"""
wild mixture of
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/CompVis/taming-transformers
-- merci
"""
import logging
import os
import random
from contextlib import contextmanager
from functools import partial
import numpy as np
from einops import rearrange, repeat
from tqdm import tqdm
mainlogger = logging.getLogger('mainlogger')
import pytorch_lightning as pl
import torch
import torch.nn as nn
from pytorch_lightning.utilities import rank_zero_only
from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
from torchvision.utils import make_grid
from lvdm.basics import disabled_train
from lvdm.common import default, exists, extract_into_tensor, noise_like
from lvdm.distributions import DiagonalGaussianDistribution, normal_kl
from lvdm.ema import LitEma
from lvdm.models.samplers.ddim import DDIMSampler
from lvdm.models.utils_diffusion import make_beta_schedule
from utils.utils import instantiate_from_config
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor=None,
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.channels = channels
self.temporal_length = unet_config.params.temporal_length
self.image_size = image_size # try conv?
if isinstance(self.image_size, int):
self.image_size = [self.image_size, self.image_size]
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
#count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
mainlogger.info(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
mainlogger.info(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
mainlogger.info("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
mainlogger.info(f"Missing Keys: {missing}")
if len(unexpected) > 0:
mainlogger.info(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
'''
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
'''
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="caption",
cond_stage_trainable=False,
cond_stage_forward=None,
conditioning_key=None,
uncond_prob=0.2,
uncond_type="empty_seq",
scale_factor=1.0,
scale_by_std=False,
# added for LVDM
encoder_type="2d",
frame_cond=None,
only_model=False,
logdir=None,
empty_params_only=False,
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
conditioning_key = default(conditioning_key, 'crossattn')
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
self.empty_params_only = empty_params_only
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.first_stage_config = first_stage_config
self.cond_stage_config = cond_stage_config
self.clip_denoised = False
self.cond_stage_forward = cond_stage_forward
self.encoder_type = encoder_type
assert(encoder_type in ["2d", "3d"])
self.uncond_prob = uncond_prob
self.classifier_free_guidance = True if uncond_prob > 0 else False
assert(uncond_type in ["zero_embed", "empty_seq"])
self.uncond_type = uncond_type
## future frame prediction
self.frame_cond = frame_cond
if self.frame_cond:
# frame_len = self.model.diffusion_model.temporal_length
frame_len = self.temporal_length
cond_mask = torch.zeros(frame_len, dtype=torch.float32)
cond_mask[:self.frame_cond] = 1.0
## b,c,t,h,w
self.cond_mask = cond_mask[None,None,:,None,None]
mainlogger.info("---training for %d-frame conditoning T2V"%(self.frame_cond))
else:
self.cond_mask = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
self.restarted_from_ckpt = True
self.logdir = logdir
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None):
# only for very first batch, reset the self.scale_factor
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and \
not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
mainlogger.info("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
mainlogger.info(f"setting self.scale_factor to {self.scale_factor}")
mainlogger.info("### USING STD-RESCALING ###")
mainlogger.info(f"std={z.flatten().std()}")
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
model = instantiate_from_config(config)
self.cond_stage_model = model
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def get_first_stage_encoding(self, encoder_posterior, noise=None):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample(noise=noise)
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
@torch.no_grad()
def encode_first_stage(self, x):
if self.encoder_type == "2d" and x.dim() == 5:
return self.encode_first_stage_2DAE(x)
encoder_posterior = self.first_stage_model.encode(x)
results = self.get_first_stage_encoding(encoder_posterior).detach()
return results
def encode_first_stage_2DAE(self, x):
"""encode frame by frame"""
b, _, t, _, _ = x.shape
results = torch.cat([self.get_first_stage_encoding(self.first_stage_model.encode(x[:,:,i])).detach().unsqueeze(2) for i in range(t)], dim=2)
return results
def decode_first_stage_2DAE(self, z, **kwargs):
"""decode frame by frame"""
_, _, t, _, _ = z.shape
results = torch.cat([self.first_stage_model.decode(z[:,:,i], **kwargs).unsqueeze(2) for i in range(t)], dim=2)
return results
def _decode_core(self, z, **kwargs):
z = 1. / self.scale_factor * z
if self.encoder_type == "2d" and z.dim() == 5:
return self.decode_first_stage_2DAE(z)
results = self.first_stage_model.decode(z, **kwargs)
return results
@torch.no_grad()
def decode_first_stage(self, z, **kwargs):
return self._decode_core(z, **kwargs)
def differentiable_decode_first_stage(self, z, **kwargs):
"""same as decode_first_stage but without decorator"""
return self._decode_core(z, **kwargs)
@torch.no_grad()
