 0) {
+        float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f};
+        float* vt0_ptr = V + (F[f * 3] * 4);
+        float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+        float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+        float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f};
+        float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f};
+        float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f};
+
+        calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric);
+
+        barycentric[0] = barycentric[0] / vt0_ptr[3];
+        barycentric[1] = barycentric[1] / vt1_ptr[3];
+        barycentric[2] = barycentric[2] / vt2_ptr[3];
+        float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]);
+        barycentric[0] *= w;
+        barycentric[1] *= w;
+        barycentric[2] *= w;
+    }
+    barycentric_map[pix * 3] = barycentric[0];
+    barycentric_map[pix * 3 + 1] = barycentric[1];
+    barycentric_map[pix * 3 + 2] = barycentric[2];
+}
+
+void rasterizeImagecoordsKernelCPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces, int f)
+{
+    float* vt0_ptr = V + (F[f * 3] * 4);
+    float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+    float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+    float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f};
+    float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f};
+    float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f};
+
+    rasterizeTriangleCPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc);
+}
+
+std::vector<torch::Tensor> rasterize_image_cpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int num_faces = F.size(0);
+    int num_vertices = V.size(0);
+    auto options = torch::TensorOptions().dtype(torch::kInt32).requires_grad(false);
+    auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false);
+    auto findices = torch::zeros({height, width}, options);
+    INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
+    auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;
+
+    if (!use_depth_prior) {
+        for (int i = 0; i < num_faces; ++i) {
+            rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), 0,
+                (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i); 
+        }
+    } else {
+        for (int i = 0; i < num_faces; ++i)
+            rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
+                (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
+    }
+
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
+    auto barycentric = torch::zeros({height, width, 3}, float_options);
+    for (int i = 0; i < width * height; ++i)
+        barycentricFromImgcoordCPU(V.data_ptr<float>(), F.data_ptr<int>(),
+            findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>(), i);
+
+    return {findices, barycentric};
+}
+
+std::vector<torch::Tensor> rasterize_image(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int device_id = V.get_device();
+    if (device_id == -1)
+        return rasterize_image_cpu(V, F, D, width, height, occlusion_truncation, use_depth_prior);
+    else
+        return rasterize_image_gpu(V, F, D, width, height, occlusion_truncation, use_depth_prior);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("rasterize_image", &rasterize_image, "Custom image rasterization");
+}

python/sglang/multimodal_gen/csrc/render/hunyuan3d_rasterizer/rasterizer.h

@@ -0,0 +1,50 @@
+// SPDX-License-Identifier: Apache-2.0
+// Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+// Original license: TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+
+#ifndef RASTERIZER_H_
+#define RASTERIZER_H_
+
+#include <torch/extension.h>
+#include <vector>
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#define INT64 unsigned long long
+#define MAXINT 2147483647
+
+__host__ __device__ inline float calculateSignedArea2(float* a, float* b, float* c) {
+    return ((c[0] - a[0]) * (b[1] - a[1]) - (b[0] - a[0]) * (c[1] - a[1]));
+}
+
+__host__ __device__ inline void calculateBarycentricCoordinate(float* a, float* b, float* c, float* p,
+    float* barycentric)
+{
+    float beta_tri = calculateSignedArea2(a, p, c);
+    float gamma_tri = calculateSignedArea2(a, b, p);
+    float area = calculateSignedArea2(a, b, c);
+    if (area == 0) {
+        barycentric[0] = -1.0;
+        barycentric[1] = -1.0;
+        barycentric[2] = -1.0;
+        return;
+    }
+    float tri_inv = 1.0 / area;
+    float beta = beta_tri * tri_inv;
+    float gamma = gamma_tri * tri_inv;
+    float alpha = 1.0 - beta - gamma;
+    barycentric[0] = alpha;
+    barycentric[1] = beta;
+    barycentric[2] = gamma;
+}
+
+__host__ __device__ inline bool isBarycentricCoordInBounds(float* barycentricCoord) {
+    return barycentricCoord[0] >= 0.0 && barycentricCoord[0] <= 1.0 &&
+           barycentricCoord[1] >= 0.0 && barycentricCoord[1] <= 1.0 &&
+           barycentricCoord[2] >= 0.0 && barycentricCoord[2] <= 1.0;
+}
+
+std::vector<torch::Tensor> rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior);
+
+#endif

python/sglang/multimodal_gen/csrc/render/hunyuan3d_rasterizer/rasterizer_gpu.cu

@@ -0,0 +1,130 @@
+// SPDX-License-Identifier: Apache-2.0
+// Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+// Original license: TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+
+#include "rasterizer.h"
+
+__device__ void rasterizeTriangleGPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) {
+    float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0]));
+    float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0]));
+    float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1]));
+    float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1]));
+
+    for (int px = x_min; px < x_max + 1; ++px) {
+        if (px < 0 || px >= width)
+            continue;
+        for (int py = y_min; py < y_max + 1; ++py) {
+            if (py < 0 || py >= height)
+                continue;
+            float vt[2] = {px + 0.5f, py + 0.5f};
+            float baryCentricCoordinate[3];
+            calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate);
+            if (isBarycentricCoordInBounds(baryCentricCoordinate)) {
+                int pixel = py * width + px;
+                if (zbuffer == 0) {
+                    atomicExch(&zbuffer[pixel], (INT64)(idx + 1));
+                    continue;
+                }
+                float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2];
+                float depth_thres = 0;
+                if (d) {
+                    depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation;
+                }
+                
+                int z_quantize = depth * (2<<17);
+                INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1);
+                if (depth < depth_thres)
+                    continue;
+                atomicMin(&zbuffer[pixel], token);
+            }
+        }
+    }
+}
+
+__global__ void barycentricFromImgcoordGPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces,
+    float* barycentric_map)
+{
+    int pix = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pix >= width * height)
+        return;
+    INT64 f = zbuffer[pix] % MAXINT;
+    if (f == (MAXINT-1)) {
+        findices[pix] = 0;
+        barycentric_map[pix * 3] = 0;
+        barycentric_map[pix * 3 + 1] = 0;
+        barycentric_map[pix * 3 + 2] = 0;
+        return;
+    }
+    findices[pix] = f;
+    f -= 1;
+    float barycentric[3] = {0, 0, 0};
+    if (f >= 0) {
+        float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f};
+        float* vt0_ptr = V + (F[f * 3] * 4);
+        float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+        float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+        float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f};
+        float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f};
+        float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f};
+
+        calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric);
+
+        barycentric[0] = barycentric[0] / vt0_ptr[3];
+        barycentric[1] = barycentric[1] / vt1_ptr[3];
+        barycentric[2] = barycentric[2] / vt2_ptr[3];
+        float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]);
+        barycentric[0] *= w;
+        barycentric[1] *= w;
+        barycentric[2] *= w;
+    }
+    barycentric_map[pix * 3] = barycentric[0];
+    barycentric_map[pix * 3 + 1] = barycentric[1];
+    barycentric_map[pix * 3 + 2] = barycentric[2];
+}
+
+__global__ void rasterizeImagecoordsKernelGPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces)
+{
+    int f = blockIdx.x * blockDim.x + threadIdx.x;
+    if (f >= num_faces)
+        return; 
+
+    float* vt0_ptr = V + (F[f * 3] * 4);
+    float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+    float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+    float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f};
+    float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f};
+    float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f};
+
+    rasterizeTriangleGPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc);
+}
+
+std::vector<torch::Tensor> rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int device_id = V.get_device();
+    cudaSetDevice(device_id);
+    int num_faces = F.size(0);
+    int num_vertices = V.size(0);
+    auto options = torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_id).requires_grad(false);
+    auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false);
+    auto findices = torch::zeros({height, width}, options);
+    INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
+    auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;
+
+    if (!use_depth_prior) {
+        rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), 0,
+            (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces); 
+    } else {
+        rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
+            (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces); 
+    }
+
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false);
+    auto barycentric = torch::zeros({height, width, 3}, float_options);
+    barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256, 0, at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(),
+        findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>());
+
+    return {findices, barycentric};
+}

python/sglang/multimodal_gen/csrc/render/mesh_processor/__init__.py

@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Mesh processor C++ extension for texture inpainting.
+
+This module provides JIT-compiled C++ mesh processing for fast texture inpainting.
+Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+"""
+
+from __future__ import annotations
+
+import os
+from typing import Tuple
+
+import numpy as np
+
+_abs_path = os.path.dirname(os.path.abspath(__file__))
+_mesh_processor_kernel = None
+
+
+def _load_mesh_processor():
+    """JIT compile and load the mesh processor kernel."""
+    global _mesh_processor_kernel
+
+    if _mesh_processor_kernel is not None:
+        return _mesh_processor_kernel
+
+    from torch.utils.cpp_extension import load
+
+    _mesh_processor_kernel = load(
+        name="mesh_processor_kernel",
+        sources=[
+            f"{_abs_path}/mesh_processor.cpp",
+        ],
+        extra_cflags=["-O3"],
+        verbose=False,
+    )
+    return _mesh_processor_kernel
+
+
+def meshVerticeInpaint(
+    texture: np.ndarray,
+    mask: np.ndarray,
+    vtx_pos: np.ndarray,
+    vtx_uv: np.ndarray,
+    pos_idx: np.ndarray,
+    uv_idx: np.ndarray,
+    method: str = "smooth",
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Inpaint texture using mesh vertex connectivity."""
+    kernel = _load_mesh_processor()
+
+    texture = np.ascontiguousarray(texture, dtype=np.float32)
+    mask = np.ascontiguousarray(mask, dtype=np.uint8)
+    vtx_pos = np.ascontiguousarray(vtx_pos, dtype=np.float32)
+    vtx_uv = np.ascontiguousarray(vtx_uv, dtype=np.float32)
+    pos_idx = np.ascontiguousarray(pos_idx, dtype=np.int32)
+    uv_idx = np.ascontiguousarray(uv_idx, dtype=np.int32)
+
+    return kernel.meshVerticeInpaint(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx, method)
+
+
+__all__ = ["meshVerticeInpaint"]

