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from math import sqrt
from random import random
from functools import partial
from contextlib import contextmanager, nullcontext
from typing import List, Union
from collections import namedtuple
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.cuda.amp import autocast
from torch.nn.parallel import DistributedDataParallel
import torchvision.transforms as T
import kornia.augmentation as K
from einops import rearrange, repeat, reduce
from imagen_pytorch.imagen_pytorch import (
GaussianDiffusionContinuousTimes,
Unet,
NullUnet,
first,
exists,
identity,
maybe,
default,
cast_tuple,
cast_uint8_images_to_float,
eval_decorator,
pad_tuple_to_length,
resize_image_to,
calc_all_frame_dims,
safe_get_tuple_index,
right_pad_dims_to,
module_device,
normalize_neg_one_to_one,
unnormalize_zero_to_one,
compact,
maybe_transform_dict_key
)
from imagen_pytorch.imagen_video import (
Unet3D,
resize_video_to,
scale_video_time
)
from imagen_pytorch.t5 import t5_encode_text, get_encoded_dim, DEFAULT_T5_NAME
# constants
Hparams_fields = [
'num_sample_steps',
'sigma_min',
'sigma_max',
'sigma_data',
'rho',
'P_mean',
'P_std',
'S_churn',
'S_tmin',
'S_tmax',
'S_noise'
]
Hparams = namedtuple('Hparams', Hparams_fields)
# helper functions
def log(t, eps = 1e-20):
return torch.log(t.clamp(min = eps))
# main class
class ElucidatedImagen(nn.Module):
def __init__(
self,
unets,
*,
image_sizes, # for cascading ddpm, image size at each stage
text_encoder_name = DEFAULT_T5_NAME,
text_embed_dim = None,
channels = 3,
cond_drop_prob = 0.1,
random_crop_sizes = None,
resize_mode = 'nearest',
temporal_downsample_factor = 1,
resize_cond_video_frames = True,
lowres_sample_noise_level = 0.2, # in the paper, they present a new trick where they noise the lowres conditioning image, and at sample time, fix it to a certain level (0.1 or 0.3) - the unets are also made to be conditioned on this noise level
per_sample_random_aug_noise_level = False, # unclear when conditioning on augmentation noise level, whether each batch element receives a random aug noise value - turning off due to @marunine's find
condition_on_text = True,
auto_normalize_img = True, # whether to take care of normalizing the image from [0, 1] to [-1, 1] and back automatically - you can turn this off if you want to pass in the [-1, 1] ranged image yourself from the dataloader
dynamic_thresholding = True,
dynamic_thresholding_percentile = 0.95, # unsure what this was based on perusal of paper
only_train_unet_number = None,
lowres_noise_schedule = 'linear',
num_sample_steps = 32, # number of sampling steps
sigma_min = 0.002, # min noise level
sigma_max = 80, # max noise level
sigma_data = 0.5, # standard deviation of data distribution
rho = 7, # controls the sampling schedule
P_mean = -1.2, # mean of log-normal distribution from which noise is drawn for training
P_std = 1.2, # standard deviation of log-normal distribution from which noise is drawn for training
S_churn = 80, # parameters for stochastic sampling - depends on dataset, Table 5 in apper
S_tmin = 0.05,
S_tmax = 50,
S_noise = 1.003,
):
super().__init__()
self.only_train_unet_number = only_train_unet_number
# conditioning hparams
self.condition_on_text = condition_on_text
self.unconditional = not condition_on_text
# channels
self.channels = channels
# automatically take care of ensuring that first unet is unconditional
# while the rest of the unets are conditioned on the low resolution image produced by previous unet
unets = cast_tuple(unets)
num_unets = len(unets)
# randomly cropping for upsampler training
self.random_crop_sizes = cast_tuple(random_crop_sizes, num_unets)
assert not exists(first(self.random_crop_sizes)), 'you should not need to randomly crop image during training for base unet, only for upsamplers - so pass in `random_crop_sizes = (None, 128, 256)` as example'
# lowres augmentation noise schedule
self.lowres_noise_schedule = GaussianDiffusionContinuousTimes(noise_schedule = lowres_noise_schedule)
# get text encoder
self.text_encoder_name = text_encoder_name
self.text_embed_dim = default(text_embed_dim, lambda: get_encoded_dim(text_encoder_name))
self.encode_text = partial(t5_encode_text, name = text_encoder_name)
# construct unets
self.unets = nn.ModuleList([])
self.unet_being_trained_index = -1 # keeps track of which unet is being trained at the moment
for ind, one_unet in enumerate(unets):
assert isinstance(one_unet, (Unet, Unet3D, NullUnet))
is_first = ind == 0
