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253 lines
9.2 KiB
253 lines
9.2 KiB
import os
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import argparse
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import numpy as np
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import torch
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import rembg
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from PIL import Image
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from torchvision.transforms import v2
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from pytorch_lightning import seed_everything
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from omegaconf import OmegaConf
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from einops import rearrange, repeat
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from tqdm import tqdm
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from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
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from src.utils.train_util import instantiate_from_config
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from src.utils.camera_util import (
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FOV_to_intrinsics,
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get_zero123plus_input_cameras,
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get_circular_camera_poses,
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)
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from src.utils.mesh_util import save_obj, save_obj_with_mtl
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from src.utils.infer_util import remove_background, resize_foreground, save_video
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def get_render_cameras(batch_size=1, M=120, radius=4.0, elevation=20.0, is_flexicubes=False):
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"""
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Get the rendering camera parameters.
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"""
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c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
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if is_flexicubes:
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cameras = torch.linalg.inv(c2ws)
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cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
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else:
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extrinsics = c2ws.flatten(-2)
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intrinsics = FOV_to_intrinsics(30.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
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cameras = torch.cat([extrinsics, intrinsics], dim=-1)
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cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
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return cameras
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def render_frames(model, planes, render_cameras, render_size=512, chunk_size=1, is_flexicubes=False):
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"""
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Render frames from triplanes.
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"""
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frames = []
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for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
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if is_flexicubes:
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frame = model.forward_geometry(
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planes,
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render_cameras[:, i:i+chunk_size],
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render_size=render_size,
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)['img']
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else:
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frame = model.forward_synthesizer(
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planes,
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render_cameras[:, i:i+chunk_size],
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render_size=render_size,
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)['images_rgb']
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frames.append(frame)
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frames = torch.cat(frames, dim=1)[0] # we suppose batch size is always 1
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return frames
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###############################################################################
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# Arguments.
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###############################################################################
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parser = argparse.ArgumentParser()
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parser.add_argument('config', type=str, help='Path to config file.')
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parser.add_argument('input_path', type=str, help='Path to input image or directory.')
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parser.add_argument('--output_path', type=str, default='outputs/', help='Output directory.')
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parser.add_argument('--diffusion_steps', type=int, default=75, help='Denoising Sampling steps.')
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parser.add_argument('--seed', type=int, default=42, help='Random seed for sampling.')
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parser.add_argument('--scale', type=float, default=1.0, help='Scale of generated object.')
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parser.add_argument('--distance', type=float, default=4.5, help='Render distance.')
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parser.add_argument('--view', type=int, default=6, choices=[4, 6], help='Number of input views.')
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parser.add_argument('--no_rembg', action='store_true', help='Do not remove input background.')
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parser.add_argument('--export_texmap', action='store_true', help='Export a mesh with texture map.')
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parser.add_argument('--save_video', action='store_true', help='Save a circular-view video.')
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args = parser.parse_args()
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seed_everything(args.seed)
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###############################################################################
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# Stage 0: Configuration.
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###############################################################################
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config = OmegaConf.load(args.config)
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config_name = os.path.basename(args.config).replace('.yaml', '')
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model_config = config.model_config
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infer_config = config.infer_config
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IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
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device = torch.device('cuda')
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# load diffusion model
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print('Loading diffusion model ...')
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pipeline = DiffusionPipeline.from_pretrained(
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"sudo-ai/zero123plus-v1.2",
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custom_pipeline="zero123plus",
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torch_dtype=torch.float16,
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)
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pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
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pipeline.scheduler.config, timestep_spacing='trailing'
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)
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# load custom white-background UNet
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print('Loading custom white-background unet ...')
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state_dict = torch.load(infer_config.unet_path, map_location='cpu')
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pipeline.unet.load_state_dict(state_dict, strict=True)
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pipeline = pipeline.to(device)
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# load reconstruction model
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print('Loading reconstruction model ...')