python/sglang/multimodal_gen/csrc/render/mesh_processor/mesh_processor.cpp

@@ -0,0 +1,163 @@
+// SPDX-License-Identifier: Apache-2.0
+// Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
+// Original license: TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+
+#include <vector>
+#include <queue>
+#include <cmath>
+#include <algorithm>
+#include <torch/extension.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+
+namespace py = pybind11;
+using namespace std;
+
+std::pair<py::array_t<float>,
+  py::array_t<uint8_t>>  meshVerticeInpaint_smooth(py::array_t<float> texture,
+py::array_t<uint8_t> mask,
+                 py::array_t<float> vtx_pos, py::array_t<float> vtx_uv, 
+                 py::array_t<int> pos_idx, py::array_t<int> uv_idx) {
+    auto texture_buf = texture.request();
+    auto mask_buf = mask.request();
+    auto vtx_pos_buf = vtx_pos.request();
+    auto vtx_uv_buf = vtx_uv.request();
+    auto pos_idx_buf = pos_idx.request();
+    auto uv_idx_buf = uv_idx.request();
+
+    int texture_height = texture_buf.shape[0];
+    int texture_width = texture_buf.shape[1];
+    int texture_channel = texture_buf.shape[2];
+    float* texture_ptr = static_cast<float*>(texture_buf.ptr);
+    uint8_t* mask_ptr = static_cast<uint8_t*>(mask_buf.ptr);
+
+    int vtx_num = vtx_pos_buf.shape[0];
+    float* vtx_pos_ptr = static_cast<float*>(vtx_pos_buf.ptr);
+    float* vtx_uv_ptr = static_cast<float*>(vtx_uv_buf.ptr);
+    int* pos_idx_ptr = static_cast<int*>(pos_idx_buf.ptr);
+    int* uv_idx_ptr = static_cast<int*>(uv_idx_buf.ptr);
+
+    vector<float> vtx_mask(vtx_num, 0.0f);
+    vector<vector<float>> vtx_color(vtx_num, vector<float>(texture_channel, 0.0f));
+    vector<int> uncolored_vtxs;
+
+    vector<vector<int>> G(vtx_num);
+
+    for (int i = 0; i < uv_idx_buf.shape[0]; ++i) {
+        for (int k = 0; k < 3; ++k) {
+            int vtx_uv_idx = uv_idx_ptr[i * 3 + k];
+            int vtx_idx = pos_idx_ptr[i * 3 + k];
+            int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1));
+            int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1));
+
+            if (mask_ptr[uv_u * texture_width + uv_v] > 0) {
+                vtx_mask[vtx_idx] = 1.0f;
+                for (int c = 0; c < texture_channel; ++c) {
+                    vtx_color[vtx_idx][c] = texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c];
+                }
+            }else{
+                uncolored_vtxs.push_back(vtx_idx);
+            }
+
+            G[pos_idx_ptr[i * 3 + k]].push_back(pos_idx_ptr[i * 3 + (k + 1) % 3]);
+        }
+    }
+
+    int smooth_count = 2;
+    int last_uncolored_vtx_count = 0;
+    while (smooth_count>0) {
+        int uncolored_vtx_count = 0;
+
+        for (int vtx_idx : uncolored_vtxs) {
+
+            vector<float> sum_color(texture_channel, 0.0f);
+            float total_weight = 0.0f;
+
+            array<float, 3> vtx_0 = {vtx_pos_ptr[vtx_idx * 3],
+vtx_pos_ptr[vtx_idx * 3 + 1], vtx_pos_ptr[vtx_idx * 3 + 2]};
+            for (int connected_idx : G[vtx_idx]) {
+                if (vtx_mask[connected_idx] > 0) {
+                    array<float, 3> vtx1 = {vtx_pos_ptr[connected_idx * 3],
+                    vtx_pos_ptr[connected_idx * 3 + 1], vtx_pos_ptr[connected_idx * 3 + 2]};
+                    float dist_weight = 1.0f / max(sqrt(pow(vtx_0[0] - vtx1[0], 2) + pow(vtx_0[1] - vtx1[1], 2) + \
+                     pow(vtx_0[2] - vtx1[2], 2)), 1E-4);
+                    dist_weight = dist_weight * dist_weight;
+                    for (int c = 0; c < texture_channel; ++c) {
+                        sum_color[c] += vtx_color[connected_idx][c] * dist_weight;
+                    }
+                    total_weight += dist_weight;
+                }
+            }
+
+            if (total_weight > 0.0f) {
+                for (int c = 0; c < texture_channel; ++c) {
+                    vtx_color[vtx_idx][c] = sum_color[c] / total_weight;
+                }
+                vtx_mask[vtx_idx] = 1.0f;
+            } else {
+                uncolored_vtx_count++;
+            }
+            
+        }
+
+        if(last_uncolored_vtx_count==uncolored_vtx_count){
+            smooth_count--;
+        }else{
+            smooth_count++;
+        }
+        last_uncolored_vtx_count = uncolored_vtx_count;
+    }
+
+    py::array_t<float> new_texture(texture_buf.size);
+    py::array_t<uint8_t> new_mask(mask_buf.size);
+
+    auto new_texture_buf = new_texture.request();
+    auto new_mask_buf = new_mask.request();
+
+    float* new_texture_ptr = static_cast<float*>(new_texture_buf.ptr);
+    uint8_t* new_mask_ptr = static_cast<uint8_t*>(new_mask_buf.ptr);
+    std::copy(texture_ptr, texture_ptr + texture_buf.size, new_texture_ptr);
+    std::copy(mask_ptr, mask_ptr + mask_buf.size, new_mask_ptr);
+
+    for (int face_idx = 0; face_idx < uv_idx_buf.shape[0]; ++face_idx) {
+        for (int k = 0; k < 3; ++k) {
+            int vtx_uv_idx = uv_idx_ptr[face_idx * 3 + k];
+            int vtx_idx = pos_idx_ptr[face_idx * 3 + k];
+
+            if (vtx_mask[vtx_idx] == 1.0f) {
+                int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1));
+                int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1));
+
+                for (int c = 0; c < texture_channel; ++c) {
+                    new_texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c] = vtx_color[vtx_idx][c];
+                }
+                new_mask_ptr[uv_u * texture_width + uv_v] = 255;
+            }
+        }
+    }
+
+    new_texture.resize({texture_height, texture_width, 3});
+    new_mask.resize({texture_height, texture_width});
+  return std::make_pair(new_texture, new_mask);
+}
+
+
+std::pair<py::array_t<float>, py::array_t<uint8_t>> meshVerticeInpaint(py::array_t<float> texture,
+          py::array_t<uint8_t> mask,
+          py::array_t<float> vtx_pos, py::array_t<float> vtx_uv,
+          py::array_t<int> pos_idx, py::array_t<int> uv_idx, const std::string& method = "smooth") {
+    if (method == "smooth") {
+        return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx);
+    } else {
+        throw std::invalid_argument("Invalid method. Use 'smooth'.");
+    }
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("meshVerticeInpaint", &meshVerticeInpaint, "Mesh-aware texture inpainting",
+          py::arg("texture"), py::arg("mask"),
+          py::arg("vtx_pos"), py::arg("vtx_uv"),
+          py::arg("pos_idx"), py::arg("uv_idx"),
+          py::arg("method") = "smooth");
+}

python/sglang/multimodal_gen/registry.py

@@ -45,6 +45,9 @@ from sglang.multimodal_gen.configs.pipeline_configs.flux import (
 from sglang.multimodal_gen.configs.pipeline_configs.glm_image import (
     GlmImagePipelineConfig,
 )
+from sglang.multimodal_gen.configs.pipeline_configs.hunyuan3d import (
+    Hunyuan3D2PipelineConfig,
+)
 from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import LTX2PipelineConfig
 from sglang.multimodal_gen.configs.pipeline_configs.mova import (
     MOVA360PConfig,
@@ -75,6 +78,7 @@ from sglang.multimodal_gen.configs.sample.hunyuan import (
     FastHunyuanSamplingParam,
     HunyuanSamplingParams,
 )
+from sglang.multimodal_gen.configs.sample.hunyuan3d import Hunyuan3DSamplingParams
 from sglang.multimodal_gen.configs.sample.ltx_2 import LTX2SamplingParams
 from sglang.multimodal_gen.configs.sample.mova import (
     MOVA_360P_SamplingParams,
@@ -367,26 +371,36 @@ def get_model_info(
     # 1. Discover all available pipeline classes and cache them
     _discover_and_register_pipelines()
 
-    # 2. Get pipeline class from model's model_index.json
-    try:
-        if os.path.exists(model_path):
-            config = verify_model_config_and_directory(model_path)
-        else:
-            config = maybe_download_model_index(model_path)
-    except Exception as e:
-        logger.error(f"Could not read model config for '{model_path}': {e}")
-        if backend == Backend.AUTO:
-            logger.info("Falling back to diffusers backend")
-            return _get_diffusers_model_info(model_path)
-        return None
+    # 2. Get pipeline class - check non-diffusers models first
+    pipeline_class_name = get_non_diffusers_pipeline_name(model_path)
+    if pipeline_class_name:
+        # Known non-diffusers model, skip model_index.json download
+        logger.debug(
+            f"Using registered pipeline '{pipeline_class_name}' for non-diffusers model '{model_path}'"
+        )
+    else:
+        # Try to get from model_index.json
+        try:
+            if os.path.exists(model_path):
+                config = verify_model_config_and_directory(model_path)
+            else:
+                config = maybe_download_model_index(model_path)
+        except Exception as e:
+            logger.error(f"Could not read model config for '{model_path}': {e}")
+            if backend == Backend.AUTO:
+                logger.info("Falling back to diffusers backend")
+                return _get_diffusers_model_info(model_path)
+            return None
 
-    pipeline_class_name = config.get("_class_name")
-    if not pipeline_class_name:
-        logger.error(f"'_class_name' not found in model_index.json for '{model_path}'")
-        if backend == Backend.AUTO:
-            logger.info("Falling back to diffusers backend")
-            return _get_diffusers_model_info(model_path)
-        return None
+        pipeline_class_name = config.get("_class_name")
+        if not pipeline_class_name:
+            logger.error(
+                f"'_class_name' not found in model_index.json for '{model_path}'"
+            )
+            if backend == Backend.AUTO:
+                logger.info("Falling back to diffusers backend")
+                return _get_diffusers_model_info(model_path)
+            return None
 
     pipeline_cls = _PIPELINE_REGISTRY.get(pipeline_class_name)
     if not pipeline_cls:
@@ -453,7 +467,7 @@ def _register_configs():
         hf_model_paths=[
             "hunyuanvideo-community/HunyuanVideo",
         ],
-        model_detectors=[lambda hf_id: "hunyuan" in hf_id.lower()],
+        model_detectors=[lambda hf_id: "hunyuanvideo" in hf_id.lower()],
     )
     register_configs(
         sampling_param_cls=FastHunyuanSamplingParam,
@@ -658,6 +672,37 @@ def _register_configs():
         pipeline_config_cls=GlmImagePipelineConfig,
         model_detectors=[lambda hf_id: "glm-image" in hf_id.lower()],
     )
+    register_configs(
+        sampling_param_cls=Hunyuan3DSamplingParams,
+        pipeline_config_cls=Hunyuan3D2PipelineConfig,
+        hf_model_paths=[
+            "tencent/Hunyuan3D-2",
+        ],
+        model_detectors=[lambda hf_id: "hunyuan3d" in hf_id.lower()],
+    )
 
 
 _register_configs()
+
+
+# Known non-diffusers multimodal model patterns
+# Maps pattern -> pipeline_name for models that don't have model_index.json
+_NON_DIFFUSERS_MULTIMODAL_PATTERNS: Dict[str, str] = {
+    "hunyuan3d": "Hunyuan3D2Pipeline",
+}
+
+
+def is_known_non_diffusers_multimodal_model(model_path: str) -> bool:
+    model_path_lower = model_path.lower()
+    return any(
+        pattern in model_path_lower for pattern in _NON_DIFFUSERS_MULTIMODAL_PATTERNS
+    )
+
+
+def get_non_diffusers_pipeline_name(model_path: str) -> Optional[str]:
+    """Get the pipeline name for a known non-diffusers model."""
+    model_path_lower = model_path.lower()
+    for pattern, pipeline_name in _NON_DIFFUSERS_MULTIMODAL_PATTERNS.items():
+        if pattern in model_path_lower:
+            return pipeline_name
+    return None

python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py

@@ -13,7 +13,10 @@ import os
 import time
 from typing import Any, List, Union
 
-from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
+from sglang.multimodal_gen.configs.sample.sampling_params import (
+    DataType,
+    SamplingParams,
+)
 from sglang.multimodal_gen.runtime.entrypoints.utils import (
     GenerationResult,
     ListLorasReq,
@@ -227,6 +230,19 @@ class DiffGenerator:
                             )
                         continue
 
+                    if req.data_type == DataType.MESH:
+                        for output_idx, sample in enumerate(
+                            output_batch.output_file_paths
+                        ):
+                            results.append(
+                                GenerationResult(
+                                    **common,
+                                    prompt_index=output_idx,
+                                    output_file_path=sample,
+                                )
+                            )
+                        continue
+
                     samples_out: list[Any] = []
                     audios_out: list[Any] = []
                     frames_out: list[Any] = []

python/sglang/multimodal_gen/runtime/entrypoints/http_server.py

@@ -214,11 +214,12 @@ def create_app(server_args: ServerArgs):
     app.include_router(health_router)
     app.include_router(vertex_router)
 
-    from sglang.multimodal_gen.runtime.entrypoints.openai import common_api
+    from sglang.multimodal_gen.runtime.entrypoints.openai import common_api, mesh_api
 
     app.include_router(common_api.router)
     app.include_router(image_api.router)
     app.include_router(video_api.router)
+    app.include_router(mesh_api.router)
     app.include_router(weights_api.router)
 