one_unet = one_unet.cast_model_parameters(
lowres_cond = not is_first,
cond_on_text = self.condition_on_text,
text_embed_dim = self.text_embed_dim if self.condition_on_text else None,
channels = self.channels,
channels_out = self.channels
)
self.unets.append(one_unet)
# determine whether we are training on images or video
is_video = any([isinstance(unet, Unet3D) for unet in self.unets])
self.is_video = is_video
self.right_pad_dims_to_datatype = partial(rearrange, pattern = ('b -> b 1 1 1' if not is_video else 'b -> b 1 1 1 1'))
self.resize_to = resize_video_to if is_video else resize_image_to
self.resize_to = partial(self.resize_to, mode = resize_mode)
# unet image sizes
self.image_sizes = cast_tuple(image_sizes)
assert num_unets == len(self.image_sizes), f'you did not supply the correct number of u-nets ({len(self.unets)}) for resolutions {self.image_sizes}'
self.sample_channels = cast_tuple(self.channels, num_unets)
# cascading ddpm related stuff
lowres_conditions = tuple(map(lambda t: t.lowres_cond, self.unets))
assert lowres_conditions == (False, *((True,) * (num_unets - 1))), 'the first unet must be unconditioned (by low resolution image), and the rest of the unets must have `lowres_cond` set to True'
self.lowres_sample_noise_level = lowres_sample_noise_level
self.per_sample_random_aug_noise_level = per_sample_random_aug_noise_level
# classifier free guidance
self.cond_drop_prob = cond_drop_prob
self.can_classifier_guidance = cond_drop_prob > 0.
# normalize and unnormalize image functions
self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
self.unnormalize_img = unnormalize_zero_to_one if auto_normalize_img else identity
self.input_image_range = (0. if auto_normalize_img else -1., 1.)
# dynamic thresholding
self.dynamic_thresholding = cast_tuple(dynamic_thresholding, num_unets)
self.dynamic_thresholding_percentile = dynamic_thresholding_percentile
# temporal interpolations
temporal_downsample_factor = cast_tuple(temporal_downsample_factor, num_unets)
self.temporal_downsample_factor = temporal_downsample_factor
self.resize_cond_video_frames = resize_cond_video_frames
self.temporal_downsample_divisor = temporal_downsample_factor[0]
assert temporal_downsample_factor[-1] == 1, 'downsample factor of last stage must be 1'
assert tuple(sorted(temporal_downsample_factor, reverse = True)) == temporal_downsample_factor, 'temporal downsample factor must be in order of descending'
# elucidating parameters
hparams = [
num_sample_steps,
sigma_min,
sigma_max,
sigma_data,
rho,
P_mean,
P_std,
S_churn,
S_tmin,
S_tmax,
S_noise,
]
hparams = [cast_tuple(hp, num_unets) for hp in hparams]
self.hparams = [Hparams(*unet_hp) for unet_hp in zip(*hparams)]
# one temp parameter for keeping track of device
self.register_buffer('_temp', torch.tensor([0.]), persistent = False)
# default to device of unets passed in
self.to(next(self.unets.parameters()).device)
def force_unconditional_(self):
self.condition_on_text = False
self.unconditional = True
for unet in self.unets:
unet.cond_on_text = False
@property
def device(self):
return self._temp.device
def get_unet(self, unet_number):
assert 0 < unet_number <= len(self.unets)
index = unet_number - 1
if isinstance(self.unets, nn.ModuleList):
unets_list = [unet for unet in self.unets]
delattr(self, 'unets')
self.unets = unets_list
if index != self.unet_being_trained_index:
for unet_index, unet in enumerate(self.unets):
unet.to(self.device if unet_index == index else 'cpu')
self.unet_being_trained_index = index
return self.unets[index]
def reset_unets_all_one_device(self, device = None):
device = default(device, self.device)
self.unets = nn.ModuleList([*self.unets])
self.unets.to(device)
self.unet_being_trained_index = -1
@contextmanager
def one_unet_in_gpu(self, unet_number = None, unet = None):
assert exists(unet_number) ^ exists(unet)
if exists(unet_number):
unet = self.unets[unet_number - 1]
cpu = torch.device('cpu')
devices = [module_device(unet) for unet in self.unets]
self.unets.to(cpu)
unet.to(self.device)
yield
for unet, device in zip(self.unets, devices):
unet.to(device)
# overriding state dict functions
def state_dict(self, *args, **kwargs):
self.reset_unets_all_one_device()
return super().state_dict(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
self.reset_unets_all_one_device()
return super().load_state_dict(*args, **kwargs)
# dynamic thresholding
def threshold_x_start(self, x_start, dynamic_threshold = True):
if not dynamic_threshold:
return x_start.clamp(-1., 1.)
