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"""
https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L30
"""
import copy
import functools
import json
import os
from pathlib import Path
from pdb import set_trace as st
from typing import Any
from click import prompt
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, FrozenOpenCLIPImagePredictionEmbedder, FrozenOpenCLIPImageEmbedder
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',
use_eos_feature=False,
compile=False,
**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,
compile=compile,
**kwargs)
self.conditioning_key = 'c_crossattn'
self.cond_key = cond_key
self.instantiate_cond_stage(normalize_clip_encoding,
scale_clip_encoding, cfg_dropout_prob,
use_eos_feature)
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,
use_eos_feature):
# https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/models/diffusion/ddpm.py#L509C1-L509C46
# self.cond_stage_model.train = disabled_train # type: ignore
if self.cond_key == 'caption':
self.cond_txt_model = TextEmbedder(dropout_prob=cfg_dropout_prob,
use_eos_feature=use_eos_feature)
elif self.cond_key == 'img':
self.cond_img_model = FrozenOpenCLIPImagePredictionEmbedder(
1, 1,
FrozenOpenCLIPImageEmbedder(freeze=True,
device=dist_util.dev(),
init_device=dist_util.dev()))
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="",
force_drop_ids=None,
*args,
**kwargs):
if use_text:
cond_inp = prompt
else:
if 'caption' in self.cond_key: # support caption-img
cond_inp = batch['caption']
else:
cond_inp = batch[self.cond_key]
# if bs is not None:
# cond_inp = cond_inp[:bs]
# using clip to transform control to tokens for crossattn
control = None
if 'caption' in self.cond_key:
c = self.cond_txt_model(
cond_inp,
train=self.ddpm_model.training,
force_drop_ids=force_drop_ids,
) # ! SD training text condition injection layer
if bs is None: # duplicated sample
if c.shape[0] != batch['c'].shape[0]:
c = th.repeat_interleave(c,
batch['c'].shape[0] // c.shape[0],
dim=0)
else:
assert c.shape[0] == bs
# st()
# if 'img' in self.cond_key:
# ! later
# if 'img' in batch:
# control = batch['img'] + 0.02 * th.randn_like(
# batch['img']) # follow SVD?
elif self.cond_key == 'img':
c = self.cond_img_model(cond_inp)
# control = batch['img']
control = batch['img'] + 0.02 * th.randn_like(
batch['img']) # follow SVD?
else: # zero shot
if use_text: # for test
assert prompt != ""
c = self.cond_txt_model.encode(prompt) # ! for test
else:
cond_inp = cond_inp.to(
memory_format=th.contiguous_format).float()
c = self.cond_stage_model(cond_inp) # BS 768
# if c.shape[0] < batch['img_to_encoder'].shape[0]:
# c = th.repeat_interleave(c, batch['img_to_encoder'].shape[0]//c.shape[0], dim=0)
# return dict(c_concat=[control])
# return dict(c_crossattn=c, c_concat=batch['img'])
# if self.cond_key == 'img':
# return dict(c_crossattn=c, c_concat=control)
return dict(c_crossattn=c)
# else:
# return dict(c_crossattn=c)
# 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,
timesteps=t,
**{
**model_kwargs, 'context':
c['c_crossattn'],
'hint':
c.get('c_concat', None)
})
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'],
'hint': cond.get('c_concat', None)
# **cond,
})
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):
# eval camera
camera = th.load('eval_pose.pt', map_location=dist_util.dev())
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 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
if self.cond_key == 'caption':
self.eval_cldm(
use_ddim=False,
prompt="a voxelized dog",
use_train_trajectory=False,
camera=camera) # fix condition bug first
else:
pass # TODO
# self.eval_cldm(use_ddim=False,
# prompt="",
# use_train_trajectory=False,
# camera=camera) # 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() # !!!!
if 'img' in batch:
batch_size = batch['img'].shape[0]
else:
batch_size = len(batch['caption'])
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())
if 'latent' in micro:
vae_out = {self.latent_name: micro['latent']}
else:
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] / self.triplane_scaling_divider
# 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, bs=eps.shape[0])
# ! 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']
if 'posterior' in vae_out:
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']
if 'img' in micro:
gt_img = micro['img']
else:
gt_img = th.zeros_like(pred['image_raw'])
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
if 'img' in micro:
vis = th.cat([gt_vis, pred_vis],
dim=-2)[0].permute(1, 2,
0).cpu() # ! pred in range[-1, 1]
else:
vis = pred_vis[0].permute(1, 2, 0).cpu()
# 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,
camera=None,
num_samples=1,
num_instances=1,
export_mesh=False,
):
self.ddpm_model.eval()
args = dnnlib.EasyDict(
dict(
# batch_size=1,
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)
# for i, batch in enumerate(tqdm(self.eval_data)):
# use the first frame as the condition now
extra_kwargs = {}
uc = None
if args.use_ddim:
if unconditional_guidance_scale != 1.0:
uc = self.get_c_input(
{self.cond_key: 'None'},
use_text=True,
prompt="None",
bs=1, # TODO, support BS>1 later
force_drop_ids=np.array(
[ # ! make sure using dropped tokens
1
])) # use specific prompt for debug
extra_kwargs.update(
dict(
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc, # TODO
objv_inference=True,
# {
# k : unconditional_guidance_scale
# for k in cond.keys()
# }
))
# hint = th.cat(cond['c_concat'], 1)
# record cond images
# broadcast to args.batch_size
for instance in range(num_instances):
if self.cond_key == 'caption':
if camera is not None:
batch = {'c': camera.clone()}
else:
if use_train_trajectory:
batch = next(iter(self.data))
else:
try:
batch = next(self.eval_data)
except Exception as e:
self.eval_data = iter(self.eval_data)
batch = next(self.eval_data)
if camera is not None:
batch['c'] = camera.clone()
# ! generate new samples
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
cond = {
k: cond_v.repeat_interleave(args.batch_size, 0)
for k, cond_v in cond.items()
# if k == self.conditioning_key
}
if self.cond_key == 'caption':
if prompt != '':
with open(
f'{logger.get_dir()}/triplane_{self.step+self.resume_step}_{instance}_caption.txt',
'w') as f:
f.write(prompt)
else:
with open(
f'{logger.get_dir()}/triplane_{self.step+self.resume_step}_{instance}_caption.txt',
'w') as f:
try:
f.write(novel_view_cond['caption'][0])
except Exception as e:
pass
elif self.cond_key == 'img':
torchvision.utils.save_image(
cond['c_concat'],
f'{logger.get_dir()}/{self.step + self.resume_step}_{instance}_cond.jpg',
normalize=True,
value_range=(-1, 1))
# continue
for i in range(num_samples):
triplane_sample = sample_fn(
self,
(
args.batch_size,
self.ddpm_model.in_channels
if not self.ddpm_model.roll_out else 3 *
self.ddpm_model.in_channels, # type: ignore
self.diffusion_input_size,
self.diffusion_input_size),
cond=cond,
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
# mixing_normal=True, # !
mixing_normal=self.ddpm_model.mixed_prediction, # !
device=dist_util.dev(),
**extra_kwargs)
th.cuda.empty_cache()
# render the generated samples
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}_{instance}_{i+sub_idx}',
save_img=save_img,
render_reference=batch,
export_mesh=export_mesh)
# save gt
# video_out = imageio.get_writer(
# f'{logger.get_dir()}/triplane_{self.step + self.resume_step}_{i}_reference.mp4',
# mode='I',
# fps=15,
# codec='libx264')
# for j in range(batch['img'].shape[0]
# ): # ! currently only export one plane at a time
# cpu_gt = batch['img'][j].cpu().permute(1,2,0).numpy()
# cpu_gt = (cpu_gt*127.5)+127.5
# video_out.append_data(cpu_gt.astype(np.uint8))
# video_out.close()
# del video_out
# del triplane_sample
# th.cuda.empty_cache()
self.ddpm_model.train()
class TrainLoop3DDiffusionLSGM_crossattn_controlNet(
TrainLoop3DDiffusionLSGM_crossattn):
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,
cfg_dropout_prob=0,
cond_key='img_sr',
use_eos_feature=False,
compile=False,
**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,
normalize_clip_encoding=normalize_clip_encoding,
scale_clip_encoding=scale_clip_encoding,
cfg_dropout_prob=cfg_dropout_prob,
cond_key=cond_key,
use_eos_feature=use_eos_feature,
compile=compile,
**kwargs)
# st()
self.control_model = control_model
self.control_key = control_key
self.only_mid_control = only_mid_control
self.control_scales = [1.0] * 13
self.sd_locked = True
self._setup_control_model()
def _setup_control_model(self):
requires_grad(self.rec_model, False)
requires_grad(self.ddpm_model, False)
self.mp_cldm_trainer = MixedPrecisionTrainer(
model=self.control_model,
use_fp16=self.use_fp16,
fp16_scale_growth=self.fp16_scale_growth,
use_amp=self.use_amp,
model_name='cldm')
self.ddp_control_model = DDP(
self.control_model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
requires_grad(self.ddp_control_model, True)
# ! load trainable copy
# TODO
# st()
try:
logger.log(f"load pretrained controlnet, not trainable copy.")