     app.state.server_args = server_args

python/sglang/multimodal_gen/runtime/entrypoints/openai/mesh_api.py

@@ -0,0 +1,296 @@
+import asyncio
+import os
+import time
+from typing import Any, Dict, List, Optional
+
+from fastapi import (
+    APIRouter,
+    File,
+    Form,
+    HTTPException,
+    Path,
+    Query,
+    Request,
+    UploadFile,
+)
+from fastapi.responses import FileResponse
+
+from sglang.multimodal_gen.configs.sample.sampling_params import (
+    SamplingParams,
+    generate_request_id,
+)
+from sglang.multimodal_gen.runtime.entrypoints.openai.protocol import (
+    MeshGenerationsRequest,
+    MeshListResponse,
+    MeshResponse,
+)
+from sglang.multimodal_gen.runtime.entrypoints.openai.storage import cloud_storage
+from sglang.multimodal_gen.runtime.entrypoints.openai.stores import MESH_STORE
+from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
+    add_common_data_to_response,
+    merge_image_input_list,
+    process_generation_batch,
+    save_image_to_path,
+)
+from sglang.multimodal_gen.runtime.entrypoints.utils import prepare_request
+from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
+from sglang.multimodal_gen.runtime.server_args import get_global_server_args
+from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+
+logger = init_logger(__name__)
+router = APIRouter(prefix="/v1/meshes", tags=["meshes"])
+
+
+def _normalize_format(fmt: Optional[str]) -> str:
+    fmt = (fmt or "glb").lower()
+    return fmt if fmt in ("glb", "obj") else "glb"
+
+
+def _build_sampling_params_from_request(
+    request_id: str, req: MeshGenerationsRequest, image_path: Optional[str] = None
+) -> SamplingParams:
+    ext = _normalize_format(req.output_format)
+
+    server_args = get_global_server_args()
+    sampling_kwargs: Dict[str, Any] = {
+        "request_id": request_id,
+        "prompt": req.prompt,
+        "num_frames": 1,
+        "image_path": [image_path] if image_path else None,
+        "save_output": True,
+        "output_file_name": f"{request_id}.{ext}",
+        "seed": req.seed,
+        "generator_device": req.generator_device,
+    }
+    if req.num_inference_steps is not None:
+        sampling_kwargs["num_inference_steps"] = req.num_inference_steps
+    if req.guidance_scale is not None:
+        sampling_kwargs["guidance_scale"] = req.guidance_scale
+    if req.negative_prompt is not None:
+        sampling_kwargs["negative_prompt"] = req.negative_prompt
+
+    return SamplingParams.from_user_sampling_params_args(
+        model_path=server_args.model_path,
+        server_args=server_args,
+        **sampling_kwargs,
+    )
+
+
+def _mesh_job_from_sampling(
+    request_id: str, req: MeshGenerationsRequest, sampling: SamplingParams
+) -> Dict[str, Any]:
+    return {
+        "id": request_id,
+        "object": "mesh",
+        "model": req.model or "",
+        "status": "queued",
+        "progress": 0,
+        "created_at": int(time.time()),
+        "format": _normalize_format(req.output_format),
+        "file_path": os.path.abspath(sampling.output_file_path()),
+    }
+
+
+async def _dispatch_job_async(job_id: str, batch: Req) -> None:
+    from sglang.multimodal_gen.runtime.scheduler_client import async_scheduler_client
+
+    try:
+        save_file_path_list, result = await process_generation_batch(
+            async_scheduler_client, batch
+        )
+        save_file_path = save_file_path_list[0]
+
+        file_size = None
+        if os.path.exists(save_file_path):
+            file_size = os.path.getsize(save_file_path)
+
+        cloud_url = await cloud_storage.upload_and_cleanup(save_file_path)
+
+        update_fields: Dict[str, Any] = {
+            "status": "completed",
+            "progress": 100,
+            "completed_at": int(time.time()),
+            "url": cloud_url,
+            "file_path": save_file_path if not cloud_url else None,
+            "file_size_bytes": file_size,
+        }
+        update_fields = add_common_data_to_response(
+            update_fields, request_id=job_id, result=result
+        )
+        await MESH_STORE.update_fields(job_id, update_fields)
+    except Exception as e:
+        logger.error(f"{e}")
+        await MESH_STORE.update_fields(
+            job_id, {"status": "failed", "error": {"message": str(e)}}
+        )
+
+
+@router.post("", response_model=MeshResponse)
+async def create_mesh(
+    request: Request,
+    image: Optional[List[UploadFile]] = File(None),
+    image_array: Optional[List[UploadFile]] = File(None, alias="image[]"),
+    url: Optional[List[str]] = Form(None),
+    url_array: Optional[List[str]] = Form(None, alias="url[]"),
+    prompt: Optional[str] = Form("generate 3d mesh"),
+    model: Optional[str] = Form(None),
+    seed: Optional[int] = Form(None),
+    generator_device: Optional[str] = Form("cuda"),
+    guidance_scale: Optional[float] = Form(None),
+    num_inference_steps: Optional[int] = Form(None),
+    negative_prompt: Optional[str] = Form(None),
+    output_format: Optional[str] = Form("glb"),
+):
+    content_type = request.headers.get("content-type", "").lower()
+    request_id = generate_request_id()
+    server_args = get_global_server_args()
+
+    input_path = None
+
+    if "multipart/form-data" in content_type:
+        images = image or image_array
+        urls = url or url_array
+        image_list = merge_image_input_list(images, urls)
+
+        if not image_list:
+            raise HTTPException(
+                status_code=422,
+                detail="Field 'image' or 'url' is required for mesh generation",
+            )
+
+        uploads_dir = os.path.join("outputs", "uploads")
+        os.makedirs(uploads_dir, exist_ok=True)
+        img = image_list[0]
+        filename = img.filename if hasattr(img, "filename") else "input_image"
+        try:
+            input_path = await save_image_to_path(
+                img, os.path.join(uploads_dir, f"{request_id}_{filename}")
+            )
+        except Exception as e:
+            raise HTTPException(
+                status_code=400, detail=f"Failed to process image source: {str(e)}"
+            )
+
+        req = MeshGenerationsRequest(
+            prompt=prompt or "generate 3d mesh",
+            model=model,
+            seed=seed,
+            generator_device=generator_device,
+            num_inference_steps=num_inference_steps,
+            negative_prompt=negative_prompt,
+            output_format=output_format,
+            **(
+                {"guidance_scale": guidance_scale} if guidance_scale is not None else {}
+            ),
+        )
+    else:
+        try:
+            body = await request.json()
+        except Exception:
+            body = {}
+        try:
+            payload: Dict[str, Any] = dict(body or {})
+
+            if payload.get("input_image"):
+                img_src = payload.pop("input_image")
+                uploads_dir = os.path.join("outputs", "uploads")
+                os.makedirs(uploads_dir, exist_ok=True)
+                input_path = await save_image_to_path(
+                    img_src,
+                    os.path.join(uploads_dir, f"{request_id}_input_image"),
+                )
+
+            req = MeshGenerationsRequest(**payload)
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"Invalid request body: {e}")
+
+    if not input_path:
+        raise HTTPException(
+            status_code=422,
+            detail="An input image is required for mesh generation",
+        )
+
+    sampling_params = _build_sampling_params_from_request(request_id, req, input_path)
+    job = _mesh_job_from_sampling(request_id, req, sampling_params)
+    await MESH_STORE.upsert(request_id, job)
+
+    batch = prepare_request(
+        server_args=server_args,
+        sampling_params=sampling_params,
+    )
+
+    asyncio.create_task(_dispatch_job_async(request_id, batch))
+    return MeshResponse(**job)
+
+
+@router.get("", response_model=MeshListResponse)
+async def list_meshes(
+    after: Optional[str] = Query(None),
+    limit: Optional[int] = Query(None, ge=1, le=100),
+    order: Optional[str] = Query("desc"),
+):
+    order = (order or "desc").lower()
+    if order not in ("asc", "desc"):
+        order = "desc"
+    jobs = await MESH_STORE.list_values()
+
+    reverse = order != "asc"
+    jobs.sort(key=lambda j: j.get("created_at", 0), reverse=reverse)
+
+    if after is not None:
+        try:
+            idx = next(i for i, j in enumerate(jobs) if j["id"] == after)
+            jobs = jobs[idx + 1 :]
+        except StopIteration:
+            jobs = []
+
+    if limit is not None:
+        jobs = jobs[:limit]
+    items = [MeshResponse(**j) for j in jobs]
+    return MeshListResponse(data=items)
+
+
+@router.get("/{mesh_id}", response_model=MeshResponse)
+async def retrieve_mesh(mesh_id: str = Path(...)):
+    job = await MESH_STORE.get(mesh_id)
+    if not job:
+        raise HTTPException(status_code=404, detail="Mesh not found")
+    return MeshResponse(**job)
+
+
+@router.delete("/{mesh_id}", response_model=MeshResponse)
+async def delete_mesh(mesh_id: str = Path(...)):
+    job = await MESH_STORE.pop(mesh_id)
+    if not job:
+        raise HTTPException(status_code=404, detail="Mesh not found")
+    job["status"] = "deleted"
+    return MeshResponse(**job)
+
+
+@router.get("/{mesh_id}/content")
+async def download_mesh_content(
+    mesh_id: str = Path(...), variant: Optional[str] = Query(None)
+):
+    job = await MESH_STORE.get(mesh_id)
+    if not job:
+        raise HTTPException(status_code=404, detail="Mesh not found")
+
+    if job.get("url"):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Mesh has been uploaded to cloud storage. Please use the cloud URL: {job.get('url')}",
+        )
+
+    file_path = job.get("file_path")
+    if not file_path or not os.path.exists(file_path):
+        raise HTTPException(status_code=404, detail="Generation is still in-progress")
+
+    ext = os.path.splitext(file_path)[1].lower()
+    media_type = {
+        ".glb": "model/gltf-binary",
+        ".obj": "text/plain",
+    }.get(ext, "application/octet-stream")
+
+    return FileResponse(
+        path=file_path, media_type=media_type, filename=os.path.basename(file_path)
+    )

python/sglang/multimodal_gen/runtime/entrypoints/openai/protocol.py

@@ -110,6 +110,42 @@ class VideoRemixRequest(BaseModel):
     prompt: str
 
 
+# Mesh API protocol models
+class MeshResponse(BaseModel):
+    id: str
+    object: str = "mesh"
+    model: str = ""
+    status: str = "queued"
+    progress: int = 0
+    created_at: int = Field(default_factory=lambda: int(time.time()))
+    format: str = "glb"
+    url: Optional[str] = None
+    completed_at: Optional[int] = None
+    expires_at: Optional[int] = None
+    error: Optional[Dict[str, Any]] = None
+    file_path: Optional[str] = None
+    file_size_bytes: Optional[int] = None
+    peak_memory_mb: Optional[float] = None
+    inference_time_s: Optional[float] = None
+
+
+class MeshGenerationsRequest(BaseModel):
+    prompt: str = "generate 3d mesh"
+    input_image: Optional[str] = None
+    model: Optional[str] = None
+    seed: Optional[int] = None
+    generator_device: Optional[str] = "cuda"
+    num_inference_steps: Optional[int] = None
+    guidance_scale: Optional[float] = None
+    negative_prompt: Optional[str] = None
+    output_format: Optional[str] = "glb"
+
+
+class MeshListResponse(BaseModel):
+    data: List[MeshResponse]
+    object: str = "list"
+
+
 @dataclass
 class BaseReq(ABC):
     rid: Optional[Union[str, List[str]]] = field(default=None, kw_only=True)

python/sglang/multimodal_gen/runtime/entrypoints/openai/storage.py

@@ -56,6 +56,8 @@ class CloudStorage:
                 ".jpeg": "image/jpeg",
                 ".webp": "image/webp",
                 ".mp4": "video/mp4",
+                ".glb": "model/gltf-binary",
+                ".obj": "text/plain",
             }.get(ext, "application/octet-stream")
 
             # Use the client created once in __init__

python/sglang/multimodal_gen/runtime/entrypoints/openai/stores.py

@@ -45,3 +45,4 @@ class AsyncDictStore:
 # [request_id, dict]
 VIDEO_STORE = AsyncDictStore()
 IMAGE_STORE = AsyncDictStore()
+MESH_STORE = AsyncDictStore()

python/sglang/multimodal_gen/runtime/managers/gpu_worker.py

@@ -255,22 +255,23 @@ class GPUWorker:
             # Save output to file and return file path only if requested. Avoid the serialization
             # and deserialization overhead between scheduler_client and gpu_worker.
             if req.save_output and req.return_file_paths_only and self.rank == 0:
-                output_paths = save_outputs(
-                    output_batch.output,
-                    req.data_type,
-                    req.fps,
-                    True,
-                    lambda idx: req.output_file_path(len(output_batch.output), idx),
-                    audio=output_batch.audio,
-                    audio_sample_rate=output_batch.audio_sample_rate,
-                    output_compression=req.output_compression,
-                    enable_frame_interpolation=req.enable_frame_interpolation,
-                    frame_interpolation_exp=req.frame_interpolation_exp,
-                    frame_interpolation_scale=req.frame_interpolation_scale,
-                    frame_interpolation_model_path=req.frame_interpolation_model_path,
-                )
-                output_batch.output_file_paths = output_paths
-                output_batch.output = None
+                if output_batch.output is not None:
+                    output_paths = save_outputs(
+                        output_batch.output,
+                        req.data_type,
+                        req.fps,
+                        True,
+                        lambda idx: req.output_file_path(len(output_batch.output), idx),
+                        audio=output_batch.audio,
+                        audio_sample_rate=output_batch.audio_sample_rate,
+                        output_compression=req.output_compression,
+                        enable_frame_interpolation=req.enable_frame_interpolation,
+                        frame_interpolation_exp=req.frame_interpolation_exp,
+                        frame_interpolation_scale=req.frame_interpolation_scale,
+                        frame_interpolation_model_path=req.frame_interpolation_model_path,
+                    )
+                    output_batch.output_file_paths = output_paths
+                    output_batch.output = None
 
             # TODO: extract to avoid duplication
             if req.perf_dump_path is not None or envs.SGLANG_DIFFUSION_STAGE_LOGGING:

python/sglang/multimodal_gen/runtime/models/encoders/hunyuan3d.py

@@ -0,0 +1,264 @@
+# Copied and adapted from: https://github.com/Tencent-Hunyuan/Hunyuan3D-2
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import (
+    CLIPVisionConfig,
+    CLIPVisionModelWithProjection,
+    Dinov2Config,
+    Dinov2Model,
+)
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    return np.concatenate([emb_sin, emb_cos], axis=1)
+
+
+class ImageEncoder(nn.Module):
+    MODEL_CLASS = None
+    MODEL_CONFIG_CLASS = None
+    mean = []
+    std = []
+
+    def __init__(
+        self,
+        version=None,
+        config=None,
+        use_cls_token=True,
+        image_size=224,
+        **kwargs,
+    ):
+        super().__init__()
+
+        if config is None:
+            self.model = self.MODEL_CLASS.from_pretrained(version)
+        else:
+            self.model = self.MODEL_CLASS(self.MODEL_CONFIG_CLASS.from_dict(config))
+        self.model.eval()
+        self.model.requires_grad_(False)
+        self.use_cls_token = use_cls_token
+        self.size = image_size // 14
+        self.num_patches = (image_size // 14) ** 2
+        if self.use_cls_token:
+            self.num_patches += 1
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(
+                    image_size, transforms.InterpolationMode.BILINEAR, antialias=True
+                ),
+                transforms.CenterCrop(image_size),
+                transforms.Normalize(
+                    mean=self.mean,
+                    std=self.std,
+                ),
+            ]
+        )
+
+    def forward(self, image, mask=None, value_range=(-1, 1), **kwargs):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.model.device, dtype=self.model.dtype)
+        inputs = self.transform(image)
+        outputs = self.model(inputs)
+
+        last_hidden_state = outputs.last_hidden_state
+        if not self.use_cls_token:
+            last_hidden_state = last_hidden_state[:, 1:, :]
+
+        return last_hidden_state
+
+    def unconditional_embedding(self, batch_size, **kwargs):
+        device = next(self.model.parameters()).device
+        dtype = next(self.model.parameters()).dtype
+        zero = torch.zeros(
+            batch_size,
+            self.num_patches,
+            self.model.config.hidden_size,
+            device=device,
+            dtype=dtype,
+        )
+
+        return zero
+
+
+class CLIPImageEncoder(ImageEncoder):
+    MODEL_CLASS = CLIPVisionModelWithProjection
+    MODEL_CONFIG_CLASS = CLIPVisionConfig
+    mean = [0.48145466, 0.4578275, 0.40821073]
+    std = [0.26862954, 0.26130258, 0.27577711]
+
+
+class DinoImageEncoder(ImageEncoder):
+    MODEL_CLASS = Dinov2Model
+    MODEL_CONFIG_CLASS = Dinov2Config
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+
+
+class DinoImageEncoderMV(DinoImageEncoder):
+    _aliases = [
+        "hy3dshape.models.conditioner.DinoImageEncoderMV",
+    ]
+
+    def __init__(
+        self,
+        version=None,
+        config=None,
+        use_cls_token=True,
+        image_size=224,
+        view_num=4,
+        **kwargs,
+    ):
+        super().__init__(version, config, use_cls_token, image_size, **kwargs)
+        self.view_num = view_num
+        self.num_patches = self.num_patches
+        pos = np.arange(self.view_num, dtype=np.float32)
+        view_embedding = torch.from_numpy(
+            get_1d_sincos_pos_embed_from_grid(self.model.config.hidden_size, pos)
+        ).float()
+
+        view_embedding = view_embedding.unsqueeze(1).repeat(1, self.num_patches, 1)
+        self.view_embed = view_embedding.unsqueeze(0)
+
+    def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None, **kwargs):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.model.device, dtype=self.model.dtype)
+
+        bs, num_views, c, h, w = image.shape
+        image = image.view(bs * num_views, c, h, w)
+
+        inputs = self.transform(image)
+        outputs = self.model(inputs)
+
+        last_hidden_state = outputs.last_hidden_state
+        last_hidden_state = last_hidden_state.view(
+            bs, num_views, last_hidden_state.shape[-2], last_hidden_state.shape[-1]
+        )
+
+        view_embedding = self.view_embed.to(last_hidden_state.dtype).to(
+            last_hidden_state.device
+        )
+        if view_idxs is not None:
+            assert len(view_idxs) == bs
+            view_embeddings = []
+            for i in range(bs):
+                view_idx = view_idxs[i]
+                assert num_views == len(view_idx)
+                view_embeddings.append(self.view_embed[:, view_idx, ...])
+            view_embedding = (
+                torch.cat(view_embeddings, 0)
+                .to(last_hidden_state.dtype)
+                .to(last_hidden_state.device)
+            )
+
+        if num_views != self.view_num:
+            view_embedding = view_embedding[:, :num_views, ...]
+        last_hidden_state = last_hidden_state + view_embedding
+        last_hidden_state = last_hidden_state.view(
+            bs, num_views * last_hidden_state.shape[-2], last_hidden_state.shape[-1]
+        )
+        return last_hidden_state
+
+    def unconditional_embedding(self, batch_size, view_idxs, **kwargs):
+        device = next(self.model.parameters()).device
+        dtype = next(self.model.parameters()).dtype
+        zero = torch.zeros(
+            batch_size,
+            self.num_patches * len(view_idxs[0]),
+            self.model.config.hidden_size,
+            device=device,
+            dtype=dtype,
+        )
+        return zero
+
+
+def build_image_encoder(config):
+    if config["type"] == "CLIPImageEncoder":
+        return CLIPImageEncoder(**config["kwargs"])
+    elif config["type"] == "DinoImageEncoder":
+        return DinoImageEncoder(**config["kwargs"])
+    elif config["type"] == "DinoImageEncoderMV":
+        return DinoImageEncoderMV(**config["kwargs"])
+    else:
+        raise ValueError(f'Unknown image encoder type: {config["type"]}')
+
+
+class DualImageEncoder(nn.Module):
+    def __init__(
+        self,
+        main_image_encoder,
+        additional_image_encoder,
+    ):
+        super().__init__()
+        self.main_image_encoder = build_image_encoder(main_image_encoder)
+        self.additional_image_encoder = build_image_encoder(additional_image_encoder)
+
+    def forward(self, image, mask=None, **kwargs):
+        outputs = {
+            "main": self.main_image_encoder(image, mask=mask, **kwargs),
+            "additional": self.additional_image_encoder(image, mask=mask, **kwargs),
+        }
+        return outputs
+
+    def unconditional_embedding(self, batch_size, **kwargs):
+        outputs = {
+            "main": self.main_image_encoder.unconditional_embedding(
+                batch_size, **kwargs
+            ),
+            "additional": self.additional_image_encoder.unconditional_embedding(
+                batch_size, **kwargs
+            ),
+        }
+        return outputs
+
+
+class SingleImageEncoder(nn.Module):
+    def __init__(
+        self,
+        main_image_encoder,
+    ):
+        super().__init__()
+        self.main_image_encoder = build_image_encoder(main_image_encoder)
+
+    def forward(self, image, mask=None, **kwargs):
+        outputs = {
+            "main": self.main_image_encoder(image, mask=mask, **kwargs),
+        }
+        return outputs
+
+    def unconditional_embedding(self, batch_size, **kwargs):
+        outputs = {
+            "main": self.main_image_encoder.unconditional_embedding(
+                batch_size, **kwargs
+            ),
+        }
+        return outputs
+
+
+# Entry class for model registry
+EntryClass = [
+    SingleImageEncoder,
+    DualImageEncoder,
+    DinoImageEncoder,
+    DinoImageEncoderMV,
+]

python/sglang/multimodal_gen/runtime/models/registry.py

@@ -37,10 +37,27 @@ _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
     "CLIPVisionModelWithProjection": ("encoders", "clip", "CLIPVisionModel"),
 }
 
+# Global alias mapping: external_path -> canonical_class_name
+_ALIAS_TO_MODEL: dict[str, str] = {}
+
+
+def _parse_aliases_from_ast(value_node: ast.expr) -> list[str]:
+    """Parse _aliases list from AST node."""
+    aliases = []
+    if isinstance(value_node, (ast.List, ast.Tuple)):
+        for elt in value_node.elts:
+            if isinstance(elt, ast.Constant) and isinstance(elt.value, str):
+                aliases.append(elt.value)
+    return aliases
+
 
 @lru_cache(maxsize=None)
 def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
-    discovered_models = _IMAGE_ENCODER_MODELS
+    discovered_models = dict(_IMAGE_ENCODER_MODELS)
+
+    # Collect class definitions with their _aliases
+    class_aliases: dict[str, list[str]] = {}
+
     for component in COMPONENT_DIRS:
         component_path = os.path.join(MODELS_PATH, component)
         for filename in os.listdir(component_path):
@@ -57,7 +74,25 @@ def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
                 entry_class_node = None
                 first_class_def = None
 
+                # Collect all class definitions and their _aliases
+                file_class_aliases: dict[str, list[str]] = {}
                 for node in ast.walk(tree):
+                    if isinstance(node, ast.ClassDef):
+                        if first_class_def is None:
+                            first_class_def = node
+                        # Look for _aliases in the class body
+                        for class_body_node in node.body:
+                            if isinstance(class_body_node, ast.Assign):
+                                for target in class_body_node.targets:
+                                    if (
+                                        isinstance(target, ast.Name)
+                                        and target.id == "_aliases"
+                                    ):
+                                        aliases = _parse_aliases_from_ast(
+                                            class_body_node.value
+                                        )
+                                        if aliases:
+                                            file_class_aliases[node.name] = aliases
                     if isinstance(node, ast.Assign):
                         for target in node.targets:
                             if (
@@ -66,8 +101,7 @@ def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
                             ):
                                 entry_class_node = node
                                 break
-                    if first_class_def is None and isinstance(node, ast.ClassDef):
-                        first_class_def = node
+
                 if entry_class_node and first_class_def:
                     model_cls_name_list = []
                     value_node = entry_class_node.value
@@ -95,10 +129,25 @@ def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
                                 mod_relname,
                                 model_cls_str,
                             )
+                            # Collect aliases for this class
+                            if model_cls_str in file_class_aliases:
+                                class_aliases[model_cls_str] = file_class_aliases[
+                                    model_cls_str
+                                ]
 
             except Exception as e:
                 logger.warning(f"Could not parse {filepath} to find models: {e}")
 
+    # Build alias -> canonical class name mapping
+    for class_name, aliases in class_aliases.items():
+        for alias in aliases:
+            if alias in _ALIAS_TO_MODEL:
+                logger.warning(
+                    f"Alias '{alias}' already registered for '{_ALIAS_TO_MODEL[alias]}', "
+                    f"will be overwritten by '{class_name}'"
+                )
+            _ALIAS_TO_MODEL[alias] = class_name
+
     return discovered_models
 
 
@@ -242,6 +291,13 @@ class _ModelRegistry:
     def get_supported_archs(self) -> Set[str]:
         return self.registered_models.keys()
 
+    def resolve_by_alias(self, alias: str) -> type[nn.Module] | None:
+        """Resolve a model class by its alias (external module path)."""
+        if alias in _ALIAS_TO_MODEL:
+            canonical_name = _ALIAS_TO_MODEL[alias]
+            return self._try_load_model_cls(canonical_name)
+        return None
+
     def register_model(
         self,
         model_arch: str,

python/sglang/multimodal_gen/runtime/models/schedulers/hunyuan3d_scheduler.py

@@ -0,0 +1,371 @@
+# Copied and adapted from: https://github.com/Tencent-Hunyuan/Hunyuan3D-2
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class Hunyuan3DFlowMatchSchedulerOutput(BaseOutput):
+    """Output class for the scheduler's step function."""
+
+    prev_sample: torch.FloatTensor
+
+
+class Hunyuan3DFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    """Euler discrete scheduler for flow matching."""
+
+    # External module path aliases for compatibility with Hunyuan3D configs
+    _aliases = [
+        "hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler",
+        "hy3dshape.schedulers.FlowMatchEulerDiscreteScheduler",
+    ]
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,
+        use_dynamic_shifting: bool = False,
+    ):
+        timesteps = np.linspace(
+            1, num_train_timesteps, num_train_timesteps, dtype=np.float32
+        ).copy()
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+        if not use_dynamic_shifting:
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+
+        self.timesteps = sigmas * num_train_timesteps
+        self._step_index = None
+        self._begin_index = None
+
+        self.sigmas = sigmas.to("cpu")
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+
+    @property
+    def step_index(self) -> Optional[int]:
+        """The index counter for current timestep."""
+        return self._step_index
+
+    @property
+    def begin_index(self) -> Optional[int]:
+        """The index for the first timestep."""
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0):
+        """Set the begin index for the scheduler.
+
+        Args:
+            begin_index: The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def scale_model_input(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Optional[Union[float, torch.FloatTensor]] = None,
+    ) -> torch.FloatTensor:
+        """Identity operation for flow matching (no input scaling needed)."""
+        return sample
+
+    def scale_noise(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        noise: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        """Forward process in flow-matching (add noise to sample)."""
+        sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)
+
+        if sample.device.type == "mps" and torch.is_floating_point(timestep):
+            schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
+            timestep = timestep.to(sample.device, dtype=torch.float32)
+        else:
+            schedule_timesteps = self.timesteps.to(sample.device)
+            timestep = timestep.to(sample.device)
+
+        if self.begin_index is None:
+            step_indices = [
+                self.index_for_timestep(t, schedule_timesteps) for t in timestep
+            ]
+        elif self.step_index is not None:
+            step_indices = [self.step_index] * timestep.shape[0]
+        else:
+            step_indices = [self.begin_index] * timestep.shape[0]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(sample.shape):
+            sigma = sigma.unsqueeze(-1)
+
+        sample = sigma * noise + (1.0 - sigma) * sample
+        return sample
+
+    def _sigma_to_t(self, sigma: float) -> float:
+        """Convert sigma to timestep."""
+        return sigma * self.config.num_train_timesteps
+
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
+        """Apply time shift transformation."""
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
+        """Set the discrete timesteps for the diffusion chain."""
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(
+                "Must pass a value for `mu` when `use_dynamic_shifting` is True"
+            )
+
+        if sigmas is None:
+            self.num_inference_steps = num_inference_steps
+            timesteps = np.linspace(
+                self._sigma_to_t(self.sigma_max),
+                self._sigma_to_t(self.sigma_min),
+                num_inference_steps,
+            )
+            sigmas = timesteps / self.config.num_train_timesteps
+
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift(mu, 1.0, sigmas)
+        else:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+        timesteps = sigmas * self.config.num_train_timesteps
+
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)])
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(
+        self, timestep: float, schedule_timesteps: Optional[torch.Tensor] = None
+    ) -> int:
+        """Find the index for a given timestep."""
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+        pos = 1 if len(indices) > 1 else 0
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep: Union[float, torch.Tensor]):
+        """Initialize step index from timestep."""
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[Hunyuan3DFlowMatchSchedulerOutput, Tuple]:
+        """Predict the sample from the previous timestep."""
+        if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
+            raise ValueError(
+                "Passing integer indices as timesteps is not supported. "
+                "Pass one of `scheduler.timesteps` as a timestep."
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        # Upcast to avoid precision issues
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
+
+        prev_sample = sample + (sigma_next - sigma) * model_output
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return Hunyuan3DFlowMatchSchedulerOutput(prev_sample=prev_sample)
+
+    def __len__(self) -> int:
+        return self.config.num_train_timesteps
+
+
+@dataclass
+class Hunyuan3DConsistencyFlowMatchSchedulerOutput(BaseOutput):
+    """Output for consistency flow matching scheduler."""
+
+    prev_sample: torch.FloatTensor
+    pred_original_sample: torch.FloatTensor
+
+
+class Hunyuan3DConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    """Consistency Flow Matching Euler Discrete Scheduler."""
+
+    # External module path aliases for compatibility with Hunyuan3D configs
+    _aliases = [
+        "hy3dshape.schedulers.Hunyuan3DConsistencyFlowMatchEulerDiscreteScheduler",
+    ]
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        pcm_timesteps: int = 50,
+    ):
+        sigmas = np.linspace(0, 1, num_train_timesteps)
+        step_ratio = num_train_timesteps // pcm_timesteps
+
+        euler_timesteps = (np.arange(1, pcm_timesteps) * step_ratio).round().astype(
+            np.int64
+        ) - 1
+        euler_timesteps = np.asarray([0] + euler_timesteps.tolist())
+
+        self.euler_timesteps = euler_timesteps
+        self.sigmas = sigmas[self.euler_timesteps]
+        self.sigmas = torch.from_numpy(self.sigmas.copy()).to(dtype=torch.float32)
+        self.timesteps = self.sigmas * num_train_timesteps
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")
+
+    @property
+    def step_index(self) -> Optional[int]:
+        return self._step_index
+
+    @property
+    def begin_index(self) -> Optional[int]:
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0):
+        self._begin_index = begin_index
+
+    def scale_model_input(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Optional[Union[float, torch.FloatTensor]] = None,
+    ) -> torch.FloatTensor:
+        """Identity operation for flow matching (no input scaling needed)."""
+        return sample
+
+    def _sigma_to_t(self, sigma: float) -> float:
+        return sigma * self.config.num_train_timesteps
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+    ):
+        """Set timesteps for inference."""
+        self.num_inference_steps = (
+            num_inference_steps if num_inference_steps is not None else len(sigmas)
+        )
+        inference_indices = np.linspace(
+            0, self.config.pcm_timesteps, num=self.num_inference_steps, endpoint=False
+        )
+        inference_indices = np.floor(inference_indices).astype(np.int64)
+        inference_indices = torch.from_numpy(inference_indices).long()
+
+        self.sigmas_ = self.sigmas[inference_indices]
+        timesteps = self.sigmas_ * self.config.num_train_timesteps
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas_ = torch.cat(
+            [self.sigmas_, torch.ones(1, device=self.sigmas_.device)]
+        )
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(
+        self, timestep: float, schedule_timesteps: Optional[torch.Tensor] = None
+    ) -> int:
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+        indices = (schedule_timesteps == timestep).nonzero()
+        pos = 1 if len(indices) > 1 else 0
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep: Union[float, torch.Tensor]):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[Hunyuan3DConsistencyFlowMatchSchedulerOutput, Tuple]:
+        """Perform one step of the consistency flow matching scheduler."""
+        if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
+            raise ValueError("Passing integer indices as timesteps is not supported.")
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas_[self.step_index]
+        sigma_next = self.sigmas_[self.step_index + 1]
+
+        prev_sample = sample + (sigma_next - sigma) * model_output
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        pred_original_sample = sample + (1.0 - sigma) * model_output
+        pred_original_sample = pred_original_sample.to(model_output.dtype)
+
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return Hunyuan3DConsistencyFlowMatchSchedulerOutput(
+            prev_sample=prev_sample, pred_original_sample=pred_original_sample
+        )
+
+    def __len__(self) -> int:
+        return self.config.num_train_timesteps
+
+
+# Entry class for model registry
+EntryClass = [
+    Hunyuan3DFlowMatchEulerDiscreteScheduler,
+    Hunyuan3DConsistencyFlowMatchEulerDiscreteScheduler,
+]

