import os os.environ.setdefault("SPCONV_ALGO", "native") os.environ.setdefault("ATTN_BACKEND", "xformers") os.environ.setdefault("SPARSE_ATTN_BACKEND", "xformers") import subprocess import sys # Install gradio_litmodel3d ignoring its over-restrictive gradio<5 cap. try: import gradio_litmodel3d # noqa: F401 except ImportError: subprocess.check_call( [sys.executable, "-m", "pip", "install", "--no-deps", "gradio_litmodel3d==0.0.1"], ) import spaces import torch import ctypes import tempfile CUDA_HOME = "/cuda-image/usr/local/cuda-13.0" CUDA_LIBDIR = os.path.join(CUDA_HOME, "lib64") @spaces.GPU(duration=600) def _first_gpu_setup(): need = {} for name, modname in [ ("nvdiffrast", "nvdiffrast"), ("diff_gaussian_rasterization", "diff_gaussian_rasterization"), ]: try: __import__(modname) except ImportError: need[name] = True if not need: return patch_dir = tempfile.mkdtemp(prefix="torch_cuda_patch_") with open(os.path.join(patch_dir, "sitecustomize.py"), "w") as f: f.write( "try:\n" " import torch.utils.cpp_extension as _c\n" " _c._check_cuda_version = lambda *a, **k: None\n" "except Exception:\n" " pass\n" ) env = os.environ.copy() env["CUDA_HOME"] = CUDA_HOME env["CUDA_PATH"] = CUDA_HOME env["PATH"] = os.path.join(CUDA_HOME, "bin") + os.pathsep + env.get("PATH", "") env["PYTHONPATH"] = patch_dir + os.pathsep + env.get("PYTHONPATH", "") env["TORCH_CUDA_ARCH_LIST"] = "12.0" subprocess.check_call( [sys.executable, "-m", "pip", "install", "--no-deps", "setuptools", "wheel", "ninja", "packaging"], ) if "nvdiffrast" in need: subprocess.check_call( [sys.executable, "-m", "pip", "install", "--no-build-isolation", "--no-deps", "git+https://github.com/NVlabs/nvdiffrast/"], env=env, ) if "diff_gaussian_rasterization" in need: mip = tempfile.mkdtemp(prefix="mip_") subprocess.check_call( ["git", "clone", "--recursive", "--depth=1", "https://github.com/autonomousvision/mip-splatting.git", mip], ) subprocess.check_call( [sys.executable, "-m", "pip", "install", "--no-build-isolation", "--no-deps", os.path.join(mip, "submodules", "diff-gaussian-rasterization")], env=env, ) _first_gpu_setup() try: ctypes.CDLL(os.path.join(CUDA_LIBDIR, "libcudart.so.13"), mode=ctypes.RTLD_GLOBAL) os.environ["LD_LIBRARY_PATH"] = CUDA_LIBDIR + os.pathsep + os.environ.get("LD_LIBRARY_PATH", "") except OSError: pass # xformers on the Blackwell (sm_120) ZeroGPU container is built without CUDA # extensions for any FwOp: cutlassF-pt rejects compute capability >= (9, 0) # ("too new") and FlashAttn3 is Hopper-only. Reroute xformers.ops.memory_efficient_attention # (used by DINOv2, VGGT, trellis dense+sparse paths) to torch.nn.functional.scaled_dot_product_attention, # which is CUDA-native on torch 2.10/2.11 and supports sm_120. Must be patched BEFORE # anything that calls memory_efficient_attention is imported. try: import xformers.ops as _xops import torch.nn.functional as _F from xformers.ops.fmha.attn_bias import BlockDiagonalMask as _BlockDiagonalMask def _bdm_starts(seqinfo): # xformers' BlockDiagonalMask sub-attribute. Try the public python-list view first; # otherwise pull from the tensor and tolist(). for attr in ("seqstart_py", "_seqstart_py"): v = getattr(seqinfo, attr, None) if v is not None: return list(v) t = getattr(seqinfo, "seqstart", None) if t is not None: return t.detach().cpu().tolist() raise AttributeError("BlockDiagonalMask seqinfo has no seqstart_py / seqstart") def _mea_sdpa(q, k, v, attn_bias=None, p=0.0, scale=None, op=None): # q, k, v shapes: [B, M, H, K] (xformers convention). SDPA wants [B, H, M, K]. if isinstance(attn_bias, _BlockDiagonalMask): # Block-diagonal mask used by trellis sparse attention to batch # variable-length sequences in a single dense tensor. Materialize each # block separately and concat — SDPA has no block-diagonal kernel. q_starts = _bdm_starts(attn_bias.q_seqinfo) k_starts = _bdm_starts(attn_bias.k_seqinfo) outs = [] # q,k,v come in as [1, total_tokens, H, K] for i in range(len(q_starts) - 1): qs, qe = q_starts[i], q_starts[i + 1] ks, ke = k_starts[i], k_starts[i + 1] qi = q[:, qs:qe].transpose(1, 2) # [1, H, Lq, K] ki = k[:, ks:ke].transpose(1, 2) # [1, H, Lk, K] vi = v[:, ks:ke].transpose(1, 2) oi = _F.scaled_dot_product_attention(qi, ki, vi, dropout_p=p, scale=scale) outs.append(oi.transpose(1, 2)) # back to [1, Li, H, K] return torch.cat(outs, dim=1) attn_mask = None if attn_bias is not None and hasattr(attn_bias, "materialize"): attn_mask = attn_bias.materialize((q.shape[0], q.shape[2], q.shape[1], k.shape[1]), dtype=q.dtype, device=q.device) elif