import os import tempfile import gradio as gr import numpy as np from launch.utils import find_cuda import spaces import torch from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from einops import rearrange 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 instantMesh.src.utils.camera_util import (FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras) from instantMesh.src.utils.mesh_util import save_glb, save_obj from instantMesh.src.utils.train_util import instantiate_from_config # Configuration cuda_path = find_cuda() config_path = 'instantMesh/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 = config_name.startswith('instant-mesh') device = torch.device('cuda') # Load diffusion model print('Loading diffusion model ...') pipeline = DiffusionPipeline.from_pretrained( "sudo-ai/zero123plus-v1.2", custom_pipeline="./instantMesh/zero123plus", torch_dtype=torch.float16, ) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( pipeline.scheduler.config, timestep_spacing='trailing' ) 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) # 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 = 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) def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False): 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 @spaces.GPU def generate_mvs(input_image): sample_seed = np.random.randint(0, 1000000) seed_everything(sample_seed) sample_steps = 75 z123_image = pipeline( input_image, num_inference_steps=sample_steps).images[0] show_image = np.asarray(z123_image, dtype=np.uint8) show_image = torch.from_numpy(show_image) show_image = rearrange( show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2) show_image = rearrange( show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3) show_image = Image.fromarray(show_image.numpy()) return z123_image, show_image @spaces.GPU def make3d(images): 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() images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) input_cameras = get_zero123plus_input_cameras( batch_size=1, radius=4.0).to(device) render_cameras = get_render_cameras( batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device) images = images.unsqueeze(0).to(device) images = v2.functional.resize( images, (320, 320), interpolation=3, antialias=True).clamp(0, 1) mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name print(mesh_fpath) mesh_basename = os.path.basename(mesh_fpath).split('.')[0] mesh_dirname = os.path.dirname(mesh_fpath) mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb") with torch.no_grad(): planes = model.forward_planes(images, input_cameras) mesh_out = model.extract_mesh( planes, use_texture_map=False, **infer_config) vertices, faces, vertex_colors = mesh_out vertices = vertices[:, [1, 2, 0]] save_glb(vertices, faces, vertex_colors, mesh_glb_fpath) save_obj(vertices, faces, vertex_colors, mesh_fpath) print(f"Mesh saved to {mesh_fpath}") return mesh_fpath, mesh_glb_fpath def model_generation_ui(processed_image): with gr.Column(): with gr.Row(): submit_mesh = gr.Button( "Generate 3D Model", elem_id="generate", variant="primary") with gr.Row(): with gr.Column(): mv_show_images = gr.Image( label="Generated Multi-views", type="pil", interactive=False ) with gr.Column(): with gr.Tab("OBJ"): output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", interactive=False, ) with gr.Tab("GLB"): output_model_glb = gr.Model3D( label="Output Model (GLB Format)", interactive=False, ) mv_images = gr.State() submit_mesh.click(fn=generate_mvs, inputs=[processed_image], outputs=[mv_images, mv_show_images]).success( fn=make3d, inputs=[mv_images], outputs=[ output_model_obj, output_model_glb] ) return output_model_obj, output_model_glb