python/sglang/multimodal_gen/runtime/pipelines/hunyuan3d_pipeline.py

@@ -0,0 +1,411 @@
+"""
+Hunyuan3D image-to-mesh pipeline implementation.
+
+Shape pipeline: BeforeDenoising -> Denoising -> Export -> Save
+Paint pipeline (optional): Preprocess -> TexGen -> Postprocess
+"""
+
+from __future__ import annotations
+
+import glob
+import importlib
+import os
+from itertools import chain
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from sglang.multimodal_gen.configs.pipeline_configs.hunyuan3d import (
+    Hunyuan3D2PipelineConfig,
+)
+from sglang.multimodal_gen.runtime.loader.fsdp_load import (
+    load_model_from_full_model_state_dict,
+    set_default_torch_dtype,
+)
+from sglang.multimodal_gen.runtime.loader.utils import get_param_names_mapping
+from sglang.multimodal_gen.runtime.pipelines_core.composed_pipeline_base import (
+    ComposedPipelineBase,
+)
+from sglang.multimodal_gen.runtime.pipelines_core.stages import (
+    Hunyuan3DPaintPostprocessStage,
+    Hunyuan3DPaintPreprocessStage,
+    Hunyuan3DPaintTexGenStage,
+    Hunyuan3DShapeBeforeDenoisingStage,
+    Hunyuan3DShapeDenoisingStage,
+    Hunyuan3DShapeExportStage,
+    Hunyuan3DShapeSaveStage,
+)
+from sglang.multimodal_gen.runtime.server_args import ServerArgs
+from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+
+logger = init_logger(__name__)
+
+
+class Hunyuan3D2Pipeline(ComposedPipelineBase):
+    """Hunyuan3D 2.0 image-to-mesh pipeline.
+
+    Shape pipeline: BeforeDenoising -> Denoising -> Export -> Save
+    Paint pipeline (optional): Preprocess -> TexGen -> Postprocess
+    """
+
+    pipeline_name = "Hunyuan3D2Pipeline"
+    _required_config_modules = [
+        "hy3dshape_model",
+        "hy3dshape_vae",
+        "hy3dshape_scheduler",
+        "hy3dshape_conditioner",
+        "hy3dshape_image_processor",
+    ]
+
+    def _load_config(self) -> dict[str, Any]:
+        return {
+            "_class_name": self.pipeline_name,
+            "_diffusers_version": "0.0.0",
+            "hy3dshape_model": ["diffusers", "Hunyuan3DShapeModel"],
+            "hy3dshape_vae": ["diffusers", "Hunyuan3DShapeVAE"],
+            "hy3dshape_scheduler": ["diffusers", "Hunyuan3DShapeScheduler"],
+            "hy3dshape_conditioner": ["diffusers", "Hunyuan3DShapeConditioner"],
+            "hy3dshape_image_processor": ["diffusers", "Hunyuan3DShapeImageProcessor"],
+        }
+
+    # Class resolution
+    @staticmethod
+    def _resolve_class(target: str) -> Any:
+        """Resolve a YAML target string to a Python class."""
+        from sglang.multimodal_gen.runtime.models.registry import ModelRegistry
+
+        cls = ModelRegistry.resolve_by_alias(target)
+        if cls is not None:
+            return cls
+
+        class_name = target.rsplit(".", 1)[-1]
+        try:
+            cls, _ = ModelRegistry.resolve_model_cls(class_name)
+            return cls
+        except Exception:
+            pass
+
+        from sglang.multimodal_gen.runtime.utils.mesh3d_utils import (
+            resolve_hunyuan3d_tool,
+        )
+
+        for name in (target, class_name):
+            tool_cls = resolve_hunyuan3d_tool(name)
+            if tool_cls is not None:
+                return tool_cls
+
+        module, cls_name = target.rsplit(".", 1)
+        return getattr(importlib.import_module(module, package=None), cls_name)
+
+    # Path / checkpoint resolution
+    @staticmethod
+    def _resolve_shape_dir(
+        model_path: str,
+        subfolder: str,
+        use_safetensors: bool,
+        variant: str | None,
+    ) -> tuple[str, str]:
+        """Locate (or download) the shape subfolder and return (config_path, ckpt_path)."""
+        local_path = os.path.join(model_path, subfolder)
+        if not os.path.exists(local_path):
+            local_path = os.path.expanduser(local_path)
+
+        if not os.path.exists(local_path):
+            logger.info(
+                "Local path %s not found, downloading from HuggingFace Hub",
+                local_path,
+            )
+            from huggingface_hub import snapshot_download
+
+            downloaded = snapshot_download(
+                repo_id=model_path,
+                allow_patterns=[f"{subfolder}/*"],
+            )
+            local_path = os.path.join(downloaded, subfolder)
+
+        config_path = os.path.join(local_path, "config.yaml")
+        if not os.path.exists(config_path):
+            for alt in ("config.yml", "model_config.yaml"):
+                alt_path = os.path.join(local_path, alt)
+                if os.path.exists(alt_path):
+                    config_path = alt_path
+                    break
+
+        if use_safetensors:
+            ckpt_name = (
+                f"model.{variant}.safetensors" if variant else "model.safetensors"
+            )
+        else:
+            ckpt_name = f"model-{variant}.ckpt" if variant else "model.ckpt"
+
+        ckpt_path = os.path.join(local_path, ckpt_name)
+        if not os.path.exists(ckpt_path):
+            pattern = "*.safetensors" if use_safetensors else "*.ckpt"
+            files = glob.glob(os.path.join(local_path, pattern))
+            if files:
+                ckpt_path = files[0]
+
+        logger.info("Config path: %s", config_path)
+        logger.info("Checkpoint path: %s", ckpt_path)
+        return config_path, ckpt_path
+
+    @staticmethod
+    def _resolve_paint_dir(model_path: str, subfolder: str) -> str:
+        """Locate (or download) the paint subfolder and return its local path."""
+        local_path = os.path.join(model_path, subfolder)
+        if not os.path.exists(local_path):
+            local_path = os.path.expanduser(local_path)
+
+        if not os.path.exists(local_path):
+            logger.info(
+                "Local path %s not found, downloading from HuggingFace Hub",
+                local_path,
+            )
+            from huggingface_hub import snapshot_download
+
+            downloaded = snapshot_download(
+                repo_id=model_path,
+                allow_patterns=[f"{subfolder}/*"],
+            )
+            local_path = os.path.join(downloaded, subfolder)
+
+        for subdir in ("vae", "unet"):
+            config_file = os.path.join(local_path, subdir, "config.json")
+            if not os.path.exists(config_file):
+                raise FileNotFoundError(
+                    f"Paint model incomplete: {config_file} not found. "
+                    "Download the model or check network connectivity."
+                )
+
+        logger.info("Resolved paint model directory: %s", local_path)
+        return local_path
+
+    @staticmethod
+    def _load_and_split_checkpoint(
+        ckpt_path: str, use_safetensors: bool
+    ) -> dict[str, dict[str, torch.Tensor]]:
+        """Load a bundled checkpoint and split by the first '.' in each key."""
+        if use_safetensors:
+            import safetensors.torch
+
+            flat = safetensors.torch.load_file(ckpt_path, device="cpu")
+            ckpt: dict[str, dict[str, torch.Tensor]] = {}
+            for key, value in flat.items():
+                component = key.split(".")[0]
+                sub_key = key[len(component) + 1 :]
+                ckpt.setdefault(component, {})[sub_key] = value
+            return ckpt
+        else:
+            return torch.load(ckpt_path, map_location="cpu", weights_only=True)
+
+    # Component loading helpers
+    @classmethod
+    def _load_dit_model(
+        cls,
+        cfg: dict[str, Any],
+        weights: dict[str, torch.Tensor],
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> nn.Module:
+        """Load the DiT model using meta-device instantiation + standard weight loading."""
+        if "target" not in cfg:
+            raise KeyError("Expected key 'target' in model config.")
+        target_cls = cls._resolve_class(cfg["target"])
+        params = cfg.get("params", {})
+
+        if hasattr(target_cls, "build_config_from_params"):
+            dit_config = target_cls.build_config_from_params(params)
+            init_kwargs: dict[str, Any] = {"config": dit_config, "hf_config": {}}
+        else:
+            init_kwargs = params
+
+        with set_default_torch_dtype(dtype), torch.device("meta"):
+            model = target_cls(**init_kwargs)
+
+        weight_iterator = ((k, v) for k, v in weights.items())
+        param_names_mapping_fn = get_param_names_mapping(model.param_names_mapping)
+
+        load_model_from_full_model_state_dict(
+            model,
+            weight_iterator,
+            device,
+            dtype,
+            strict=False,
+            param_names_mapping=param_names_mapping_fn,
+        )
+
+        for name, p in chain(model.named_parameters(), model.named_buffers()):
+            if p.is_meta:
+                raise RuntimeError(f"Unexpected param or buffer {name} on meta device.")
+            if isinstance(p, nn.Parameter):
+                p.requires_grad = False
+
+        return model.eval()
+
+    @classmethod
+    def _load_simple_component(
+        cls,
+        cfg: dict[str, Any],
+        weights: dict[str, torch.Tensor] | None,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> nn.Module:
+        """Load a component (VAE / conditioner) with direct instantiation + state_dict."""
+        if "target" not in cfg:
+            raise KeyError("Expected key 'target' in component config.")
+        target_cls = cls._resolve_class(cfg["target"])
+        params = cfg.get("params", {})
+
+        with set_default_torch_dtype(dtype):
+            component = target_cls(**params)
+
+        if weights is not None:
+            component.load_state_dict(weights, strict=False)
+
+        component.to(device=device, dtype=dtype)
+        return component.eval()
+
+    @classmethod
+    def _instantiate_component(cls, cfg: dict[str, Any]) -> Any:
+        """Instantiate a lightweight component (scheduler / image_processor) without weights."""
+        if "target" not in cfg:
+            raise KeyError("Expected key 'target' in component config.")
+        target_cls = cls._resolve_class(cfg["target"])
+        params = cfg.get("params", {})
+        return target_cls(**params)
+
+    # Module loading override
+    def load_modules(
+        self,
+        server_args: ServerArgs,
+        loaded_modules: dict[str, torch.nn.Module] | None = None,
+    ) -> dict[str, Any]:
+        """Load all Hunyuan3D shape components from a bundled checkpoint."""
+        import yaml
+
+        from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
+
+        config = server_args.pipeline_config
+        if not isinstance(config, Hunyuan3D2PipelineConfig):
+            raise TypeError(f"Expected Hunyuan3D2PipelineConfig, got {type(config)}")
+
+        model_path = config.shape_model_path or server_args.model_path
+
+        logger.info("Loading Hunyuan3D shape models from %s", model_path)
+
+        config_path, ckpt_path = self._resolve_shape_dir(
+            model_path,
+            config.shape_subfolder,
+            config.shape_use_safetensors,
+            config.shape_variant,
+        )
+
+        with open(config_path, "r") as f:
+            model_config = yaml.safe_load(f)
+
+        ckpt = self._load_and_split_checkpoint(ckpt_path, config.shape_use_safetensors)
+
+        dtype = torch.float16
+        if config.shape_variant and "bf16" in config.shape_variant:
+            dtype = torch.bfloat16
+        device = get_local_torch_device()
+
+        components: dict[str, Any] = {}
+
+        components["hy3dshape_model"] = self._load_dit_model(
+            model_config["model"], ckpt["model"], device, dtype
+        )
+
+        components["hy3dshape_vae"] = self._load_simple_component(
+            model_config["vae"], ckpt.get("vae"), device, dtype
+        )
+
+        components["hy3dshape_conditioner"] = self._load_simple_component(
+            model_config["conditioner"], ckpt.get("conditioner"), device, dtype
+        )
+
+        components["hy3dshape_scheduler"] = self._instantiate_component(
+            model_config["scheduler"]
+        )
+        components["hy3dshape_image_processor"] = self._instantiate_component(
+            model_config["image_processor"]
+        )
+
+        logger.info("All Hunyuan3D shape components loaded successfully")
+
+        if config.paint_enable:
+            try:
+                paint_dir = self._resolve_paint_dir(
+                    server_args.model_path, config.paint_subfolder
+                )
+                components["hy3dpaint_dir"] = paint_dir
+            except Exception as e:
+                logger.warning("Failed to resolve paint model path: %s", e)
+
+        return components
+
+    # Pipeline lifecycle
+    def initialize_pipeline(self, server_args: ServerArgs):
+        config = server_args.pipeline_config
+        if not isinstance(config, Hunyuan3D2PipelineConfig):
+            raise TypeError(
+                "Hunyuan3D2Pipeline requires Hunyuan3D2PipelineConfig, "
+                f"got {type(config)}"
+            )
+
+    def create_pipeline_stages(self, server_args: ServerArgs):
+        config = server_args.pipeline_config
+        assert isinstance(config, Hunyuan3D2PipelineConfig)
+
+        # Shape: 4 stages
+        self.add_stage(
+            stage_name="shape_before_denoising",
+            stage=Hunyuan3DShapeBeforeDenoisingStage(
+                image_processor=self.get_module("hy3dshape_image_processor"),
+                conditioner=self.get_module("hy3dshape_conditioner"),
+                vae=self.get_module("hy3dshape_vae"),
+                model=self.get_module("hy3dshape_model"),
+                scheduler=self.get_module("hy3dshape_scheduler"),
+                config=config,
+            ),
+        )
+        self.add_stage(
+            stage_name="shape_denoising",
+            stage=Hunyuan3DShapeDenoisingStage(
+                transformer=self.get_module("hy3dshape_model"),
+                scheduler=self.get_module("hy3dshape_scheduler"),
+            ),
+        )
+        self.add_stage(
+            stage_name="shape_export",
+            stage=Hunyuan3DShapeExportStage(
+                vae=self.get_module("hy3dshape_vae"),
+                config=config,
+            ),
+        )
+        self.add_stage(
+            stage_name="shape_save",
+            stage=Hunyuan3DShapeSaveStage(config=config),
+        )
+
+        # Paint: 3 stages (optional)
+        if config.paint_enable:
+            self.add_stage(
+                stage_name="paint_preprocess",
+                stage=Hunyuan3DPaintPreprocessStage(config=config),
+            )
+            self.add_stage(
+                stage_name="paint_texgen",
+                stage=Hunyuan3DPaintTexGenStage(
+                    config=config,
+                    paint_dir=self.get_module("hy3dpaint_dir"),
+                ),
+            )
+            self.add_stage(
+                stage_name="paint_postprocess",
+                stage=Hunyuan3DPaintPostprocessStage(config=config),
+            )
+
+
+EntryClass = Hunyuan3D2Pipeline