s = torch.quantile(
rearrange(x_start, 'b ... -> b (...)').abs(),
self.dynamic_thresholding_percentile,
dim = -1
)
s.clamp_(min = 1.)
s = right_pad_dims_to(x_start, s)
return x_start.clamp(-s, s) / s
# derived preconditioning params - Table 1
def c_skip(self, sigma_data, sigma):
return (sigma_data ** 2) / (sigma ** 2 + sigma_data ** 2)
def c_out(self, sigma_data, sigma):
return sigma * sigma_data * (sigma_data ** 2 + sigma ** 2) ** -0.5
def c_in(self, sigma_data, sigma):
return 1 * (sigma ** 2 + sigma_data ** 2) ** -0.5
def c_noise(self, sigma):
return log(sigma) * 0.25
# preconditioned network output
# equation (7) in the paper
def preconditioned_network_forward(
self,
unet_forward,
noised_images,
sigma,
*,
sigma_data,
clamp = False,
dynamic_threshold = True,
**kwargs
):
batch, device = noised_images.shape[0], noised_images.device
if isinstance(sigma, float):
sigma = torch.full((batch,), sigma, device = device)
padded_sigma = self.right_pad_dims_to_datatype(sigma)
net_out = unet_forward(
self.c_in(sigma_data, padded_sigma) * noised_images,
self.c_noise(sigma),
**kwargs
)
out = self.c_skip(sigma_data, padded_sigma) * noised_images + self.c_out(sigma_data, padded_sigma) * net_out
if not clamp:
return out
return self.threshold_x_start(out, dynamic_threshold)
# sampling
# sample schedule
# equation (5) in the paper
def sample_schedule(
self,
num_sample_steps,
rho,
sigma_min,
sigma_max
):
N = num_sample_steps
inv_rho = 1 / rho
steps = torch.arange(num_sample_steps, device = self.device, dtype = torch.float32)
sigmas = (sigma_max ** inv_rho + steps / (N - 1) * (sigma_min ** inv_rho - sigma_max ** inv_rho)) ** rho
sigmas = F.pad(sigmas, (0, 1), value = 0.) # last step is sigma value of 0.
return sigmas
@torch.no_grad()
def one_unet_sample(
self,
unet,
shape,
*,
unet_number,
clamp = True,
dynamic_threshold = True,
cond_scale = 1.,
use_tqdm = True,
inpaint_videos = None,
inpaint_images = None,
inpaint_masks = None,
inpaint_resample_times = 5,
init_images = None,
skip_steps = None,
sigma_min = None,
sigma_max = None,
**kwargs
):
# video
is_video = len(shape) == 5
frames = shape[-3] if is_video else None
resize_kwargs = dict(target_frames = frames) if exists(frames) else dict()
# get specific sampling hyperparameters for unet
hp = self.hparams[unet_number - 1]
sigma_min = default(sigma_min, hp.sigma_min)
sigma_max = default(sigma_max, hp.sigma_max)
# get the schedule, which is returned as (sigma, gamma) tuple, and pair up with the next sigma and gamma
sigmas = self.sample_schedule(hp.num_sample_steps, hp.rho, sigma_min, sigma_max)
gammas = torch.where(
(sigmas >= hp.S_tmin) & (sigmas <= hp.S_tmax),
min(hp.S_churn / hp.num_sample_steps, sqrt(2) - 1),
0.