def get_batch_input(self, batch, random_uncond, return_first_stage_outputs=False, return_original_cond=False, is_imgbatch=False):
## image/video shape: b, c, t, h, w
data_key = 'jpg' if is_imgbatch else self.first_stage_key
x = super().get_input(batch, data_key)
if is_imgbatch:
## pack image as video
#x = x[:,:,None,:,:]
b = x.shape[0] // self.temporal_length
x = rearrange(x, '(b t) c h w -> b c t h w', b=b, t=self.temporal_length)
x_ori = x
## encode video frames x to z via a 2D encoder
z = self.encode_first_stage(x)
## get caption condition
cond_key = 'txt' if is_imgbatch else self.cond_stage_key
cond = batch[cond_key]
if random_uncond and self.uncond_type == 'empty_seq':
for i, ci in enumerate(cond):
if random.random() < self.uncond_prob:
cond[i] = ""
if isinstance(cond, dict) or isinstance(cond, list):
cond_emb = self.get_learned_conditioning(cond)
else:
cond_emb = self.get_learned_conditioning(cond.to(self.device))
if random_uncond and self.uncond_type == 'zero_embed':
for i, ci in enumerate(cond):
if random.random() < self.uncond_prob:
cond_emb[i] = torch.zeros_like(ci)
out = [z, cond_emb]
## optional output: self-reconst or caption
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x_ori, xrec])
if return_original_cond:
out.append(cond)
return out
def forward(self, x, c, **kwargs):
if 't' in kwargs:
t = kwargs.pop('t')
else:
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, c, t, **kwargs)
def shared_step(self, batch, random_uncond, **kwargs):
is_imgbatch = False
if "loader_img" in batch.keys():
ratio = 10.0 / self.temporal_length
if random.uniform(0.,10.) < ratio:
is_imgbatch = True
batch = batch["loader_img"]
else:
batch = batch["loader_video"]
else:
pass
x, c = self.get_batch_input(batch, random_uncond=random_uncond, is_imgbatch=is_imgbatch)
loss, loss_dict = self(x, c, is_imgbatch=is_imgbatch, **kwargs)
return loss, loss_dict
def apply_model(self, x_noisy, t, cond, **kwargs):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
x_recon = self.model(x_noisy, t, **cond, **kwargs)
if isinstance(x_recon, tuple):
return x_recon[0]
else:
return x_recon
def p_losses(self, x_start, cond, t, noise=None, **kwargs):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
if self.frame_cond:
if self.cond_mask.device is not self.device:
self.cond_mask = self.cond_mask.to(self.device)
## condition on fist few frames
x_noisy = x_start * self.cond_mask + (1.-self.cond_mask) * x_noisy
model_output = self.apply_model(x_noisy, t, cond, **kwargs)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
if self.frame_cond:
## [b,c,t,h,w]: only care about the predicted part (avoid disturbance)
model_output = model_output[:,:,self.frame_cond:,:,:]
target = target[:,:,self.frame_cond:,:,:]
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3, 4])
if torch.isnan(loss_simple).any():
print(f"loss_simple exists nan: {loss_simple}")
# import pdb; pdb.set_trace()
for i in range(loss_simple.shape[0]):
if torch.isnan(loss_simple[i]).any():
loss_simple[i] = torch.zeros_like(loss_simple[i])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
if self.logvar.device is not self.device:
self.logvar = self.logvar.to(self.device)
logvar_t = self.logvar[t]
# logvar_t = self.logvar[t.item()].to(self.device) # device conflict when ddp shared
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
if self.original_elbo_weight > 0:
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3, 4))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch, random_uncond=self.classifier_free_guidance)
## sync_dist | rank_zero_only
self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=False)
#self.log("epoch/global_step", self.global_step.float(), prog_bar=True, logger=True, on_step=True, on_epoch=False)
'''
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False, rank_zero_only=True)
'''
if (batch_idx+1) % self.log_every_t == 0:
mainlogger.info(f"batch:{batch_idx}|epoch:{self.current_epoch} [globalstep:{self.global_step}]: loss={loss}")
return loss
def _get_denoise_row_from_list(self, samples, desc=''):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device)))
n_log_timesteps = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W
if denoise_row.dim() == 5:
# img, num_imgs= n_log_timesteps * bs, grid_size=[bs,n_log_timesteps]
# 先batch再n，grid时候一行是一个sample的不同steps，batch是列，行是n
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps)
elif denoise_row.dim() == 6:
# video, grid_size=[n_log_timesteps*bs, t]
video_length = denoise_row.shape[3]
denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w')
denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
denoise_grid = make_grid(denoise_grid, nrow=video_length)
else:
raise ValueError
return denoise_grid
@torch.no_grad()
def log_images(self, batch, sample=True, ddim_steps=200, ddim_eta=1., plot_denoise_rows=False, \
unconditional_guidance_scale=1.0, **kwargs):
""" log images for LatentDiffusion """
## TBD: currently, classifier_free_guidance sampling is only supported by DDIM
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_batch_input(batch, random_uncond=False,
return_first_stage_outputs=True,
return_original_cond=True)
N, _, T, H, W = x.shape
log["inputs"] = x
log["reconst"] = xrec
log["condition"] = xc
if sample:
# get uncond embedding for classifier-free guidance sampling
if unconditional_guidance_scale != 1.0:
if isinstance(c, dict):