python/sglang/multimodal_gen/runtime/pipelines_core/stages/__init__.py

@@ -27,6 +27,21 @@ from sglang.multimodal_gen.runtime.pipelines_core.stages.denoising_dmd import (
     DmdDenoisingStage,
 )
 from sglang.multimodal_gen.runtime.pipelines_core.stages.encoding import EncodingStage
+
+# Hunyuan3D paint stages
+from sglang.multimodal_gen.runtime.pipelines_core.stages.hunyuan3d_paint import (
+    Hunyuan3DPaintPostprocessStage,
+    Hunyuan3DPaintPreprocessStage,
+    Hunyuan3DPaintTexGenStage,
+)
+
+# Hunyuan3D shape stages
+from sglang.multimodal_gen.runtime.pipelines_core.stages.hunyuan3d_shape import (
+    Hunyuan3DShapeBeforeDenoisingStage,
+    Hunyuan3DShapeDenoisingStage,
+    Hunyuan3DShapeExportStage,
+    Hunyuan3DShapeSaveStage,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.stages.image_encoding import (
     ImageEncodingStage,
     ImageVAEEncodingStage,
@@ -68,4 +83,13 @@ __all__ = [
     "ImageVAEEncodingStage",
     "TextEncodingStage",
     "LTX2TextConnectorStage",
+    # Hunyuan3D shape stages
+    "Hunyuan3DShapeBeforeDenoisingStage",
+    "Hunyuan3DShapeDenoisingStage",
+    "Hunyuan3DShapeExportStage",
+    "Hunyuan3DShapeSaveStage",
+    # Hunyuan3D paint stages
+    "Hunyuan3DPaintPreprocessStage",
+    "Hunyuan3DPaintTexGenStage",
+    "Hunyuan3DPaintPostprocessStage",
 ]