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model = instantiate_from_config(model_config)
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state_dict = torch.load(infer_config.model_path, map_location='cpu')['state_dict']
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state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.')}
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model.load_state_dict(state_dict, strict=True)
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model = model.to(device)
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if IS_FLEXICUBES:
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model.init_flexicubes_geometry(device, fovy=30.0)
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model = model.eval()
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# make output directories
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image_path = os.path.join(args.output_path, config_name, 'images')
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mesh_path = os.path.join(args.output_path, config_name, 'meshes')
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video_path = os.path.join(args.output_path, config_name, 'videos')
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os.makedirs(image_path, exist_ok=True)
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os.makedirs(mesh_path, exist_ok=True)
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os.makedirs(video_path, exist_ok=True)
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# process input files
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if os.path.isdir(args.input_path):
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input_files = [
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os.path.join(args.input_path, file)
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for file in os.listdir(args.input_path)
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if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.webp')
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]
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else:
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input_files = [args.input_path]
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print(f'Total number of input images: {len(input_files)}')
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###############################################################################
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# Stage 1: Multiview generation.
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###############################################################################
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rembg_session = None if args.no_rembg else rembg.new_session()
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outputs = []
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for idx, image_file in enumerate(input_files):
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name = os.path.basename(image_file).split('.')[0]
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print(f'[{idx+1}/{len(input_files)}] Imagining {name} ...')
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# remove background optionally
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input_image = Image.open(image_file)
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if not args.no_rembg:
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input_image = remove_background(input_image, rembg_session)
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input_image = resize_foreground(input_image, 0.85)
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# sampling
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output_image = pipeline(
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input_image,
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num_inference_steps=args.diffusion_steps,
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).images[0]
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output_image.save(os.path.join(image_path, f'{name}.png'))
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print(f"Image saved to {os.path.join(image_path, f'{name}.png')}")
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images = np.asarray(output_image, dtype=np.float32) / 255.0
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images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640)
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images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320)
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outputs.append({'name': name, 'images': images})
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# delete pipeline to save memory
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del pipeline
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###############################################################################
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# Stage 2: Reconstruction.
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###############################################################################
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input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0*args.scale).to(device)
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chunk_size = 20 if IS_FLEXICUBES else 1
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for idx, sample in enumerate(outputs):
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name = sample['name']
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print(f'[{idx+1}/{len(outputs)}] Creating {name} ...')
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images = sample['images'].unsqueeze(0).to(device)
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images = v2.functional.resize(images, 320, interpolation=3, antialias=True).clamp(0, 1)
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if args.view == 4:
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indices = torch.tensor([0, 2, 4, 5]).long().to(device)
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images = images[:, indices]
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input_cameras = input_cameras[:, indices]
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with torch.no_grad():
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# get triplane
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planes = model.forward_planes(images, input_cameras)
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# get mesh
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mesh_path_idx = os.path.join(mesh_path, f'{name}.obj')
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mesh_out = model.extract_mesh(
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planes,
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use_texture_map=args.export_texmap,
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**infer_config,
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)
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if args.export_texmap:
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vertices, faces, uvs, mesh_tex_idx, tex_map = mesh_out
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save_obj_with_mtl(
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vertices.data.cpu().numpy(),
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uvs.data.cpu().numpy(),
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faces.data.cpu().numpy(),
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mesh_tex_idx.data.cpu().numpy(),
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tex_map.permute(1, 2, 0).data.cpu().numpy(),
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mesh_path_idx,
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)
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else:
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vertices, faces, vertex_colors = mesh_out
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save_obj(vertices, faces, vertex_colors, mesh_path_idx)
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print(f"Mesh saved to {mesh_path_idx}")
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# get video
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if args.save_video:
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video_path_idx = os.path.join(video_path, f'{name}.mp4')
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render_size = infer_config.render_resolution
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render_cameras = get_render_cameras(
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batch_size=1,
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M=120,
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radius=args.distance,
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elevation=20.0,
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is_flexicubes=IS_FLEXICUBES,
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).to(device)
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frames = render_frames(
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model,
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planes,
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render_cameras=render_cameras,
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render_size=render_size,
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chunk_size=chunk_size,
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is_flexicubes=IS_FLEXICUBES,
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)
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save_video(
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frames,
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video_path_idx,
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fps=30,
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)
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print(f"Video saved to {video_path_idx}")
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