attn_bias is not None: attn_mask = attn_bias qh = q.transpose(1, 2) # [B, H, M, K] kh = k.transpose(1, 2) vh = v.transpose(1, 2) out = _F.scaled_dot_product_attention(qh, kh, vh, attn_mask=attn_mask, dropout_p=p, scale=scale) return out.transpose(1, 2) # [B, M, H, K] _xops.memory_efficient_attention = _mea_sdpa print("[blackwell] xformers.memory_efficient_attention rerouted to torch SDPA") except Exception as _e: print(f"[blackwell] xformers SDPA shim skipped: {_e}") import gradio as gr from gradio_litmodel3d import LitModel3D import shutil from typing import * import numpy as np import imageio from easydict import EasyDict as edict from PIL import Image from trellis.pipelines import TrellisVGGTTo3DPipeline from trellis.representations import Gaussian, MeshExtractResult from trellis.utils import render_utils, postprocessing_utils MAX_SEED = np.iinfo(np.int32).max TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp') # TMP_DIR = "tmp/Trellis-demo" # os.environ['GRADIO_TEMP_DIR'] = 'tmp' os.makedirs(TMP_DIR, exist_ok=True) def start_session(req: gr.Request): user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) def end_session(req: gr.Request): user_dir = os.path.join(TMP_DIR, str(req.session_hash)) shutil.rmtree(user_dir) @spaces.GPU def preprocess_image(image: Image.Image) -> Image.Image: """ Preprocess the input image for 3D generation. This function is called when a user uploads an image or selects an example. It applies background removal and other preprocessing steps necessary for optimal 3D model generation. Args: image (Image.Image): The input image from the user Returns: Image.Image: The preprocessed image ready for 3D generation """ processed_image = pipeline.preprocess_image(image) return processed_image @spaces.GPU def preprocess_videos(video: str) -> List[Tuple[Image.Image, str]]: """ Preprocess the input video for multi-image 3D generation. This function is called when a user uploads a video. It extracts frames from the video and processes each frame to prepare them for the multi-image 3D generation pipeline. Args: video (str): The path to the input video file Returns: List[Tuple[Image.Image, str]]: The list of preprocessed images ready for 3D generation """ vid = imageio.get_reader(video, 'ffmpeg') fps = vid.get_meta_data()['fps'] images = [] for i, frame in enumerate(vid): if i % max(int(fps * 1), 1) == 0: img = Image.fromarray(frame) W, H = img.size img = img.resize((int(W / H * 512), 512)) images.append(img) vid.close() processed_images = [pipeline.preprocess_image(image) for image in images] return processed_images @spaces.GPU def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]: """ Preprocess a list of input images for multi-image 3D generation. This function is called when users upload multiple images in the gallery. It processes each image to prepare them for the multi-image 3D generation pipeline. Args: images (List[Tuple[Image.Image, str]]): The input images from the gallery Returns: List[Image.Image]: The preprocessed images ready for 3D generation """ images = [image[0] for image in images] processed_images = [pipeline.preprocess_image(image) for image in images] return processed_images def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict: return { 'gaussian': { **gs.init_params, '_xyz': gs._xyz.cpu().numpy(), '_features_dc': gs._features_dc.cpu().numpy(), '_scaling': gs._scaling.cpu().numpy(), '_rotation': gs._rotation.cpu().numpy(), '_opacity': gs._opacity.cpu().numpy(), }, 'mesh': { 'vertices': mesh.vertices.cpu().numpy(), 'faces': mesh.faces.cpu().numpy(), }, } def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]: gs = Gaussian( aabb=state['gaussian']['aabb'], sh_degree=state['gaussian']['sh_degree'], mininum_kernel_size=state['gaussian']['mininum_kernel_size'], scaling_bias=state['gaussian']['scaling_bias'], opacity_bias=state['gaussian']['opacity_bias'], scaling_activation=state['gaussian']['scaling_activation'], ) gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda') gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda') gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda') gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda') gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda') mesh = edict( vertices=torch.tensor(state['mesh']['vertices'], device='cuda'), faces=torch.tensor(state['mesh']['faces'], device='cuda'), ) return gs, mesh def get_seed(randomize_seed: bool, seed: int) -> int: """ Get the random seed for