python/sglang/multimodal_gen/runtime/pipelines_core/stages/hunyuan3d_shape.py

@@ -0,0 +1,527 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Hunyuan3D shape generation stages.
+
+Four-stage pipeline: BeforeDenoising -> Denoising -> Export -> Save.
+"""
+
+from __future__ import annotations
+
+import os
+from typing import Any
+
+import numpy as np
+import torch
+
+from sglang.multimodal_gen.configs.pipeline_configs.hunyuan3d import (
+    Hunyuan3D2PipelineConfig,
+)
+from sglang.multimodal_gen.runtime.loader.component_loaders.transformer_loader import (
+    TransformerLoader,
+)
+from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch, Req
+from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage
+from sglang.multimodal_gen.runtime.pipelines_core.stages.denoising import DenoisingStage
+from sglang.multimodal_gen.runtime.pipelines_core.stages.validators import (
+    StageValidators as V,
+)
+from sglang.multimodal_gen.runtime.pipelines_core.stages.validators import (
+    VerificationResult,
+)
+from sglang.multimodal_gen.runtime.server_args import ServerArgs
+from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+from sglang.multimodal_gen.runtime.utils.mesh3d_utils import export_to_trimesh
+
+logger = init_logger(__name__)
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps=None,
+    device=None,
+    timesteps=None,
+    sigmas=None,
+    **kwargs,
+):
+    """Retrieve timesteps from scheduler."""
+    import inspect
+
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of timesteps or sigmas can be passed.")
+
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"Scheduler {scheduler.__class__} doesn't support custom timesteps."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+
+    elif sigmas is not None:
+        accepts_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_sigmas:
+            raise ValueError(
+                f"Scheduler {scheduler.__class__} doesn't support custom sigmas."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+
+    return timesteps, num_inference_steps
+
+
+def _prepare_shape_image(image_processor, image, mask=None) -> dict:
+    """Prepare shape image for conditioning."""
+    if isinstance(image, torch.Tensor) and isinstance(mask, torch.Tensor):
+        return {"image": image, "mask": mask}
+
+    if isinstance(image, str) and not os.path.exists(image):
+        raise FileNotFoundError(f"Couldn't find image at path {image}")
+
+    if not isinstance(image, list):
+        image = [image]
+
+    outputs = [image_processor(img) for img in image]
+    cond_input = {k: [] for k in outputs[0].keys()}
+    for output in outputs:
+        for key, value in output.items():
+            cond_input[key].append(value)
+    for key, value in cond_input.items():
+        if isinstance(value[0], torch.Tensor):
+            cond_input[key] = torch.cat(value, dim=0)
+    return cond_input
+
+
+def _move_to_device(payload, device, dtype):
+    """Recursively move tensors in payload to specified device and dtype."""
+    if isinstance(payload, torch.Tensor):
+        return payload.to(device=device, dtype=dtype)
+    if isinstance(payload, dict):
+        return {k: _move_to_device(v, device, dtype) for k, v in payload.items()}
+    if isinstance(payload, list):
+        return [_move_to_device(v, device, dtype) for v in payload]
+    return payload
+
+
+class Hunyuan3DShapeBeforeDenoisingStage(PipelineStage):
+    """Monolithic pre-processing stage for Hunyuan3D shape generation.
+
+    Consolidates input validation, image preprocessing, conditioning, and
+    latent/timestep preparation into a single stage.
+    """
+
+    def __init__(
+        self,
+        image_processor: Any,
+        conditioner: Any,
+        vae: Any,
+        model: Any,
+        scheduler: Any,
+        config: Hunyuan3D2PipelineConfig,
+    ) -> None:
+        super().__init__()
+        self.image_processor = image_processor
+        self.conditioner = conditioner
+        self.vae = vae
+        self.model = model
+        self.scheduler = scheduler
+        self.config = config
+
+    def _validate_input(self, batch: Req, server_args: ServerArgs) -> None:
+        if batch.image_path is None:
+            raise ValueError("Hunyuan3D requires 'image_path' input.")
+        if isinstance(batch.image_path, list):
+            if len(batch.image_path) != 1:
+                raise ValueError("Hunyuan3D only supports a single image input.")
+            batch.image_path = batch.image_path[0]
+        if not isinstance(batch.image_path, str):
+            raise ValueError(
+                f"Hunyuan3D expects image_path as str, got {type(batch.image_path)}"
+            )
+        if not os.path.exists(batch.image_path):
+            raise FileNotFoundError(f"Image path not found: {batch.image_path}")
+        if batch.num_outputs_per_prompt != 1:
+            raise ValueError("Hunyuan3D only supports num_outputs_per_prompt=1.")
+
+    def _prepare_latents(self, batch_size, dtype, device, generator):
+        from diffusers.utils.torch_utils import randn_tensor
+
+        shape = (batch_size, *self.vae.latent_shape)
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents * getattr(self.scheduler, "init_noise_sigma", 1.0)
+
+    def forward(self, batch: Req, server_args: ServerArgs) -> Req:
+        # 1. Input validation
+        self._validate_input(batch, server_args)
+
+        # 2. Image preprocessing
+        cond_inputs = _prepare_shape_image(self.image_processor, batch.image_path)
+        image = cond_inputs.pop("image")
+
+        device = self.device
+        dtype = next(self.model.parameters()).dtype
+        image = _move_to_device(image, device, dtype)
+        cond_inputs = _move_to_device(cond_inputs, device, dtype)
+
+        # 3. Conditioning with CFG
+        do_cfg = batch.guidance_scale >= 0 and not (
+            hasattr(self.model, "guidance_embed") and self.model.guidance_embed is True
+        )
+
+        cond = self.conditioner(image=image, **cond_inputs)
+        if do_cfg:
+            un_cond = self.conditioner.unconditional_embedding(
+                image.shape[0], **cond_inputs
+            )
+
+            def cat_recursive(a, b):
+                if isinstance(a, torch.Tensor):
+                    return torch.cat([a, b], dim=0).to(dtype)
+                out = {}
+                for key in a.keys():
+                    out[key] = cat_recursive(a[key], b[key])
+                return out
+
+            cond = cat_recursive(cond, un_cond)
+
+        # 4. Latent and timestep preparation
+        batch_size = image.shape[0]
+        sigmas = np.linspace(0, 1, batch.num_inference_steps)
+        timesteps, _ = retrieve_timesteps(
+            self.scheduler,
+            batch.num_inference_steps,
+            device,
+            sigmas=sigmas,
+        )
+
+        generator = batch.generator
+        if generator is None and batch.seed is not None:
+            generator = torch.Generator(device=device).manual_seed(batch.seed)
+
+        latents = self._prepare_latents(batch_size, dtype, device, generator)
+
+        guidance = None
+        if hasattr(self.model, "guidance_embed") and self.model.guidance_embed is True:
+            guidance = torch.tensor(
+                [batch.guidance_scale] * batch_size, device=device, dtype=dtype
+            )
+
+        # 5. Populate batch
+        batch.prompt_embeds = [cond]
+        batch.do_classifier_free_guidance = do_cfg
+        batch.timesteps = timesteps
+        batch.latents = latents
+        batch.extra["shape_guidance"] = guidance
+        batch.extra["shape_image"] = image
+        return batch
+
+    def verify_input(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("image_path", batch.image_path, V.not_none)
+        result.add_check(
+            "num_inference_steps", batch.num_inference_steps, V.positive_int
+        )
+        return result
+
+    def verify_output(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("timesteps", batch.timesteps, [V.is_tensor, V.min_dims(1)])
+        result.add_check("latents", batch.latents, V.is_tensor)
+        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
+        return result
+
+
+class Hunyuan3DShapeDenoisingStage(DenoisingStage):
+    """Denoising stage for Hunyuan3D shape generation."""
+
+    def __init__(self, transformer: Any, scheduler: Any, **kwargs) -> None:
+        super().__init__(transformer=transformer, scheduler=scheduler, **kwargs)
+
+    def _prepare_denoising_loop(self, batch: Req, server_args: ServerArgs):
+        """Prepare Hunyuan3D-specific variables for the base denoising loop."""
+        assert self.transformer is not None
+        pipeline = self.pipeline() if self.pipeline else None
+        cache_dit_num_inference_steps = batch.extra.get(
+            "cache_dit_num_inference_steps", batch.num_inference_steps
+        )
+        if not server_args.model_loaded["transformer"]:
+            loader = TransformerLoader()
+            self.transformer = loader.load(
+                server_args.model_paths["transformer"], server_args, "transformer"
+            )
+            self._maybe_enable_cache_dit(cache_dit_num_inference_steps, batch)
+            self._maybe_enable_torch_compile(self.transformer)
+            if pipeline:
+                pipeline.add_module("transformer", self.transformer)
+            server_args.model_loaded["transformer"] = True
+        else:
+            self._maybe_enable_cache_dit(cache_dit_num_inference_steps, batch)
+
+        timesteps = batch.timesteps
+        if timesteps is None:
+            raise ValueError("Timesteps must be provided")
+
+        latents = batch.latents
+        if latents is None:
+            raise ValueError("Latents must be provided")
+
+        cond = batch.prompt_embeds[0] if batch.prompt_embeds else None
+        if cond is None:
+            raise ValueError("Conditioning (prompt_embeds) must be provided")
+
+        if batch.raw_latent_shape is None:
+            batch.raw_latent_shape = latents.shape
+
+        guidance = batch.extra.get("shape_guidance")
+        num_inference_steps = batch.num_inference_steps
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+
+        extra_step_kwargs = self.prepare_extra_func_kwargs(
+            self.scheduler.step,
+            {"generator": batch.generator, "eta": batch.eta},
+        )
+
+        target_dtype = next(self.transformer.parameters()).dtype
+        autocast_enabled = False
+
+        pos_cond_kwargs = {"encoder_hidden_states": cond}
+        neg_cond_kwargs = {}
+
+        return {
+            "extra_step_kwargs": extra_step_kwargs,
+            "target_dtype": target_dtype,
+            "autocast_enabled": autocast_enabled,
+            "timesteps": timesteps,
+            "num_inference_steps": num_inference_steps,
+            "num_warmup_steps": num_warmup_steps,
+            "image_kwargs": {},
+            "pos_cond_kwargs": pos_cond_kwargs,
+            "neg_cond_kwargs": neg_cond_kwargs,
+            "latents": latents,
+            "prompt_embeds": batch.prompt_embeds,
+            "neg_prompt_embeds": None,
+            "boundary_timestep": None,
+            "z": None,
+            "reserved_frames_mask": None,
+            "seq_len": None,
+            "guidance": guidance,
+        }
+
+    def _predict_noise(
+        self,
+        current_model,
+        latent_model_input,
+        timestep,
+        target_dtype,
+        guidance: torch.Tensor,
+        **kwargs,
+    ):
+        """Hunyuan3D-specific noise prediction with normalized timestep."""
+        cond = kwargs.get("encoder_hidden_states")
+        timestep_norm = timestep / self.scheduler.config.num_train_timesteps
+        return current_model(latent_model_input, timestep_norm, cond, guidance=guidance)
+
+    def _predict_noise_with_cfg(
+        self,
+        current_model,
+        latent_model_input: torch.Tensor,
+        timestep,
+        batch: Req,
+        timestep_index: int,
+        attn_metadata,
+        target_dtype,
+        current_guidance_scale,
+        image_kwargs: dict[str, Any],
+        pos_cond_kwargs: dict[str, Any],
+        neg_cond_kwargs: dict[str, Any],
+        server_args,
+        guidance,
+        latents,
+    ):
+        """Hunyuan3D-specific CFG: concat latents, single forward, then split."""
+        cond = pos_cond_kwargs.get("encoder_hidden_states")
+        do_cfg = batch.do_classifier_free_guidance
+
+        if do_cfg:
+            latent_input = torch.cat([latent_model_input] * 2)
+        else:
+            latent_input = latent_model_input
+
+        timestep_expanded = timestep.expand(latent_input.shape[0]).to(latents.dtype)
+
+        with set_forward_context(
+            current_timestep=timestep_index,
+            attn_metadata=attn_metadata,
+            forward_batch=batch,
+        ):
+            noise_pred = self._predict_noise(
+                current_model=current_model,
+                latent_model_input=latent_input,
+                timestep=timestep_expanded,
+                target_dtype=target_dtype,
+                guidance=guidance,
+                encoder_hidden_states=cond,
+            )
+
+        if do_cfg:
+            noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + current_guidance_scale * (
+                noise_pred_cond - noise_pred_uncond
+            )
+
+        return noise_pred
+
+    def verify_input(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("timesteps", batch.timesteps, [V.is_tensor, V.min_dims(1)])
+        result.add_check("latents", batch.latents, V.is_tensor)
+        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
+        result.add_check(
+            "num_inference_steps", batch.num_inference_steps, V.positive_int
+        )
+        result.add_check("guidance_scale", batch.guidance_scale, V.non_negative_float)
+        return result
+
+    def verify_output(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("latents", batch.latents, V.is_tensor)
+        return result
+
+
+class Hunyuan3DShapeExportStage(PipelineStage):
+    """VAE decoding and mesh extraction stage."""
+
+    def __init__(self, vae: Any, config: Hunyuan3D2PipelineConfig) -> None:
+        super().__init__()
+        self.vae = vae
+        self.config = config
+
+    def forward(self, batch: Req, server_args: ServerArgs) -> Req:
+        if self.config.shape_mc_algo is not None:
+            try:
+                from sglang.multimodal_gen.runtime.models.vaes.hunyuan3d_vae import (
+                    SurfaceExtractors,
+                )
+
+                self.vae.surface_extractor = SurfaceExtractors[
+                    self.config.shape_mc_algo
+                ]()
+            except ImportError:
+                logger.warning(
+                    f"Could not load SurfaceExtractors for mc_algo={self.config.shape_mc_algo}"
+                )
+
+        latents = batch.latents
+
+        if self.config.shape_output_type != "latent":
+            latents = 1.0 / self.vae.scale_factor * latents
+            latents = self.vae(latents)
+
+            outputs = self.vae.latents2mesh(
+                latents,
+                bounds=self.config.shape_box_v,
+                mc_level=self.config.shape_mc_level,
+                num_chunks=self.config.shape_num_chunks,
+                octree_resolution=self.config.shape_octree_resolution,
+                mc_algo=self.config.shape_mc_algo,
+                enable_pbar=False,
+            )
+        else:
+            outputs = latents
+
+        if self.config.shape_output_type == "trimesh":
+            outputs = export_to_trimesh(outputs)
+
+        batch.extra["shape_meshes"] = outputs
+        return batch
+
+    def verify_input(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("latents", batch.latents, V.is_tensor)
+        return result
+
+    def verify_output(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("shape_meshes", batch.extra.get("shape_meshes"), V.not_none)
+        return result
+
+
+class Hunyuan3DShapeSaveStage(PipelineStage):
+    """Mesh file export and output decision stage."""
+
+    def __init__(self, config: Hunyuan3D2PipelineConfig) -> None:
+        super().__init__()
+        self.config = config
+
+    def _get_output_paths(self, batch: Req) -> tuple[str, str]:
+        output_path = batch.output_file_path() or os.path.join(
+            batch.output_path, "output.obj"
+        )
+        if output_path.endswith(".glb"):
+            obj_path = output_path[:-4] + ".obj"
+            return obj_path, output_path
+        if output_path.endswith(".obj"):
+            return output_path, output_path
+        return output_path + ".obj", output_path + ".obj"
+
+    def forward(self, batch: Req, server_args: ServerArgs) -> Req | OutputBatch:
+        mesh_outputs = batch.extra["shape_meshes"]
+        mesh = mesh_outputs[0] if isinstance(mesh_outputs, list) else mesh_outputs
+        if isinstance(mesh, list):
+            mesh = mesh[0]
+
+        if mesh is None:
+            if batch.is_warmup:
+                logger.info(
+                    "Skipping mesh export during warmup "
+                    "(surface extraction returned None)"
+                )
+                batch.extra["_mesh_failed"] = True
+                if self.config.paint_enable:
+                    return batch
+                return OutputBatch(output_file_paths=[], metrics=batch.metrics)
+            raise RuntimeError(
+                "Mesh generation failed: surface extraction returned None. "
+                "The surface level may be outside the volume data range."
+            )
+
+        obj_path, return_path = self._get_output_paths(batch)
+        output_dir = os.path.dirname(obj_path)
+        if output_dir:
+            os.makedirs(output_dir, exist_ok=True)
+        mesh.export(obj_path)
+
+        batch.extra["shape_obj_path"] = obj_path
+        batch.extra["shape_return_path"] = return_path
+
+        if self.config.paint_enable:
+            return batch
+
+        if return_path.endswith(".glb"):
+            return_path = obj_path
+        return OutputBatch(output_file_paths=[return_path], timings=batch.timings)
+
+    def verify_input(self, batch: Req, server_args: ServerArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("shape_meshes", batch.extra.get("shape_meshes"), V.not_none)
+        return result
+
+
+__all__ = [
+    "retrieve_timesteps",
+    "Hunyuan3DShapeBeforeDenoisingStage",
+    "Hunyuan3DShapeDenoisingStage",
+    "Hunyuan3DShapeExportStage",
+    "Hunyuan3DShapeSaveStage",
+]

python/sglang/multimodal_gen/runtime/pipelines_core/stages/input_validation.py

@@ -241,8 +241,21 @@ class InputValidationStage(PipelineStage):
 
         self._generate_seeds(batch, server_args)
 
-        # Ensure prompt is properly formatted
-        if batch.prompt is None and batch.prompt_embeds is None:
+        if (
+            server_args.pipeline_config.task_type == ModelTaskType.I2M
+            and batch.num_inference_steps is None
+            and hasattr(server_args.pipeline_config, "shape_num_inference_steps")
+        ):
+            batch.num_inference_steps = (
+                server_args.pipeline_config.shape_num_inference_steps
+            )
+
+        # Ensure prompt is properly formatted (I2M can be image-only)
+        if (
+            server_args.pipeline_config.task_type != ModelTaskType.I2M
+            and batch.prompt is None
+            and batch.prompt_embeds is None
+        ):
             raise ValueError("Either `prompt` or `prompt_embeds` must be provided")
 
         # Ensure negative prompt is properly formatted if using classifier-free guidance
@@ -299,9 +312,10 @@ class InputValidationStage(PipelineStage):
                 )
                 batch.original_condition_image_size = image.size
 
-            self.preprocess_condition_image(
-                batch, server_args, condition_image_width, condition_image_height
-            )
+            if server_args.pipeline_config.task_type != ModelTaskType.I2M:
+                self.preprocess_condition_image(
+                    batch, server_args, condition_image_width, condition_image_height
+                )
 
         # if height or width is not specified at this point, set default to 720p
         default_height = 720
@@ -323,16 +337,24 @@ class InputValidationStage(PipelineStage):
         result.add_check(
             "num_videos_per_prompt", batch.num_outputs_per_prompt, V.positive_int
         )
-        result.add_check(
-            "prompt_or_embeds",
-            None,
-            lambda _: V.string_or_list_strings(batch.prompt)
-            or V.list_not_empty(batch.prompt_embeds),
-        )
+        if server_args.pipeline_config.task_type != ModelTaskType.I2M:
+            result.add_check(
+                "prompt_or_embeds",
+                None,
+                lambda _: V.string_or_list_strings(batch.prompt)
+                or V.list_not_empty(batch.prompt_embeds),
+            )
 
