"""
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
"""
from functools import partial
from contextlib import contextmanager
import numpy as np
from tqdm import tqdm
from einops import rearrange, repeat
import logging
mainlogger = logging.getLogger("mainlogger")
import torch
import torch.nn as nn
from torchvision.utils import make_grid
import pytorch_lightning as pl
from utils.utils import instantiate_from_config
from lvdm.ema import LitEma
from lvdm.distributions import DiagonalGaussianDistribution
from lvdm.models.utils_diffusion import make_beta_schedule
from lvdm.modules.encoders.ip_resampler import ImageProjModel, Resampler
from lvdm.basics import disabled_train
from lvdm.common import extract_into_tensor, noise_like, exists, default
__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=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
):
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
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)
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 = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, 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(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - 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) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - 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))
/ (2.0 * 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_(-1.0, 1.0)
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.scale_arr, t, x_start.shape)
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def get_input(self, batch, k):
x = batch[k]
x = x.to(memory_format=torch.contiguous_format).float()
return x
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
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,
encoder_type="2d",
only_model=False,
use_scale=False,
scale_a=1,
scale_b=0.3,
mid_step=400,
fix_scale_bug=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
# scale factor
self.use_scale = use_scale
if self.use_scale:
self.scale_a = scale_a
self.scale_b = scale_b
if fix_scale_bug:
scale_step = self.num_timesteps - mid_step
else: # bug
scale_step = self.num_timesteps
scale_arr1 = np.linspace(scale_a, scale_b, mid_step)
scale_arr2 = np.full(scale_step, scale_b)
scale_arr = np.concatenate((scale_arr1, scale_arr2))
scale_arr_prev = np.append(scale_a, scale_arr[:-1])
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("scale_arr", to_torch(scale_arr))
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
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
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
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
if self.use_scale:
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
* x_start
* extract_into_tensor(self.scale_arr, t, x_start.shape)
+ extract_into_tensor(
self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
)
* noise
)
else:
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 _freeze_model(self):
for name, para in self.model.diffusion_model.named_parameters():
para.requires_grad = False
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:
b, _, t, _, _ = x.shape
x = rearrange(x, "b c t h w -> (b t) c h w")
reshape_back = True
else:
reshape_back = False
encoder_posterior = self.first_stage_model.encode(x)
results = self.get_first_stage_encoding(encoder_posterior).detach()
if reshape_back:
results = rearrange(results, "(b t) c h w -> b c t h w", b=b, t=t)
return results
@torch.no_grad()
def encode_first_stage_2DAE(self, x):
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_core(self, z, **kwargs):
if self.encoder_type == "2d" and z.dim() == 5:
b, _, t, _, _ = z.shape
z = rearrange(z, "b c t h w -> (b t) c h w")
reshape_back = True
else:
reshape_back = False
z = 1.0 / self.scale_factor * z
results = self.first_stage_model.decode(z, **kwargs)
if reshape_back:
results = rearrange(results, "(b t) c h w -> b c t h w", b=b, t=t)
return results
@torch.no_grad()
def decode_first_stage(self, z, **kwargs):
return self.decode_core(z, **kwargs)
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 _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]
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 decode_first_stage_2DAE(self, z, **kwargs):
b, _, t, _, _ = z.shape
z = 1.0 / self.scale_factor * z
results = torch.cat(
[
self.first_stage_model.decode(z[:, :, i], **kwargs).unsqueeze(2)
for i in range(t)
],
dim=2,
)
return results
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_(-1.0, 1.0)
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=1.0,
noise_dropout=0.0,
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 > 0.0:
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 + (1.0 - 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
class LatentVisualDiffusion(LatentDiffusion):
def __init__(
self, cond_img_config, finegrained=False, random_cond=False, *args, **kwargs
):
super().__init__(*args, **kwargs)
self.random_cond = random_cond
self.instantiate_img_embedder(cond_img_config, freeze=True)
num_tokens = 16 if finegrained else 4
self.image_proj_model = self.init_projector(
use_finegrained=finegrained,
num_tokens=num_tokens,
input_dim=1024,
cross_attention_dim=1024,
dim=1280,
)
def instantiate_img_embedder(self, config, freeze=True):
embedder = instantiate_from_config(config)
if freeze:
self.embedder = embedder.eval()
self.embedder.train = disabled_train
for param in self.embedder.parameters():
param.requires_grad = False
def init_projector(
self, use_finegrained, num_tokens, input_dim, cross_attention_dim, dim
):
if not use_finegrained:
image_proj_model = ImageProjModel(
clip_extra_context_tokens=num_tokens,
cross_attention_dim=cross_attention_dim,
clip_embeddings_dim=input_dim,
)
else:
image_proj_model = Resampler(
dim=input_dim,
depth=4,
dim_head=64,
heads=12,
num_queries=num_tokens,
embedding_dim=dim,
output_dim=cross_attention_dim,
ff_mult=4,
)
return image_proj_model
## Never delete this func: it is used in log_images() and inference stage
def get_image_embeds(self, batch_imgs):
## img: b c h w
img_token = self.embedder(batch_imgs)
img_emb = self.image_proj_model(img_token)
return img_emb
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)
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)
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