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ddpm.py
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ddpm.py
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"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
import numpy as np
import torch
import torch.nn as nn
import torch.distributed as dist
try:
import lightning.pytorch as pl
from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
except:
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
import os
import itertools
from contextlib import contextmanager, nullcontext
from functools import partial
from einops import rearrange, repeat
from ldm.modules.fid.inception import InceptionV3
from ldm.modules.fid.fid_score import compute_statistics_of_path, calculate_frechet_distance
from ldm.models.autoencoder import *
from ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage
from ldm.models.clip_encoder import CLIPEncoder
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule, noise_like
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution, normal_kl
from ldm.modules.ema import LitEma
from ldm.modules.encoders.modules import *
from ldm.util import count_params, default, exists, instantiate_from_config, isimage, ismap, log_txt_as_img, mean_flat
from omegaconf import ListConfig
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from torchvision import transforms
from tqdm import tqdm
from PIL import Image
from mlperf_logging_utils import mllogger
import mlperf_logging.mllog.constants as mllog_constants
__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
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
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=None,
load_vae=True,
load_unet=False,
load_encoder=True,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="jpg",
first_stage_type="images",
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.,
use_fp16=True,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
validation_config=None):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
rank_zero_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.first_stage_type = first_stage_type
self.image_size = image_size
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.unet_config = unet_config
self.conditioning_key = conditioning_key
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)
rank_zero_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
self.make_it_fit = make_it_fit
self.ckpt = ckpt
self.load_vae = load_vae
self.load_unet = load_unet
self.load_encoder = load_encoder
self.ignore_keys = ignore_keys
self.load_only_unet = load_only_unet
self.reset_ema = reset_ema
self.reset_num_ema_updates = reset_num_ema_updates
if reset_ema:
assert exists(ckpt)
if reset_num_ema_updates:
rank_zero_info(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
self.timesteps = timesteps
self.beta_schedule = beta_schedule
self.given_betas = given_betas
self.linear_start = linear_start
self.linear_end = linear_end
self.cosine_s = cosine_s
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.logvar_init = logvar_init
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)
self.use_fp16 = use_fp16
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
self.to_pil_image = transforms.ToPILImage()
# Validation config
self.inception = None
self.clip_encoder = None
self.m1 = None
self.s1 = None
self.validation_save_images = False
self.validation_run_fid = False
self.validation_run_clip = False
self.validation_inecption_activations = []
self.validation_clip_scores = []
if validation_config is not None:
if validation_config["sampler"] == "plms":
self.sampler = PLMSSampler(self)
elif validation_config["sampler"] == "dpm":
self.sampler = DPMSolverSampler(self)
elif validation_config["sampler"] == "ddim":
self.sampler = DDIMSampler(self)
else:
raise NotImplementedError(f"Sampler {self.sampler} not yet supported")
self.validation_sampler_steps = validation_config["steps"]
self.validation_scale = validation_config["scale"]
self.validation_ddim_eta = validation_config["ddim_eta"]
self.prompt_key = validation_config["prompt_key"]
self.image_fname_key = validation_config["image_fname_key"]
self.validation_save_images = validation_config["save_images"]["enabled"]
self.validation_run_fid = validation_config["fid"]["enabled"]
self.validation_run_clip = validation_config["clip"]["enabled"]
if self.validation_save_images or self.validation_run_fid or self.validation_run_clip:
if self.validation_save_images:
self.validation_base_output_dir = validation_config["save_images"]["base_output_dir"]
if self.validation_run_fid:
self.inception_weights_url = validation_config["fid"]["inception_weights_url"]
self.inception_cache_dir = validation_config["fid"]["cache_dir"]
self.fid_gt_path = validation_config["fid"]["gt_path"]
if self.validation_run_clip:
self.clip_version = validation_config["clip"]["clip_version"]
self.clip_cache_dir = validation_config["clip"]["cache_dir"]
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))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas**2 / (2 * self.posterior_variance * to_torch(alphas) *
(1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
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:
rank_zero_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:
rank_zero_info(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), load_vae=True, load_unet=False, load_encoder=True):
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):
rank_zero_info("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in itertools.chain(self.named_parameters(), self.named_buffers())])
for name, param in tqdm(itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
if not load_vae:
keys = list(sd.keys())
for k in keys:
if k.startswith("first_stage_model"):
rank_zero_info("Deleting key {} from state_dict.".format(k))
del sd[k]
if not load_encoder:
keys = list(sd.keys())
for k in keys:
if k.startswith("cond_stage_model"):
rank_zero_info("Deleting key {} from state_dict.".format(k))
del sd[k]
if not load_unet:
keys = list(sd.keys())
for k in keys:
if k.startswith("model.diffusion_model"):
rank_zero_info("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
rank_zero_info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
rank_zero_info(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
rank_zero_info(f"\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
# 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)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
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_v(self, x, noise, t):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
)
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
elif self.parameterization == "v":
target = self.get_v(x_start, noise, t)
else:
raise NotImplementedError(f"Parameterization {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]
