import argparse import datetime import logging import inspect import math import os from typing import Dict, Optional, Tuple, List from omegaconf import OmegaConf from PIL import Image import cv2 import numpy as np from dataclasses import dataclass from packaging import version import shutil from collections import defaultdict import torch import torch.nn.functional as F import torch.utils.checkpoint import torchvision.transforms.functional as TF from torchvision.utils import make_grid, save_image import transformers import accelerate from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from diffusers.utils import check_min_version, deprecate, is_wandb_available from diffusers.utils.import_utils import is_xformers_available from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel from mvdiffusion.data.single_image_dataset import SingleImageDataset as MVDiffusionDataset from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline from einops import rearrange from rembg import remove import pdb weight_dtype = torch.float16 @dataclass class TestConfig: pretrained_model_name_or_path: str pretrained_unet_path:str revision: Optional[str] validation_dataset: Dict save_dir: str seed: Optional[int] validation_batch_size: int dataloader_num_workers: int local_rank: int pipe_kwargs: Dict pipe_validation_kwargs: Dict unet_from_pretrained_kwargs: Dict validation_guidance_scales: List[float] validation_grid_nrow: int camera_embedding_lr_mult: float num_views: int camera_embedding_type: str pred_type: str # joint, or ablation enable_xformers_memory_efficient_attention: bool cond_on_normals: bool cond_on_colors: bool def log_validation(dataloader, pipeline, cfg: TestConfig, weight_dtype, name, save_dir): pipeline.set_progress_bar_config(disable=True) if cfg.seed is None: generator = None else: generator = torch.Generator(device=pipeline.device).manual_seed(cfg.seed) images_cond, images_pred = [], defaultdict(list) for i, batch in tqdm(enumerate(dataloader)): # (B, Nv, 3, H, W) imgs_in = batch['imgs_in'] alphas = batch['alphas'] # (B, Nv, Nce) camera_embeddings = batch['camera_embeddings'] filename = batch['filename'] bsz, num_views = imgs_in.shape[0], imgs_in.shape[1] # (B*Nv, 3, H, W) imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W") alphas = rearrange(alphas, "B Nv C H W -> (B Nv) C H W") # (B*Nv, Nce) camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce") images_cond.append(imgs_in) with torch.autocast("cuda"): # B*Nv images for guidance_scale in cfg.validation_guidance_scales: out = pipeline( imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs ).images images_pred[f"{name}-sample_cfg{guidance_scale:.1f}"].append(out) cur_dir = os.path.join(save_dir, f"cropsize-{cfg.validation_dataset.crop_size}-cfg{guidance_scale:.1f}") # pdb.set_trace() for i in range(bsz): scene = os.path.basename(filename[i]) print(scene) scene_dir = os.path.join(cur_dir, scene) outs_dir = os.path.join(scene_dir, "outs") masked_outs_dir = os.path.join(scene_dir, "masked_outs") os.makedirs(outs_dir, exist_ok=True) os.makedirs(masked_outs_dir, exist_ok=True) img_in = imgs_in[i*num_views] alpha = alphas[i*num_views] img_in = torch.cat([img_in, alpha], dim=0) save_image(img_in, os.path.join(scene_dir, scene+".png")) for j in range(num_views): view = VIEWS[j] idx = i*num_views + j pred = out[idx] # pdb.set_trace() out_filename = f"{cfg.pred_type}_000_{view}.png" pred = save_image(pred, os.path.join(outs_dir, out_filename)) rm_pred = remove(pred) save_image_numpy(rm_pred, os.path.join(scene_dir, out_filename)) torch.cuda.empty_cache() def save_image(tensor, fp): ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy() # pdb.set_trace() im = Image.fromarray(ndarr) im.save(fp) return ndarr def save_image_numpy(ndarr, fp): im = Image.fromarray(ndarr) im.save(fp) def log_validation_joint(dataloader, pipeline, cfg: TestConfig, weight_dtype, name, save_dir): pipeline.set_progress_bar_config(disable=True) if