""" https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L30 """ import copy from matplotlib import pyplot as plt import functools import json import os from pathlib import Path from pdb import set_trace as st from typing import Any import einops import blobfile as bf import imageio import numpy as np import torch as th import torch.distributed as dist import torchvision from PIL import Image from torch.nn.parallel.distributed import DistributedDataParallel as DDP from torch.optim import AdamW from torch.utils.tensorboard.writer import SummaryWriter from tqdm import tqdm from guided_diffusion import dist_util, logger from guided_diffusion.fp16_util import MixedPrecisionTrainer from guided_diffusion.nn import update_ema from guided_diffusion.resample import LossAwareSampler, UniformSampler # from .train_util import TrainLoop3DRec from guided_diffusion.train_util import (TrainLoop, calc_average_loss, find_ema_checkpoint, find_resume_checkpoint, get_blob_logdir, log_loss_dict, log_rec3d_loss_dict, parse_resume_step_from_filename) from guided_diffusion.gaussian_diffusion import ModelMeanType from ldm.modules.encoders.modules import FrozenClipImageEmbedder, TextEmbedder, FrozenCLIPTextEmbedder import dnnlib from dnnlib.util import requires_grad from dnnlib.util import calculate_adaptive_weight from ..train_util_diffusion import TrainLoop3DDiffusion from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer # from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD # joint diffusion and rec class # from .controlLDM import TrainLoop3DDiffusionLSGM_Control # joint diffusion and rec class from .train_util_diffusion_lsgm_noD_joint import TrainLoop3DDiffusionLSGMJointnoD # joint diffusion and rec class __conditioning_keys__ = { 'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y' } def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class TrainLoop3DDiffusionLSGM_crossattn(TrainLoop3DDiffusionLSGMJointnoD): def __init__(self, *, rec_model, denoise_model, diffusion, sde_diffusion, control_model, control_key, only_mid_control, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, resume_cldm_checkpoint=None, use_fp16=False, fp16_scale_growth=0.001, schedule_sampler=None, weight_decay=0, lr_anneal_steps=0, iterations=10001, ignore_resume_opt=False, freeze_ae=False, denoised_ae=True, triplane_scaling_divider=10, use_amp=False, diffusion_input_size=224, normalize_clip_encoding=False, scale_clip_encoding=1.0, cfg_dropout_prob=0., cond_key='img_sr', **kwargs): super().__init__(rec_model=rec_model, denoise_model=denoise_model, diffusion=diffusion, sde_diffusion=sde_diffusion, control_model=control_model, control_key=control_key, only_mid_control=only_mid_control, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, resume_cldm_checkpoint=resume_cldm_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, schedule_sampler=schedule_sampler, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, ignore_resume_opt=ignore_resume_opt, freeze_ae=freeze_ae, denoised_ae=denoised_ae, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, **kwargs) self.conditioning_key = 'c_crossattn' self.cond_key = cond_key self.instantiate_cond_stage(normalize_clip_encoding, scale_clip_encoding, cfg_dropout_prob) requires_grad(self.rec_model, False) self.rec_model.eval() # self.normalize_clip_encoding = normalize_clip_encoding # self.cfg_dropout_prob = cfg_dropout_prob def instantiate_cond_stage(self, normalize_clip_encoding, scale_clip_encoding, cfg_dropout_prob): # https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L509C1-L509C46 # self.cond_stage_model.train = disabled_train # type: ignore # st() if self.cond_key == 'caption': # for objaverse training (with extracted cap3d caption) self.cond_txt_model = TextEmbedder(dropout_prob=cfg_dropout_prob) else: # zero-shot Text to 3D using normalized clip latent self.cond_stage_model = FrozenClipImageEmbedder( 'ViT-L/14', dropout_prob=cfg_dropout_prob, normalize_encoding=normalize_clip_encoding, scale_clip_encoding=scale_clip_encoding) self.cond_stage_model.freeze() self.cond_txt_model = FrozenCLIPTextEmbedder( dropout_prob=cfg_dropout_prob, scale_clip_encoding=scale_clip_encoding) self.cond_txt_model.freeze() @th.no_grad() def get_c_input(self, batch, bs=None, use_text=False, prompt="", *args, **kwargs): # using