self._load_and_sync_parameters(
model=self.control_model,
model_name='cldm',
resume_checkpoint=self.resume_cldm_checkpoint,
) # if available
except:
logger.log(f"load trainable copy to controlnet")
model_state_dict = self.control_model.state_dict()
for k, v in self.ddpm_model.state_dict().items():
if k in model_state_dict.keys() and v.size(
) == model_state_dict[k].size():
model_state_dict[k] = v
self.control_model.load_state_dict(model_state_dict)
# self._load_and_sync_parameters(
# model=self.control_model,
# model_name='ddpm') # load pre-trained SD
cldm_param = [{
'name': 'cldm.parameters()',
'params': self.control_model.parameters(),
}]
# if self.sde_diffusion.args.unfix_logit:
# self.ddpm_model.mixing_logit.requires_grad_(True)
# cldm_param.append({
# 'name': 'mixing_logit',
# 'params': self.ddpm_model.mixing_logit,
# })
self.opt_cldm = AdamW(cldm_param,
lr=self.lr,
weight_decay=self.weight_decay)
if self.sd_locked:
del self.opt
del self.mp_trainer
# add control during inference
def apply_model_inference(self, x_noisy, t, c, model_kwargs={}):
control = self.ddp_control_model(
x=x_noisy,
# hint=th.cat(c['c_concat'], 1),
hint=c['c_concat'],
timesteps=t,
context=None)
control = [c * scale for c, scale in zip(control, self.control_scales)]
model_kwargs.update({'control': control})
return super().apply_model_inference(x_noisy, t, c, model_kwargs)
def apply_control_model(self, p_sample_batch, cond):
x_noisy, t, = (p_sample_batch[k] for k in ('eps_t_p', 't_p'))
control = self.ddp_control_model(
x=x_noisy,
# hint=th.cat(cond['c_concat'], 1),
hint=cond['c_concat'],
timesteps=t,
context=None)
control = [c * scale for c, scale in zip(control, self.control_scales)]
return control
def apply_model(self, p_sample_batch, cond, model_kwargs={}):
control = self.apply_control_model(p_sample_batch,
cond) # len(control): 13
model_kwargs.update({'control': control})
return super().apply_model(p_sample_batch, cond, model_kwargs)
# cldm 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.ddp_control_model, True)
self.mp_cldm_trainer.zero_grad() # !!!!
if 'img' in batch:
batch_size = batch['img'].shape[0]
else:
batch_size = len(batch['caption'])
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_cldm_trainer.use_amp):
loss = th.tensor(0.).to(dist_util.dev())
if 'latent' in micro:
vae_out = {self.latent_name: micro['latent']}
else:
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] / self.triplane_scaling_divider
# 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, bs=eps.shape[0])
# ! 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_cldm_trainer.backward(loss) # joint gradient descent
# p self.control_model.input_hint_block[0].bias
# update ddpm accordingly
self.mp_cldm_trainer.optimize(self.opt_cldm)
if dist_util.get_rank() == 0 and self.step % 500 == 0:
self.log_control_images(vae_out, p_sample_batch, micro, ddpm_ret)
def run_loop(self):
# eval camera
camera = th.load('eval_pose.pt', map_location=dist_util.dev())
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
if self.cond_key == 'caption':
self.eval_cldm(
use_ddim=False,
prompt="a voxelized dog",
use_train_trajectory=False,
camera=camera) # fix condition bug first
else:
pass # TODO
# self.eval_cldm(use_ddim=False,
# prompt="",
# use_train_trajectory=False,
# camera=camera) # 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_cldm_trainer,
self.mp_cldm_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)
# self.save(self.mp_trainer,
# self.mp_trainer.model_name) # rec and ddpm all fixed.
# st()
# self.save(self.mp_trainer_canonical_cvD, 'cvD')
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