)
sigmas_and_gammas = list(zip(sigmas[:-1], sigmas[1:], gammas[:-1]))
# images is noise at the beginning
init_sigma = sigmas[0]
images = init_sigma * torch.randn(shape, device = self.device)
# initializing with an image
if exists(init_images):
images += init_images
# keeping track of x0, for self conditioning if needed
x_start = None
# prepare inpainting images and mask
inpaint_images = default(inpaint_videos, inpaint_images)
has_inpainting = exists(inpaint_images) and exists(inpaint_masks)
resample_times = inpaint_resample_times if has_inpainting else 1
if has_inpainting:
inpaint_images = self.normalize_img(inpaint_images)
inpaint_images = self.resize_to(inpaint_images, shape[-1], **resize_kwargs)
inpaint_masks = self.resize_to(rearrange(inpaint_masks, 'b ... -> b 1 ...').float(), shape[-1], **resize_kwargs).bool()
# unet kwargs
unet_kwargs = dict(
sigma_data = hp.sigma_data,
clamp = clamp,
dynamic_threshold = dynamic_threshold,
cond_scale = cond_scale,
**kwargs
)
# gradually denoise
initial_step = default(skip_steps, 0)
sigmas_and_gammas = sigmas_and_gammas[initial_step:]
total_steps = len(sigmas_and_gammas)
for ind, (sigma, sigma_next, gamma) in tqdm(enumerate(sigmas_and_gammas), total = total_steps, desc = 'sampling time step', disable = not use_tqdm):
is_last_timestep = ind == (total_steps - 1)
sigma, sigma_next, gamma = map(lambda t: t.item(), (sigma, sigma_next, gamma))
for r in reversed(range(resample_times)):
is_last_resample_step = r == 0
eps = hp.S_noise * torch.randn(shape, device = self.device) # stochastic sampling
sigma_hat = sigma + gamma * sigma
added_noise = sqrt(sigma_hat ** 2 - sigma ** 2) * eps
images_hat = images + added_noise
self_cond = x_start if unet.self_cond else None
if has_inpainting:
images_hat = images_hat * ~inpaint_masks + (inpaint_images + added_noise) * inpaint_masks
model_output = self.preconditioned_network_forward(
unet.forward_with_cond_scale,
images_hat,
sigma_hat,
self_cond = self_cond,
**unet_kwargs
)
denoised_over_sigma = (images_hat - model_output) / sigma_hat
images_next = images_hat + (sigma_next - sigma_hat) * denoised_over_sigma
# second order correction, if not the last timestep
has_second_order_correction = sigma_next != 0
if has_second_order_correction:
self_cond = model_output if unet.self_cond else None
model_output_next = self.preconditioned_network_forward(
unet.forward_with_cond_scale,
images_next,
sigma_next,
self_cond = self_cond,
**unet_kwargs
)
denoised_prime_over_sigma = (images_next - model_output_next) / sigma_next
images_next = images_hat + 0.5 * (sigma_next - sigma_hat) * (denoised_over_sigma + denoised_prime_over_sigma)
images = images_next
if has_inpainting and not (is_last_resample_step or is_last_timestep):
# renoise in repaint and then resample
repaint_noise = torch.randn(shape, device = self.device)
images = images + (sigma - sigma_next) * repaint_noise
x_start = model_output if not has_second_order_correction else model_output_next # save model output for self conditioning
images = images.clamp(-1., 1.)