clip to transform control to tokens for crossattn cond_inp = None if self.cond_key == 'caption': c = self.cond_txt_model( cond_inp, train=self.ddpm_model.training ) # ! SD training text condition injection layer # st() # check whether context repeat? else: # zero shot if use_text: # for test assert prompt != "" c = self.cond_txt_model.encode(prompt) # ! for test # st() else: cond_inp = batch[self.cond_key] if bs is not None: cond_inp = cond_inp[:bs] cond_inp = cond_inp.to( memory_format=th.contiguous_format).float() c = self.cond_stage_model(cond_inp) # BS 768 # return dict(c_concat=[control]) # return dict(c_crossattn=[c], c_concat=[control]) # return dict(__conditioning_keys__[self.cond_key]=) # return {self.conditioning_key: [c], 'c_concat': [cond_inp]} return {self.conditioning_key: c, 'c_concat': [cond_inp]} # TODO, merge the APIs def apply_model_inference(self, x_noisy, t, c, model_kwargs={}): pred_params = self.ddp_ddpm_model( x_noisy, t, **{ **model_kwargs, 'context': c['c_crossattn'] }) return pred_params def apply_model(self, p_sample_batch, cond, model_kwargs={}): return super().apply_model( p_sample_batch, **{ **model_kwargs, 'context': cond['c_crossattn'] }) def run_step(self, batch, step='ldm_step'): # if step == 'diffusion_step_rec': if step == 'ldm_step': self.ldm_train_step(batch) # if took_step_ddpm: # self._update_cldm_ema() self._anneal_lr() self.log_step() def run_loop(self): while (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps): # let all processes sync up before starting with a new epoch of training # dist_util.synchronize() batch = next(self.data) self.run_step(batch, step='ldm_step') if self.step % self.log_interval == 0 and dist_util.get_rank( ) == 0: out = logger.dumpkvs() # * log to tensorboard for k, v in out.items(): self.writer.add_scalar(f'Loss/{k}', v, self.step + self.resume_step) # if self.step % self.eval_interval == 0 and self.step != 0: if self.step % self.eval_interval == 0: if dist_util.get_rank() == 0: # self.eval_ddpm_sample() # self.eval_cldm(use_ddim=True, unconditional_guidance_scale=7.5, prompt="") # during training, use image as condition self.eval_cldm(use_ddim=False, prompt="") # fix condition bug first # if self.sde_diffusion.args.train_vae: # self.eval_loop() th.cuda.empty_cache() dist_util.synchronize() if self.step % self.save_interval == 0: self.save(self.mp_trainer, self.mp_trainer.model_name) if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0: return self.step += 1 if self.step > self.iterations: print('reached maximum iterations, exiting') # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save(self.mp_trainer, self.mp_trainer.model_name) # if self.sde_diffusion.args.train_vae: # self.save(self.mp_trainer_rec, # self.mp_trainer_rec.model_name) exit() # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save(self.mp_trainer, self.mp_trainer.model_name) # rec and ddpm all fixed. # st() # self.save(self.mp_trainer_canonical_cvD, 'cvD') # ddpm + rec loss def ldm_train_step(self, batch, behaviour='cano', *args, **kwargs): """ add sds grad to all ae predicted x_0 """ # ! enable the gradient of both models requires_grad(self.ddpm_model, True) self.mp_trainer.zero_grad() # !!!! batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()) if isinstance( v, th.Tensor) else v for k, v in batch.items() } # =================================== ae part =================================== with th.cuda.amp.autocast(dtype=th.float16, enabled=self.mp_trainer.use_amp): loss = th.tensor(0.).to(dist_util.dev()) vae_out = self.ddp_rec_model( img=micro['img_to_encoder'], c=micro['c'], behaviour='encoder_vae', ) # pred: (B, 3, 64, 64) eps = vae_out[self.latent_name] # eps = vae_out.pop(self.latent_name) if 'bg_plane' in vae_out: eps = th.cat((eps, vae_out['bg_plane']), dim=1) # include background, B 12+4 32 32 p_sample_batch = self.prepare_ddpm(eps) cond = self.get_c_input(micro) # ! running diffusion forward ddpm_ret = self.apply_model(p_sample_batch, cond) if self.sde_diffusion.args.p_rendering_loss: target = micro pred = self.ddp_rec_model( # latent=vae_out, latent={ # **vae_out, self.latent_name: ddpm_ret['pred_x0_p'], 'latent_name': self.latent_name }, c=micro['c'], behaviour=self.render_latent_behaviour) # vae reconstruction loss with self.ddp_control_model.no_sync(): # type: ignore p_vae_recon_loss, rec_loss_dict = self.loss_class( pred, target, test_mode=False) log_rec3d_loss_dict(rec_loss_dict) # log_rec3d_loss_dict( # dict(p_vae_recon_loss=p_vae_recon_loss, )) loss = p_vae_recon_loss + ddpm_ret[ 'p_eps_objective'] # TODO, add obj_weight_t_p? else: loss = ddpm_ret['p_eps_objective'].mean() # ===================================================================== self.mp_trainer.backward(loss) # joint gradient descent # update ddpm accordingly self.mp_trainer.optimize(self.opt) if dist_util.get_rank() == 0 and self.step % 500 == 0: self.log_control_images(vae_out, p_sample_batch, micro, ddpm_ret) @th.inference_mode() def log_control_images(self, vae_out, p_sample_batch, micro, ddpm_ret): eps_t_p, t_p, logsnr_p = (p_sample_batch[k] for k in ( 'eps_t_p', 't_p', 'logsnr_p', )) pred_eps_p = ddpm_ret['pred_eps_p'] vae_out.pop('posterior') # for calculating kl loss vae_out_for_pred = { k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v for k, v in vae_out.items() } pred = self.ddp_rec_model(latent=vae_out_for_pred, c=micro['c'][0:1], behaviour=self.render_latent_behaviour) assert isinstance(pred, dict) pred_img = pred['image_raw'] gt_img = micro['img'] if 'depth' in micro: gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) else: gt_depth = th.zeros_like(gt_img[:, 0:1, ...]) if 'image_depth' in pred: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min()) else: pred_depth = th.zeros_like(gt_depth) gt_img = self.pool_128(gt_img) gt_depth = self.pool_128(gt_depth) # cond = self.get_c_input(micro) # hint = th.cat(cond['c_concat'], 1) gt_vis = th.cat( [ gt_img, gt_img, gt_img, # self.pool_128(hint), # gt_img, gt_depth.repeat_interleave(3, dim=1) ], dim=-1)[0:1] # TODO, fail to load depth. range [0, 1] # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L if 'bg_plane' in vae_out: noised_latent = { 'latent_normalized_2Ddiffusion': eps_t_p[0:1, :12] * self.triplane_scaling_divider, 'bg_plane': eps_t_p[0:1, 12:16] * self.triplane_scaling_divider, } else: noised_latent = { 'latent_normalized_2Ddiffusion': eps_t_p[0:1] * self.triplane_scaling_divider, } noised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=noised_latent, # latent=eps_t_p[0:1] * self. # triplane_scaling_divider, # TODO, how to define the scale automatically behaviour=self.render_latent_behaviour) pred_x0 = self.sde_diffusion._predict_x0_from_eps( eps_t_p, pred_eps_p, logsnr_p) # for VAE loss, denosied latent if 'bg_plane' in vae_out: denoised_latent = { 'latent_normalized_2Ddiffusion': pred_x0[0:1, :12] * self.triplane_scaling_divider, 'bg_plane': pred_x0[0:1, 12:16] * self.triplane_scaling_divider, } else: denoised_latent = { 'latent_normalized_2Ddiffusion': pred_x0[0:1] * self.triplane_scaling_divider, } # pred_xstart_3D denoised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=denoised_latent, # latent=pred_x0[0:1] * self. # triplane_scaling_divider, # TODO, how to define the scale automatically? behaviour=self.render_latent_behaviour) pred_vis = th.cat( [ self.pool_128(img) for img in ( pred_img[0:1], noised_ae_pred['image_raw'][0:1], denoised_ae_pred['image_raw'][0:1], # controlnet result pred_depth[0:1].repeat_interleave(3, dim=1)) ], dim=-1) # B, 3, H, W vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute(1, 2, 0).cpu() # ! pred in range[-1, 1] # vis_grid = torchvision.utils.make_grid(vis) # HWC vis = vis.numpy() * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg' ) if self.cond_key == 'caption': with open( f'{logger.get_dir()}/{self.step+self.resume_step}caption_{t_p[0].item():3}.txt', 'w') as f: f.write(micro['caption'][0]) print( 'log denoised vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}.jpg' ) th.cuda.empty_cache() @th.inference_mode() def eval_cldm(self, prompt="", use_ddim=False, unconditional_guidance_scale=1.0, save_img=False, use_train_trajectory=False, export_mesh=False, camera=None, overwrite_diff_inp_size=None): self.ddpm_model.eval() args = dnnlib.EasyDict( dict( batch_size=self.batch_size, image_size=self.diffusion_input_size, denoise_in_channels=self.rec_model.decoder.triplane_decoder. out_chans, # type: ignore clip_denoised=False, class_cond=False, use_ddim=use_ddim)) model_kwargs = {} if args.class_cond: classes = th.randint(low=0, high=NUM_CLASSES, size=(args.batch_size, ), device=dist_util.dev()) model_kwargs["y"] = classes