generation. This function is called by the generate button to determine whether to use a random seed or the user-specified seed value. Args: randomize_seed (bool): Whether to generate a random seed seed (int): The user-specified seed value Returns: int: The seed to use for generation """ return np.random.randint(0, MAX_SEED) if randomize_seed else seed @spaces.GPU(duration=120) def generate_and_extract_glb( multiimages: List[Tuple[Image.Image, str]], seed: int, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int, multiimage_algo: Literal["multidiffusion", "stochastic"], mesh_simplify: float, texture_size: int, req: gr.Request, ) -> Tuple[dict, str, str, str]: """ Convert an image to a 3D model and extract GLB file. Args: image (Image.Image): The input image. multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode. is_multiimage (bool): Whether is in multi-image mode. seed (int): The random seed. ss_guidance_strength (float): The guidance strength for sparse structure generation. ss_sampling_steps (int): The number of sampling steps for sparse structure generation. slat_guidance_strength (float): The guidance strength for structured latent generation. slat_sampling_steps (int): The number of sampling steps for structured latent generation. multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation. mesh_simplify (float): The mesh simplification factor. texture_size (int): The texture resolution. Returns: dict: The information of the generated 3D model. str: The path to the video of the 3D model. str: The path to the extracted GLB file. str: The path to the extracted GLB file (for download). """ user_dir = os.path.join(TMP_DIR, str(req.session_hash)) image_files = [image[0] for image in multiimages] # Generate 3D model outputs, _, _ = pipeline.run( image=image_files, seed=seed, formats=["gaussian", "mesh"], preprocess_image=False, sparse_structure_sampler_params={ "steps": ss_sampling_steps, "cfg_strength": ss_guidance_strength, }, slat_sampler_params={ "steps": slat_sampling_steps, "cfg_strength": slat_guidance_strength, }, mode=multiimage_algo, ) # Render video # import uuid # output_id = str(uuid.uuid4()) # os.makedirs(f"{TMP_DIR}/{output_id}", exist_ok=True) # video_path = f"{TMP_DIR}/{output_id}/preview.mp4" # glb_path = f"{TMP_DIR}/{output_id}/mesh.glb" video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color'] video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal'] video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))] video_path = os.path.join(user_dir, 'sample.mp4') imageio.mimsave(video_path, video, fps=15) # Extract GLB gs = outputs['gaussian'][0] mesh = outputs['mesh'][0] glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False) glb_path = os.path.join(user_dir, 'sample.glb') glb.export(glb_path) # Pack state for optional Gaussian extraction state = pack_state(gs, mesh) torch.cuda.empty_cache() return state, video_path, glb_path, glb_path @spaces.GPU def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]: """ Extract a Gaussian splatting file from the generated 3D model. This function is called when the user clicks "Extract Gaussian" button. It converts the 3D model state into a .ply file format containing Gaussian splatting data for advanced 3D applications. Args: state (dict): The state of the generated 3D model containing Gaussian data req (gr.Request): Gradio request object for session management Returns: Tuple[str, str]: Paths to the extracted Gaussian file (for display and download) """ user_dir = os.path.join(TMP_DIR, str(req.session_hash)) gs, _ = unpack_state(state) gaussian_path = os.path.join(user_dir, 'sample.ply') gs.save_ply(gaussian_path) torch.cuda.empty_cache() return gaussian_path, gaussian_path def prepare_multi_example() -> List[Image.Image]: multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")])) images = [] for case in multi_case: _images = [] for i in range(1, 9): if os.path.exists(f'assets/example_multi_image/{case}_{i}.png'): img = Image.open(f'assets/example_multi_image/{case}_{i}.png') W, H = img.size img = img.resize((int(W / H * 512), 512)) _images.append(np.array(img)) if len(_images) > 0: images.append(Image.fromarray(np.concatenate(_images, axis=1))) return images def split_image(image: Image.Image) -> List[Image.Image]: """ Split a multi-view image into separate view images. This function is called when users select multi-image examples that contain multiple views in a single concatenated image. It automatically splits them based on alpha channel