if self.use_fp16:
x = x.to(memory_format=torch.contiguous_format).half()
else:
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):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
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
# TODO(ahmadki): lightning will pad the last batch, which will cause duplicates
# samples after all_gather, which might scew the FID and CLIP scores
@torch.no_grad()
def validation_step(self, batch, batch_idx):
prompts = batch[self.prompt_key]
fnames = batch[self.image_fname_key]
# x_T = torch.randn([len(prompts), 4, 64, 64], device=self.device) // debug
x_T = None
if self.validation_save_images or self.validation_run_fid or self.validation_run_clip:
with self.ema_scope("validation"):
uc = None
if self.validation_scale != 1.0:
uc = self.get_learned_conditioning(len(prompts) * [""])
c = self.get_learned_conditioning(prompts)
shape = [self.channels, self.image_size, self.image_size]
samples, _ = self.sampler.sample(S=self.validation_sampler_steps,
conditioning=c,
batch_size=len(prompts),
shape=shape,
verbose=False,
unconditional_guidance_scale=self.validation_scale,
unconditional_conditioning=uc,
eta=self.validation_ddim_eta,
x_T=x_T)
x_samples = self.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
if self.validation_save_images:
output_dir = os.path.join(self.validation_base_output_dir, f"epoch={self.current_epoch:06}-step={self.global_step:09}")
os.makedirs(output_dir, exist_ok=True)
for fname, x_sample in zip(fnames, x_samples):
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
img.save(os.path.join(output_dir, f"{fname}.png"))
if self.validation_run_fid:
if self.inception is None:
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
self.inception = InceptionV3(output_blocks=[block_idx],
weights_url=self.inception_weights_url,
model_dir=self.inception_cache_dir).cuda()
self.inception.eval()
pred = self.inception(x_samples)[0].squeeze(3).squeeze(2)
self.validation_inecption_activations.append(pred)
if self.validation_run_clip:
if self.clip_encoder is None:
self.clip_encoder = CLIPEncoder(clip_version=self.clip_version, cache_dir=self.clip_cache_dir)
for prompt, x_sampler in zip(prompts, x_samples):
# TODO(ahmadki): this is not efficient but clip model expects a PIL image,
# modify the clip encoder so we can use raw tensors instead
img = self.to_pil_image(x_sampler)
score = self.clip_encoder.get_clip_score(prompt, img)
self.validation_clip_scores.append(score)
def on_validation_epoch_end(self):
if self.validation_run_fid:
inception_activations = torch.cat(self.validation_inecption_activations, 0)
inception_activations = self.all_gather(inception_activations)
inception_activations = inception_activations.view(-1, inception_activations.shape[2])
# Ground truth
if self.m1 is None or self.s1 is None:
self.m1, self.s1 = compute_statistics_of_path(self.fid_gt_path, self.inception, 50, 2048, self.device, 4)
# Generated images
m2 = np.mean(inception_activations.detach().cpu().numpy(), axis=0)
s2 = np.cov(inception_activations.detach().cpu().numpy(), rowvar=False)
fid_value = calculate_frechet_distance(self.m1, self.s1, m2, s2)
self.log("validation/fid", fid_value)
self.validation_inecption_activations.clear() # free memory
if self.validation_run_clip:
clip_scores = torch.cat(self.validation_clip_scores, 0)
clip_scores = self.all_gather(clip_scores)
clip_scores = clip_scores.view(-1, clip_scores.shape[2])
clip_score = np.mean(clip_scores.detach().cpu().numpy())
self.log("validation/clip", clip_score)
self.validation_clip_scores.clear() # free memory
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="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
use_fp16=True,
force_null_conditioning=False,
*args,
**kwargs):
self.force_null_conditioning = force_null_conditioning
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
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
conditioning_key = None
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
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.first_stage_config = first_stage_config
self.cond_stage_config = cond_stage_config
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
if self.reset_num_ema_updates:
rank_zero_info(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
def configure_sharded_model(self) -> None:
rank_zero_info("Configure sharded model for LatentDiffusion")
self.model = DiffusionWrapper(self.unet_config, self.conditioning_key)
count_params(self.model, verbose=True)
if self.use_ema:
self.model_ema = LitEma(self.model)
if self.ckpt is not None:
self.init_from_ckpt(self.ckpt, ignore_keys=self.ignore_keys,
load_vae=self.load_vae, load_encoder=self.load_encoder, load_unet=self.load_unet)
if self.reset_ema:
assert self.use_ema
rank_zero_info(
f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
self.register_schedule(given_betas=self.given_betas,
beta_schedule=self.beta_schedule,
timesteps=self.timesteps,
linear_start=self.linear_start,
linear_end=self.linear_end,
cosine_s=self.cosine_s)
self.logvar = torch.full(fill_value=self.logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
self.instantiate_first_stage(self.first_stage_config)
self.instantiate_cond_stage(self.cond_stage_config)
if self.ckpt is not None:
self.init_from_ckpt(self.ckpt, ignore_keys=self.ignore_keys,
load_vae=self.load_vae, load_encoder=self.load_encoder, load_unet=self.load_unet)
self.restarted_from_ckpt = True
if self.reset_ema:
assert self.use_ema
rank_zero_info(
f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
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):
# only for very first batch
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
rank_zero_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())
rank_zero_info(f"setting self.scale_factor to {self.scale_factor}")
rank_zero_info("### USING STD-RESCALING ###")
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:
if config == "__is_first_stage__":
rank_zero_info("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
rank_zero_info(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
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:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(
self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
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_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
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.half() if self.use_fp16 else self.scale_factor * z
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 meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(
weighting,
self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"],
)
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1,
padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1,
padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self,
batch,
k,