if has_inpainting:
images = images * ~inpaint_masks + inpaint_images * inpaint_masks
return self.unnormalize_img(images)
@torch.no_grad()
@eval_decorator
def sample(
self,
texts: List[str] = None,
text_masks = None,
text_embeds = None,
cond_images = None,
cond_video_frames = None,
post_cond_video_frames = None,
inpaint_videos = None,
inpaint_images = None,
inpaint_masks = None,
inpaint_resample_times = 5,
init_images = None,
skip_steps = None,
sigma_min = None,
sigma_max = None,
video_frames = None,
batch_size = 1,
cond_scale = 1.,
lowres_sample_noise_level = None,
start_at_unet_number = 1,
start_image_or_video = None,
stop_at_unet_number = None,
return_all_unet_outputs = False,
return_pil_images = False,
use_tqdm = True,
use_one_unet_in_gpu = True,
device = None,
):
device = default(device, self.device)
self.reset_unets_all_one_device(device = device)
cond_images = maybe(cast_uint8_images_to_float)(cond_images)
if exists(texts) and not exists(text_embeds) and not self.unconditional:
assert all([*map(len, texts)]), 'text cannot be empty'
with autocast(enabled = False):
text_embeds, text_masks = self.encode_text(texts, return_attn_mask = True)
text_embeds, text_masks = map(lambda t: t.to(device), (text_embeds, text_masks))
if not self.unconditional:
assert exists(text_embeds), 'text must be passed in if the network was not trained without text `condition_on_text` must be set to `False` when training'
text_masks = default(text_masks, lambda: torch.any(text_embeds != 0., dim = -1))
batch_size = text_embeds.shape[0]
# inpainting
inpaint_images = default(inpaint_videos, inpaint_images)
if exists(inpaint_images):
if self.unconditional:
if batch_size == 1: # assume researcher wants to broadcast along inpainted images
batch_size = inpaint_images.shape[0]
assert inpaint_images.shape[0] == batch_size, 'number of inpainting images must be equal to the specified batch size on sample `sample(batch_size=<int>)``'
assert not (self.condition_on_text and inpaint_images.shape[0] != text_embeds.shape[0]), 'number of inpainting images must be equal to the number of text to be conditioned on'
assert not (self.condition_on_text and not exists(text_embeds)), 'text or text encodings must be passed into imagen if specified'
assert not (not self.condition_on_text and exists(text_embeds)), 'imagen specified not to be conditioned on text, yet it is presented'
assert not (exists(text_embeds) and text_embeds.shape[-1] != self.text_embed_dim), f'invalid text embedding dimension being passed in (should be {self.text_embed_dim})'
assert not (exists(inpaint_images) ^ exists(inpaint_masks)), 'inpaint images and masks must be both passed in to do inpainting'
outputs = []
is_cuda = next(self.parameters()).is_cuda
device = next(self.parameters()).device
lowres_sample_noise_level = default(lowres_sample_noise_level, self.lowres_sample_noise_level)
num_unets = len(self.unets)
cond_scale = cast_tuple(cond_scale, num_unets)
# handle video and frame dimension
if self.is_video and exists(inpaint_images):
video_frames = inpaint_images.shape[2]
if inpaint_masks.ndim == 3:
inpaint_masks = repeat(inpaint_masks, 'b h w -> b f h w', f = video_frames)
assert inpaint_masks.shape[1] == video_frames
assert not (self.is_video and not exists(video_frames)), 'video_frames must be passed in on sample time if training on video'
# determine the frame dimensions, if needed
all_frame_dims = calc_all_frame_dims(self.temporal_downsample_factor, video_frames)
# initializing with an image or video
init_images = cast_tuple(init_images, num_unets)
init_images = [maybe(self.normalize_img)(init_image) for init_image in init_images]
skip_steps = cast_tuple(skip_steps, num_unets)
sigma_min = cast_tuple(sigma_min, num_unets)
sigma_max = cast_tuple(sigma_max, num_unets)
# handle starting at a unet greater than 1, for training only-upscaler training
if start_at_unet_number > 1:
assert start_at_unet_number <= num_unets, 'must start a unet that is less than the total number of unets'
assert not exists(stop_at_unet_number) or start_at_unet_number <= stop_at_unet_number
assert exists(start_image_or_video), 'starting image or video must be supplied if only doing upscaling'
prev_image_size = self.image_sizes[start_at_unet_number - 2]
img = self.resize_to(start_image_or_video, prev_image_size)