diffusion = self.diffusion sample_fn = (diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop) extra_kwargs = {} if args.use_ddim: extra_kwargs.update( dict( unconditional_guidance_scale=unconditional_guidance_scale)) # for i, batch in enumerate(tqdm(self.eval_data)): # if use_train_trajectory: # batch = next(iter(self.data)) # else: # batch = next(iter(self.eval_data)) # st() # th.save(batch['c'].cpu(), 'assets/shapenet_eval_pose.pt') assert camera is not None # for evaluation batch = {'c': camera.clone()} # st() # use the first frame as the condition now novel_view_cond = { k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v[0:1] # micro['img'].shape[0], 0) for k, v in batch.items() } cond = self.get_c_input(novel_view_cond, use_text=prompt != "", prompt=prompt) # use specific prompt for debug # broadcast to args.batch_size cond = { k: cond_v.repeat_interleave(args.batch_size, 0) for k, cond_v in cond.items() if k == self.conditioning_key } for i in range(1): # st() noise_size = ( args.batch_size, self.ddpm_model.in_channels, self.diffusion_input_size if not overwrite_diff_inp_size else int(overwrite_diff_inp_size), self.diffusion_input_size if not overwrite_diff_inp_size else int(overwrite_diff_inp_size) ) triplane_sample = sample_fn( self, noise_size, cond=cond, clip_denoised=args.clip_denoised, model_kwargs=model_kwargs, mixing_normal=True, # ! device=dist_util.dev(), **extra_kwargs) # triplane_sample = th.zeros((args.batch_size, self.ddpm_model.in_channels, self.diffusion_input_size, self.diffusion_input_size), device=dist_util.dev()) th.cuda.empty_cache() for sub_idx in range(triplane_sample.shape[0]): self.render_video_given_triplane( triplane_sample[sub_idx:sub_idx + 1], self.rec_model, # compatible with join_model name_prefix=f'{self.step + self.resume_step}_{i+sub_idx}', save_img=save_img, render_reference=batch, # render_reference=None, export_mesh=export_mesh, render_all=True, ) del triplane_sample th.cuda.empty_cache() self.ddpm_model.train() @th.inference_mode() # def eval_loop(self, c_list:list): def eval_novelview_loop(self, rec_model): # novel view synthesis given evaluation camera trajectory video_out = imageio.get_writer( f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}.mp4', mode='I', fps=60, codec='libx264') all_loss_dict = [] novel_view_micro = {} # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval for i, batch in enumerate(tqdm(self.eval_data)): # for i in range(0, 8, self.microbatch): # c = c_list[i].to(dist_util.dev()).reshape(1, -1) micro = {k: v.to(dist_util.dev()) for k, v in batch.items()} if i == 0: novel_view_micro = { k: v[0:1].to(dist_util.dev()).repeat_interleave( micro['img'].shape[0], 0) for k, v in batch.items() } torchvision.utils.save_image( self.pool_128(novel_view_micro['img']), logger.get_dir() + '/FID_Cals/gt.png', normalize=True, val_range=(0, 1), padding=0) else: # if novel_view_micro['c'].shape[0] < micro['img'].shape[0]: novel_view_micro = { k: v[0:1].to(dist_util.dev()).repeat_interleave( micro['img'].shape[0], 0) for k, v in novel_view_micro.items() } th.manual_seed(0) # avoid vae re-sampling changes results pred = rec_model(img=novel_view_micro['img_to_encoder'], c=micro['c']) # pred: (B, 3, 64, 64) # ! move to other places, add tensorboard # pred_vis = th.cat([ # pred['image_raw'], # -pred['image_depth'].repeat_interleave(3, dim=1) # ], # dim=-1) # normalize depth # if True: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min()) # ! save pooled_depth = self.pool_128(pred_depth).repeat_interleave(3, dim=1) pred_vis = th.cat([ self.pool_128(micro['img']), self.pool_128(pred['image_raw']), pooled_depth, ], dim=-1) # B, 3, H, W # ! save depth name_prefix = i torchvision.utils.save_image(self.pool_128(pred['image_raw']), logger.get_dir() + '/FID_Cals/{}.png'.format(i), normalize=True, val_range=(0, 1), padding=0) torchvision.utils.save_image(self.pool_128(pooled_depth), logger.get_dir() + '/FID_Cals/{}_depth.png'.format(i), normalize=True, val_range=(0, 1), padding=0) vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy() vis = vis * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) for j in range(vis.shape[0]): video_out.append_data(vis[j]) video_out.close() del video_out # del pred_vis # del pred th.cuda.empty_cache()