boundaries and preprocesses each view. Args: image (Image.Image): A concatenated image containing multiple views Returns: List[Image.Image]: List of individual preprocessed view images """ image = np.array(image) alpha = image[..., 3] alpha = np.any(alpha>0, axis=0) start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist() end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist() images = [] for s, e in zip(start_pos, end_pos): images.append(Image.fromarray(image[:, s:e+1])) return [preprocess_image(image) for image in images] # Create interface demo = gr.Blocks( title="ReconViaGen", css=""" .slider .inner { width: 5px; background: #FFF; } .viewport { aspect-ratio: 4/3; } .tabs button.selected { font-size: 20px !important; color: crimson !important; } h1, h2, h3 { text-align: center; display: block; } .md_feedback li { margin-bottom: 0px !important; } """ ) with demo: gr.Markdown(""" # 💻 ReconViaGen

badge-github-stars

✨This demo is partial. We will release the whole model later. Stay tuned!✨ """) with gr.Row(): with gr.Column(): with gr.Tabs() as input_tabs: with gr.Tab(label="Input Video or Images", id=0) as multiimage_input_tab: input_video = gr.Video(label="Upload Video", interactive=True, height=300) image_prompt = gr.Image(label="Image Prompt", format="png", visible=False, image_mode="RGBA", type="pil", height=300) multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3) gr.Markdown(""" Input different views of the object in separate images. """) with gr.Accordion(label="Generation Settings", open=False): seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1) randomize_seed = gr.Checkbox(label="Randomize Seed", value=False) gr.Markdown("Stage 1: Sparse Structure Generation") with gr.Row(): ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1) ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=30, step=1) gr.Markdown("Stage 2: Structured Latent Generation") with gr.Row(): slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1) slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1) multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="multidiffusion") with gr.Accordion(label="GLB Extraction Settings", open=False): mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01) texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512) generate_btn = gr.Button("Generate & Extract GLB", variant="primary") extract_gs_btn = gr.Button("Extract Gaussian", interactive=False) gr.Markdown(""" *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.* """) with gr.Column(): video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300) model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300) with gr.Row(): download_glb = gr.DownloadButton(label="Download GLB", interactive=False) download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False) output_buf = gr.State() # Example images at the bottom of the page with gr.Row() as multiimage_example: examples_multi = gr.Examples( examples=prepare_multi_example(), inputs=[image_prompt], fn=split_image, outputs=[multiimage_prompt], run_on_click=True, examples_per_page=8, ) # Handlers demo.load(start_session) demo.unload(end_session) input_video.upload( preprocess_videos, inputs=[input_video], outputs=[multiimage_prompt], ) input_video.clear( lambda: tuple([None, None]), outputs=[input_video, multiimage_prompt], ) multiimage_prompt.upload( preprocess_images, inputs=[multiimage_prompt], outputs=[multiimage_prompt], ) generate_btn.click( get_seed, inputs=[randomize_seed, seed], outputs=[seed], ).then( generate_and_extract_glb, inputs=[multiimage_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo, mesh_simplify, texture_size], outputs=[output_buf, video_output, model_output, download_glb], ).then( lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]), outputs=[extract_gs_btn, download_glb], ) video_output.clear( lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False), gr.Button(interactive=False)]), outputs=[extract_gs_btn, download_glb, download_gs], ) extract_gs_btn.click( extract_gaussian, inputs=[output_buf], outputs=[model_output, download_gs], ).then( lambda: gr.Button(interactive=True), outputs=[download_gs], ) model_output.clear( lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]), outputs=[download_glb, download_gs], ) # Launch the Gradio app if __name__ == "__main__": pipeline = TrellisVGGTTo3DPipeline.from_pretrained("Stable-X/trellis-vggt-v0-2") pipeline.cuda() pipeline.VGGT_model.cuda() pipeline.birefnet_model.cuda() demo.launch()