-        result.add_check(
-            "num_inference_steps", batch.num_inference_steps, V.positive_int
-        )
+        if server_args.pipeline_config.task_type != ModelTaskType.I2M:
+            result.add_check(
+                "num_inference_steps", batch.num_inference_steps, V.positive_int
+            )
+        else:
+            result.add_check(
+                "num_inference_steps",
+                batch.num_inference_steps,
+                lambda x: x is None or V.positive_int(x),
+            )
         result.add_check(
             "guidance_scale",
             batch.guidance_scale,

python/sglang/multimodal_gen/test/server/perf_baselines.json

@@ -1920,6 +1920,72 @@
             "expected_avg_denoise_ms": 173.83,
             "expected_median_denoise_ms": 178.08
         },
+        "hunyuan3d_shape_gen": {
+            "stages_ms": {
+                "Hunyuan3DShapeBeforeDenoisingStage": 31.42,
+                "Hunyuan3DShapeDenoisingStage": 3259.83,
+                "Hunyuan3DShapeExportStage": 8735.55,
+                "Hunyuan3DShapeSaveStage": 981.64,
+                "Hunyuan3DPaintPreprocessStage": 226071.67,
+                "Hunyuan3DPaintTexGenStage": 11083.05,
+                "Hunyuan3DPaintPostprocessStage": 7469.29
+            },
+            "denoise_step_ms": {
+                "0": 32.26,
+                "1": 63.34,
+                "2": 65.44,
+                "3": 65.44,
+                "4": 65.6,
+                "5": 65.81,
+                "6": 65.82,
+                "7": 65.48,
+                "8": 65.9,
+                "9": 65.77,
+                "10": 65.54,
+                "11": 65.68,
+                "12": 65.85,
+                "13": 65.77,
+                "14": 65.7,
+                "15": 65.78,
+                "16": 66.0,
+                "17": 66.15,
+                "18": 65.91,
+                "19": 66.5,
+                "20": 65.76,
+                "21": 66.08,
+                "22": 66.06,
+                "23": 66.23,
+                "24": 65.79,
+                "25": 65.58,
+                "26": 65.88,
+                "27": 65.67,
+                "28": 65.87,
+                "29": 66.09,
+                "30": 65.81,
+                "31": 65.91,
+                "32": 66.18,
+                "33": 65.93,
+                "34": 66.26,
+                "35": 66.26,
+                "36": 66.27,
+                "37": 65.57,
+                "38": 66.02,
+                "39": 66.19,
+                "40": 65.23,
+                "41": 66.11,
+                "42": 66.18,
+                "43": 65.86,
+                "44": 65.86,
+                "45": 65.92,
+                "46": 65.65,
+                "47": 65.78,
+                "48": 66.01,
+                "49": 66.08
+            },
+            "expected_e2e_ms": 257696.97,
+            "expected_avg_denoise_ms": 65.16,
+            "expected_median_denoise_ms": 65.86
+        },
         "wan2_1_t2v_1.3b_frame_interp_2x": {
             "stages_ms": {
                 "InputValidationStage": 0.03,

python/sglang/multimodal_gen/test/server/test_server_common.py

@@ -734,6 +734,7 @@ Consider updating perf_baselines.json with the snippets below:
         modality_to_valid_task_types = {
             "image": {"T2I", "I2I", "TI2I"},
             "video": {"T2V", "I2V", "TI2V"},
+            "3d": {"I2M"},
         }
         valid_task_types = modality_to_valid_task_types.get(
             case.server_args.modality, set()
@@ -858,6 +859,17 @@ Consider updating perf_baselines.json with the snippets below:
         # Validation 1: Performance
         self._validate_and_record(case, perf_record)
 
+        # Mesh correctness check (Chamfer Distance) for 3D models
+        if case.server_args.custom_validator == "mesh":
+            from sglang.multimodal_gen.test.server.test_server_utils import (
+                MESH_OUTPUT_PATHS,
+                validate_mesh_correctness,
+            )
+
+            mesh_path = MESH_OUTPUT_PATHS.pop(case.id, None)
+            if mesh_path:
+                validate_mesh_correctness(mesh_path)
+
         # Test /v1/models endpoint for router compatibility
         self._test_v1_models_endpoint(diffusion_server, case)
         self._test_t2v_rejects_input_reference(diffusion_server, case)

python/sglang/multimodal_gen/test/server/test_server_utils.py

@@ -50,6 +50,10 @@ logger = init_logger(__name__)
 
 globally_suppress_loggers()
 
+# Tracks mesh output file paths from generate_mesh for later correctness validation.
+# Keyed by case_id, cleaned up after use.
+MESH_OUTPUT_PATHS: dict[str, str] = {}
+
 
 def download_image_from_url(url: str) -> Path:
     """Download an image from a URL to a temporary file.
@@ -637,10 +641,99 @@ class VideoPerformanceValidator(PerformanceValidator):
             )
 
 
+class MeshValidator(PerformanceValidator):
+    """Validator for 3D mesh generation. Inherits perf validation from PerformanceValidator."""
+
+    pass
+
+
+HUNYUAN3D_REFERENCE_URL = (
+    "https://raw.githubusercontent.com/sgl-project/sgl-test-files/"
+    "main/diffusion-ci/consistency_gt/1-gpu/hunyuan3d_2_0/hunyuan3d.glb"
+)
+
+
+def _download_reference_mesh(url: str) -> Path:
+    """Download a reference mesh from URL, caching in temp dir."""
+    import hashlib
+
+    cache_name = f"ref_mesh_{hashlib.md5(url.encode()).hexdigest()}.glb"
+    cache_path = Path(tempfile.gettempdir()) / cache_name
+    if cache_path.exists():
+        logger.info(f"Using cached reference mesh: {cache_path}")
+        return cache_path
+
+    logger.info(f"Downloading reference mesh from: {url}")
+    with urlopen(url, timeout=60) as resp:
+        cache_path.write_bytes(resp.read())
+    logger.info(f"Reference mesh cached at: {cache_path}")
+    return cache_path
+
+
+def validate_mesh_correctness(
+    generated_mesh_path: str,
+    reference_url: str = HUNYUAN3D_REFERENCE_URL,
+    num_sample_points: int = 4096,
+    cd_threshold_ratio: float = 0.01,
+    random_seed: int = 42,
+):
+    """Validate mesh geometric similarity against a reference via Chamfer Distance.
+
+    Downloads the reference mesh from a URL (cached), samples point clouds from
+    both meshes, and asserts Chamfer Distance is within threshold.
+    """
+    import numpy as np
+
+    try:
+        import trimesh
+    except ImportError:
+        pytest.fail("trimesh is required for mesh validation: pip install trimesh")
+
+    from scipy.spatial import cKDTree
+
+    # Load generated mesh
+    generated_mesh = trimesh.load(generated_mesh_path)
+    if isinstance(generated_mesh, trimesh.Scene):
+        generated_mesh = generated_mesh.dump(concatenate=True)
+
+    # Download and load reference mesh
+    ref_path = _download_reference_mesh(reference_url)
+    reference_mesh = trimesh.load(str(ref_path))
+    if isinstance(reference_mesh, trimesh.Scene):
+        reference_mesh = reference_mesh.dump(concatenate=True)
+
+    # Bounding box diagonal for threshold normalization
+    ref_bbox = reference_mesh.bounding_box.bounds
+    bbox_diagonal = float(np.linalg.norm(ref_bbox[1] - ref_bbox[0]))
+    cd_threshold = cd_threshold_ratio * bbox_diagonal
+
+    # Sample point clouds
+    np.random.seed(random_seed)
+    gen_points = np.array(
+        generated_mesh.sample(num_sample_points, return_index=True)[0]
+    )
+    ref_points = np.array(
+        reference_mesh.sample(num_sample_points, return_index=True)[0]
+    )
+
+    # Bidirectional Chamfer Distance
+    tree1 = cKDTree(gen_points)
+    tree2 = cKDTree(ref_points)
+    forward_cd = float(np.mean(tree2.query(gen_points)[0] ** 2))
+    backward_cd = float(np.mean(tree1.query(ref_points)[0] ** 2))
+    total_cd = forward_cd + backward_cd
+
+    assert total_cd <= cd_threshold, (
+        f"Chamfer Distance check failed: total_cd={total_cd:.6f}, "
+        f"threshold={cd_threshold:.6f} ({cd_threshold_ratio * 100:.2f}% of bbox diagonal {bbox_diagonal:.4f})"
+    )
+
+
 # Registry of validators by name
 VALIDATOR_REGISTRY = {
     "default": PerformanceValidator,
     "video": VideoPerformanceValidator,
+    "mesh": MeshValidator,
 }
 
 
@@ -1095,7 +1188,103 @@ def get_generate_fn(
                 },
             )
 
-    if modality == "video":
+    def generate_mesh(case_id, client) -> tuple[str, bytes]:
+        """I2M: Image to Mesh generation using async /v1/meshes API."""
+        import requests as http_requests
+
+        if not sampling_params.image_path:
+            pytest.skip(f"{case_id}: no input image configured for mesh generation")
+
+        image_path = sampling_params.image_path
+        if isinstance(image_path, str) and is_image_url(image_path):
+            image_path = download_image_from_url(image_path)
+        elif isinstance(image_path, Path):
+            if not image_path.exists():
+                pytest.skip(f"{case_id}: image file missing: {image_path}")
+        else:
+            image_path = Path(str(image_path))
+            if not image_path.exists():
+                pytest.skip(f"{case_id}: image file missing: {image_path}")
+
+        base_url = str(client.base_url).rstrip("/")
+        if base_url.endswith("/v1"):
+            base_url = base_url[:-3]
+
+        create_url = f"{base_url}/v1/meshes"
+
+        with open(str(image_path), "rb") as img_file:
+            files = {"image": (Path(str(image_path)).name, img_file, "image/png")}
+            data = {
+                "prompt": "generate 3d mesh",
+                "model": model_path,
+                "seed": "0",
+                "guidance_scale": "5.0",
+                "num_inference_steps": "50",
+            }
+
+            logger.info(f"[Mesh Gen] Sending request to {create_url}")
+
+            try:
+                response = http_requests.post(
+                    create_url, files=files, data=data, timeout=60
+                )
+            except Exception as e:
+                pytest.fail(f"{case_id}: mesh creation request failed: {e}")
+
+        if response.status_code != 200:
+            pytest.fail(f"{case_id}: mesh creation failed: {response.text}")
+
+        job = response.json()
+        mesh_id = job.get("id")
+        if not mesh_id:
+            pytest.fail(f"{case_id}: no mesh id in response: {job}")
+
+        poll_url = f"{base_url}/v1/meshes/{mesh_id}"
+        poll_interval = 5
+        max_wait = 1200
+        elapsed = 0
+
+        while elapsed < max_wait:
+            time.sleep(poll_interval)
+            elapsed += poll_interval
+
+            try:
+                poll_resp = http_requests.get(poll_url, timeout=30)
+            except Exception as e:
+                logger.warning(f"[Mesh Gen] Poll failed: {e}")
+                continue
+
+            if poll_resp.status_code != 200:
+                continue
+
+            status_data = poll_resp.json()
+            status = status_data.get("status", "")
+
+            if status == "completed":
+                content_url = f"{base_url}/v1/meshes/{mesh_id}/content"
+                try:
+                    content_resp = http_requests.get(content_url, timeout=60)
+                except Exception as e:
+                    pytest.fail(f"{case_id}: mesh download failed: {e}")
+
+                if content_resp.status_code != 200:
+                    pytest.fail(f"{case_id}: mesh download failed: {content_resp.text}")
+
+                temp_path = Path(tempfile.gettempdir()) / f"mesh_test_{mesh_id}.glb"
+                temp_path.write_bytes(content_resp.content)
+                MESH_OUTPUT_PATHS[case_id] = str(temp_path)
+
+                logger.info(f"[Mesh Gen] Mesh downloaded to {temp_path}")
+                return (mesh_id, b"")
+            elif status == "failed":
+                error = status_data.get("error", {})
+                pytest.fail(f"{case_id}: mesh generation failed: {error}")
+
+        pytest.fail(f"{case_id}: mesh generation timed out after {max_wait}s")
+
+    if modality == "3d":
+        fn = generate_mesh
+    elif modality == "video":
         if sampling_params.image_path and sampling_params.prompt:
             if getattr(sampling_params, "direct_url_test", False):
                 fn = generate_text_url_image_to_video

python/sglang/multimodal_gen/test/server/testcase_configs.py

@@ -190,6 +190,8 @@ class DiffusionServerArgs:
             self.custom_validator = "image"
         elif self.modality == "video":
             self.custom_validator = "video"
+        elif self.modality == "3d":
+            self.custom_validator = "mesh"
 
 
 @dataclass(frozen=True)
@@ -460,6 +462,11 @@ ONE_GPU_CASES_A: list[DiffusionTestCase] = [
     ),
 ]
 
+HUNYUAN3D_SHAPE_sampling_params = DiffusionSamplingParams(
+    prompt="",
+    image_path="https://raw.githubusercontent.com/sgl-project/sgl-test-files/main/diffusion-ci/consistency_gt/1-gpu/hunyuan3d_2_0/hunyuan3d.png",
+)
+
 ONE_GPU_CASES_B: list[DiffusionTestCase] = [
     # === Text to Video (T2V) ===
     DiffusionTestCase(
@@ -591,7 +598,19 @@ ONE_GPU_CASES_B: list[DiffusionTestCase] = [
     ),
 ]
 
-# Skip turbowan because Triton requires 81920 shared memory, but AMD only has 65536.
+# Skip hunyuan3d on AMD: marching_cubes surface extraction produces invalid SDF on ROCm.
+if not current_platform.is_hip():
+    ONE_GPU_CASES_B.append(
+        DiffusionTestCase(
+            "hunyuan3d_shape_gen",
+            DiffusionServerArgs(
+                model_path="tencent/Hunyuan3D-2",
+                modality="3d",
+            ),
+            HUNYUAN3D_SHAPE_sampling_params,
+        ),
+    )
+# Skip turbowan on AMD: Triton requires 81920 shared memory, but AMD only has 65536.
 if not current_platform.is_hip():
     ONE_GPU_CASES_B.append(
         DiffusionTestCase(