from __future__ import annotations import os import shutil import threading from queue import SimpleQueue from typing import Any import gradio as gr import numpy as np import rembg import rerun as rr import rerun.blueprint as rrb import spaces import torch from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from einops import rearrange from gradio_rerun import Rerun from huggingface_hub import hf_hub_download from omegaconf import OmegaConf from PIL import Image from pytorch_lightning import seed_everything from torchvision.transforms import v2 from src.models.lrm_mesh import InstantMesh from src.utils.camera_util import ( FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras, ) from src.utils.infer_util import remove_background, resize_foreground from src.utils.train_util import instantiate_from_config def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False): """Get the rendering camera parameters.""" c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation) if is_flexicubes: cameras = torch.linalg.inv(c2ws) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1) else: extrinsics = c2ws.flatten(-2) intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2) cameras = torch.cat([extrinsics, intrinsics], dim=-1) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1) return cameras ############################################################################### # Configuration. ############################################################################### def find_cuda(): # Check if CUDA_HOME or CUDA_PATH environment variables are set cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH") if cuda_home and os.path.exists(cuda_home): return cuda_home # Search for the nvcc executable in the system's PATH nvcc_path = shutil.which("nvcc") if nvcc_path: # Remove the 'bin/nvcc' part to get the CUDA installation path cuda_path = os.path.dirname(os.path.dirname(nvcc_path)) return cuda_path return None cuda_path = find_cuda() if cuda_path: print(f"CUDA installation found at: {cuda_path}") else: print("CUDA installation not found") config_path = "configs/instant-mesh-large.yaml" config = OmegaConf.load(config_path) config_name = os.path.basename(config_path).replace(".yaml", "") model_config = config.model_config infer_config = config.infer_config IS_FLEXICUBES = True if config_name.startswith("instant-mesh") else False device = torch.device("cuda") # load diffusion model print("Loading diffusion model ...") pipeline = DiffusionPipeline.from_pretrained( "sudo-ai/zero123plus-v1.2", custom_pipeline="zero123plus", torch_dtype=torch.float16, ) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing") # load custom white-background UNet unet_ckpt_path = hf_hub_download( repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model" ) state_dict = torch.load(unet_ckpt_path, map_location="cpu") pipeline.unet.load_state_dict(state_dict, strict=True) pipeline = pipeline.to(device) print(f"type(pipeline)={type(pipeline)}") # load reconstruction model print("Loading reconstruction model ...") model_ckpt_path = hf_hub_download( repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model" ) model: InstantMesh = instantiate_from_config(model_config) state_dict = torch.load(model_ckpt_path, map_location="cpu")["state_dict"] state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith("lrm_generator.") and "source_camera" not in k} model.load_state_dict(state_dict, strict=True) model = model.to(device) print("Loading Finished!") def check_input_image(input_image): if input_image is None: raise gr.Error("No image uploaded!") def preprocess(input_image, do_remove_background): rembg_session = rembg.new_session() if do_remove_background else None if do_remove_background: input_image = remove_background(input_image, rembg_session) input_image = resize_foreground(input_image, 0.85) return input_image def pipeline_callback( log_queue: SimpleQueue, pipe: Any, step_index: int, timestep: float, callback_kwargs: dict[str, Any] ) -> dict[str, Any]: latents = callback_kwargs["latents"] image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] # type: ignore[attr-defined] image = pipe.image_processor.postprocess(image, output_type="np").squeeze() # type: ignore[attr-defined] log_queue.put(("mvs", rr.Image(image))) log_queue.put(("latents", rr.Tensor(latents.squeeze()))) return callback_kwargs def generate_mvs(log_queue, input_image, sample_steps, sample_seed): seed_everything(sample_seed) return pipeline( input_image, num_inference_steps=sample_steps, callback_on_step_end=lambda *args, **kwargs: pipeline_callback(log_queue, *args, **kwargs), ).images[0] def make3d(log_queue, images: Image.Image): global model if IS_FLEXICUBES: model.init_flexicubes_geometry(device, use_renderer=False) model = model.eval() images = np.asarray(images, dtype=np.float32) / 255.0 images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640) images = rearrange(images, "c (n h) (m w) -> (n m) c h w", n=3, m=2) # (6, 3, 320, 320) input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device) images = images.unsqueeze(0).to(device) images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1) with torch.no_grad(): # get triplane planes = model.forward_planes(images, input_cameras) # get mesh mesh_out = model.extract_mesh( planes, use_texture_map=False, **infer_config, ) vertices, faces, vertex_colors = mesh_out log_queue.put(( "mesh", rr.Mesh3D(vertex_positions=vertices, vertex_colors=vertex_colors, triangle_indices=faces), )) return mesh_out def generate_blueprint() -> rrb.Blueprint: return rrb.Blueprint( rrb.Horizontal( rrb.Spatial3DView(origin="mesh"), rrb.Grid( rrb.Spatial2DView(origin="z123image"), rrb.Spatial2DView(origin="preprocessed_image"), rrb.Spatial2DView(origin="mvs"), rrb.TensorView( origin="latents", ), ), column_shares=[1, 1], ), collapse_panels=True, ) def compute(log_queue, input_image, do_remove_background, sample_steps, sample_seed): preprocessed_image = preprocess(input_image, do_remove_background) log_queue.put(("preprocessed_image", rr.Image(preprocessed_image))) z123_image = generate_mvs(log_queue, preprocessed_image, sample_steps, sample_seed) log_queue.put(("z123image", rr.Image(z123_image))) _mesh_out = make3d(log_queue, z123_image) log_queue.put("done") @spaces.GPU @rr.thread_local_stream("InstantMesh") def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed): log_queue = SimpleQueue() stream = rr.binary_stream() blueprint = generate_blueprint() rr.send_blueprint(blueprint) yield stream.read() handle = threading.Thread( target=compute, args=[log_queue, input_image, do_remove_background, sample_steps, sample_seed] ) handle.start() while True: msg = log_queue.get() if msg == "done": break else: entity_path, entity = msg rr.log(entity_path, entity) yield stream.read() handle.join() _HEADER_ = """

Duplicate of the InstantMesh space that uses Rerun for visualization.

InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture. Technical report: ArXiv. Source code: Github. """ with gr.Blocks() as demo: gr.Markdown(_HEADER_) with gr.Row(variant="panel"): with gr.Column(scale=1): with gr.Row(): input_image = gr.Image( label="Input Image", image_mode="RGBA", sources="upload", # width=256, # height=256, type="pil", elem_id="content_image", ) with gr.Row(): with gr.Group(): do_remove_background = gr.Checkbox(label="Remove Background", value=True) sample_seed = gr.Number(value=42, label="Seed Value", precision=0) sample_steps = gr.Slider(label="Sample Steps", minimum=30, maximum=75, value=75, step=5) with gr.Row(): submit = gr.Button("Generate", elem_id="generate", variant="primary") with gr.Row(variant="panel"): gr.Examples( examples=[os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))], inputs=[input_image], label="Examples", cache_examples=False, examples_per_page=16, ) with gr.Column(scale=2): viewer = Rerun(streaming=True, height=800) with gr.Row(): gr.Markdown("""Try a different seed value if the result is unsatisfying (Default: 42).""") mv_images = gr.State() submit.click(fn=check_input_image, inputs=[input_image]).success( fn=log_to_rr, inputs=[input_image, do_remove_background, sample_steps, sample_seed], outputs=[viewer] ) demo.launch()