cfg.seed is None: generator = None else: generator = torch.Generator(device=pipeline.device).manual_seed(cfg.seed) images_cond, normals_pred, images_pred = [], defaultdict(list), defaultdict(list) for i, batch in tqdm(enumerate(dataloader)): # repeat (2B, Nv, 3, H, W) imgs_in = torch.cat([batch['imgs_in']]*2, dim=0) filename = batch['filename'] # (2B, Nv, Nce) camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0) task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0) camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1) # (B*Nv, 3, H, W) imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W") # (B*Nv, Nce) camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce") images_cond.append(imgs_in) num_views = len(VIEWS) with torch.autocast("cuda"): # B*Nv images for guidance_scale in cfg.validation_guidance_scales: out = pipeline( imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs ).images bsz = out.shape[0] // 2 normals_pred = out[:bsz] images_pred = out[bsz:] cur_dir = os.path.join(save_dir, f"cropsize-{cfg.validation_dataset.crop_size}-cfg{guidance_scale:.1f}") for i in range(bsz//num_views): scene = filename[i] scene_dir = os.path.join(cur_dir, scene) normal_dir = os.path.join(scene_dir, "normals") masked_colors_dir = os.path.join(scene_dir, "masked_colors") os.makedirs(normal_dir, exist_ok=True) os.makedirs(masked_colors_dir, exist_ok=True) for j in range(num_views): view = VIEWS[j] idx = i*num_views + j normal = normals_pred[idx] color = images_pred[idx] normal_filename = f"normals_000_{view}.png" rgb_filename = f"rgb_000_{view}.png" normal = save_image(normal, os.path.join(normal_dir, normal_filename)) color = save_image(color, os.path.join(scene_dir, rgb_filename)) rm_normal = remove(normal) rm_color = remove(color) save_image_numpy(rm_normal, os.path.join(scene_dir, normal_filename)) save_image_numpy(rm_color, os.path.join(masked_colors_dir, rgb_filename)) torch.cuda.empty_cache() def load_wonder3d_pipeline(cfg): pipeline = MVDiffusionImagePipeline.from_pretrained( cfg.pretrained_model_name_or_path, torch_dtype=weight_dtype ) # pipeline.to('cuda:0') pipeline.unet.enable_xformers_memory_efficient_attention() if torch.cuda.is_available(): pipeline.to('cuda:0') # sys.main_lock = threading.Lock() return pipeline def main( cfg: TestConfig ): # If passed along, set the training seed now. if cfg.seed is not None: set_seed(cfg.seed) pipeline = load_wonder3d_pipeline(cfg) if cfg.enable_xformers_memory_efficient_attention: if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) if xformers_version == version.parse("0.0.16"): print( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) pipeline.unet.enable_xformers_memory_efficient_attention() print("use xformers.") else: raise ValueError("xformers is not available. Make sure it is installed correctly") # Get the dataset validation_dataset = MVDiffusionDataset( **cfg.validation_dataset ) # DataLoaders creation: validation_dataloader = torch.utils.data.DataLoader( validation_dataset, batch_size=cfg.validation_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers ) os.makedirs(cfg.save_dir, exist_ok=True) if cfg.pred_type == 'joint': log_validation_joint( validation_dataloader, pipeline, cfg, weight_dtype, 'validation', cfg.save_dir ) else: log_validation( validation_dataloader, pipeline, cfg, weight_dtype, 'validation', cfg.save_dir ) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config', type=str, required=True) args, extras = parser.parse_known_args() from utils.misc import load_config # parse YAML config to OmegaConf cfg = load_config(args.config, cli_args=extras) print(cfg) schema = OmegaConf.structured(TestConfig) # cfg = OmegaConf.load(args.config) cfg = OmegaConf.merge(schema, cfg) if cfg.num_views == 6: VIEWS = ['front', 'front_right', 'right', 'back', 'left', 'front_left'] elif cfg.num_views == 4: VIEWS = ['front', 'right', 'back', 'left'] main(cfg)