c_cat, c_emb = c["c_concat"][0], c["c_crossattn"][0]
#log["condition_cat"] = c_cat
else:
c_emb = c
if self.uncond_type == "empty_seq":
prompts = N * [""]
uc = self.get_learned_conditioning(prompts)
elif self.uncond_type == "zero_embed":
uc = torch.zeros_like(c_emb)
## hybrid case
if isinstance(c, dict):
uc_hybrid = {"c_concat": [c_cat], "c_crossattn": [uc]}
uc = uc_hybrid
else:
uc = None
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc, mask=self.cond_mask, x0=z, **kwargs)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
return log
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs):
t_in = t
model_out = self.apply_model(x, t_in, c, **kwargs)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs)
if return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
# sample an initial noise
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, \
verbose=True, timesteps=None, mask=None, x0=None, shape=None, **kwargs):
if shape is None:
shape = (batch_size, self.channels, self.temporal_length, *self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps,
mask=mask, x0=x0, **kwargs)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.temporal_length, *self.image_size)
kwargs.update({"clean_cond": True})
samples, intermediates =ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs)
return samples, intermediates
def configure_optimizers(self):
""" configure_optimizers for LatentDiffusion """
lr = self.learning_rate
if self.empty_params_only and hasattr(self, "empty_paras"):
params = [p for n, p in self.model.named_parameters() if n in self.empty_paras]
print('self.empty_paras', len(self.empty_paras))
for n, p in self.model.named_parameters():
if n not in self.empty_paras:
p.requires_grad = False
mainlogger.info(f"@Training [{len(params)}] Empty Paramters ONLY.")
else:
params = list(self.model.parameters())
mainlogger.info(f"@Training [{len(params)}] Full Paramters.")
if self.learn_logvar:
mainlogger.info('Diffusion model optimizing logvar')
if isinstance(params[0], dict):
params.append({"params": [self.logvar]})
else:
params.append(self.logvar)
## optimizer
optimizer = torch.optim.AdamW(params, lr=lr)
## lr scheduler
if self.use_scheduler:
mainlogger.info("Setting up LambdaLR scheduler...")
lr_scheduler = self.configure_schedulers(optimizer)
return [optimizer], [lr_scheduler]
return optimizer
def configure_schedulers(self, optimizer):
assert 'target' in self.scheduler_config
scheduler_name = self.scheduler_config.target.split('.')[-1]
interval = self.scheduler_config.interval
frequency = self.scheduler_config.frequency
if scheduler_name == "LambdaLRScheduler":
scheduler = instantiate_from_config(self.scheduler_config)
scheduler.start_step = self.global_step
lr_scheduler = {
'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
'interval': interval,
'frequency': frequency
}
elif scheduler_name == "CosineAnnealingLRScheduler":
scheduler = instantiate_from_config(self.scheduler_config)
decay_steps = scheduler.decay_steps
last_step = -1 if self.global_step == 0 else scheduler.start_step
lr_scheduler = {
'scheduler': CosineAnnealingLR(optimizer, T_max=decay_steps, last_epoch=last_step),
'interval': interval,
'frequency': frequency
}
else:
raise NotImplementedError
return lr_scheduler
class DiffusionWrapper(pl.LightningModule):
def __init__(self, diff_model_config, conditioning_key):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
c_adm=None, s=None, mask=None, **kwargs):
# temporal_context = fps is foNone
if self.conditioning_key is None:
out = self.diffusion_model(x, t)
elif self.conditioning_key == 'concat':
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t, **kwargs)
elif self.conditioning_key == 'crossattn':
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(x, t, context=cc, **kwargs)
elif self.conditioning_key == 'hybrid':
## it is just right [b,c,t,h,w]: concatenate in channel dim
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc, **kwargs)
elif self.conditioning_key == 'resblockcond':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, context=cc)
elif self.conditioning_key == 'adm':
cc = c_crossattn[0]
out = self.diffusion_model(x, t, y=cc)
elif self.conditioning_key == 'hybrid-adm':
assert c_adm is not None
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
elif self.conditioning_key == 'hybrid-time':
assert s is not None
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc, s=s)
elif self.conditioning_key == 'concat-time-mask':
# assert s is not None
# mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
xc = torch.cat([x] + c_concat, dim=1)
out = self.diffusion_model(xc, t, context=None, s=s, mask=mask)
elif self.conditioning_key == 'concat-adm-mask':
# assert s is not None
# mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
if c_concat is not None:
xc = torch.cat([x] + c_concat, dim=1)
else:
xc = x
out = self.diffusion_model(xc, t, context=None, y=s, mask=mask)
elif self.conditioning_key == 'hybrid-adm-mask':
cc = torch.cat(c_crossattn, 1)
if c_concat is not None:
xc = torch.cat([x] + c_concat, dim=1)
else:
xc = x
out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask)
elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
# assert s is not None
assert c_adm is not None
xc = torch.cat([x] + c_concat, dim=1)
cc = torch.cat(c_crossattn, 1)
out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm)
else:
raise NotImplementedError()
return out