# go through each unet in cascade
for unet_number, unet, channel, image_size, frame_dims, unet_hparam, dynamic_threshold, unet_cond_scale, unet_init_images, unet_skip_steps, unet_sigma_min, unet_sigma_max in tqdm(zip(range(1, num_unets + 1), self.unets, self.sample_channels, self.image_sizes, all_frame_dims, self.hparams, self.dynamic_thresholding, cond_scale, init_images, skip_steps, sigma_min, sigma_max), disable = not use_tqdm):
if unet_number < start_at_unet_number:
continue
assert not isinstance(unet, NullUnet), 'cannot sample from null unet'
context = self.one_unet_in_gpu(unet = unet) if is_cuda and use_one_unet_in_gpu else nullcontext()
with context:
lowres_cond_img = lowres_noise_times = None
shape = (batch_size, channel, *frame_dims, image_size, image_size)
resize_kwargs = dict()
video_kwargs = dict()
if self.is_video:
resize_kwargs = dict(target_frames = frame_dims[0])
video_kwargs = dict(
cond_video_frames = cond_video_frames,
post_cond_video_frames = post_cond_video_frames
)
video_kwargs = compact(video_kwargs)
# handle video conditioning frames
if self.is_video and self.resize_cond_video_frames:
downsample_scale = self.temporal_downsample_factor[unet_number - 1]
temporal_downsample_fn = partial(scale_video_time, downsample_scale = downsample_scale)
video_kwargs = maybe_transform_dict_key(video_kwargs, 'cond_video_frames', temporal_downsample_fn)
video_kwargs = maybe_transform_dict_key(video_kwargs, 'post_cond_video_frames', temporal_downsample_fn)
# low resolution conditioning
if unet.lowres_cond:
lowres_noise_times = self.lowres_noise_schedule.get_times(batch_size, lowres_sample_noise_level, device = device)
lowres_cond_img = self.resize_to(img, image_size, **resize_kwargs)
lowres_cond_img = self.normalize_img(lowres_cond_img)
lowres_cond_img, *_ = self.lowres_noise_schedule.q_sample(x_start = lowres_cond_img, t = lowres_noise_times, noise = torch.randn_like(lowres_cond_img))
if exists(unet_init_images):
unet_init_images = self.resize_to(unet_init_images, image_size, **resize_kwargs)
shape = (batch_size, self.channels, *frame_dims, image_size, image_size)
img = self.one_unet_sample(
unet,
shape,
unet_number = unet_number,
text_embeds = text_embeds,
text_mask = text_masks,
cond_images = cond_images,
inpaint_images = inpaint_images,
inpaint_masks = inpaint_masks,
inpaint_resample_times = inpaint_resample_times,
init_images = unet_init_images,
skip_steps = unet_skip_steps,
sigma_min = unet_sigma_min,
sigma_max = unet_sigma_max,
cond_scale = unet_cond_scale,
lowres_cond_img = lowres_cond_img,
lowres_noise_times = lowres_noise_times,
dynamic_threshold = dynamic_threshold,
use_tqdm = use_tqdm,
**video_kwargs
)
outputs.append(img)
if exists(stop_at_unet_number) and stop_at_unet_number == unet_number:
break
output_index = -1 if not return_all_unet_outputs else slice(None) # either return last unet output or all unet outputs
if not return_pil_images:
return outputs[output_index]
if not return_all_unet_outputs:
outputs = outputs[-1:]
assert not self.is_video, 'automatically converting video tensor to video file for saving is not built yet'
pil_images = list(map(lambda img: list(map(T.ToPILImage(), img.unbind(dim = 0))), outputs))
return pil_images[output_index] # now you have a bunch of pillow images you can just .save(/where/ever/you/want.png)
# training
def loss_weight(self, sigma_data, sigma):
return (sigma ** 2 + sigma_data ** 2) * (sigma * sigma_data) ** -2
def noise_distribution(self, P_mean, P_std, batch_size):
return (P_mean + P_std * torch.randn((batch_size,), device = self.device)).exp()
def forward(
self,
images, # rename to images or video
unet: Union[Unet, Unet3D, NullUnet, DistributedDataParallel] = None,
texts: List[str] = None,
text_embeds = None,
text_masks = None,
unet_number = None,
cond_images = None,
**kwargs
):
if self.is_video and images.ndim == 4:
images = rearrange(images, 'b c h w -> b c 1 h w')
kwargs.update(ignore_time = True)
assert images.shape[-1] == images.shape[-2], f'the images you pass in must be a square, but received dimensions of {images.shape[2]}, {images.shape[-1]}'
assert not (len(self.unets) > 1 and not exists(unet_number)), f'you must specify which unet you want trained, from a range of 1 to {len(self.unets)}, if you are training cascading DDPM (multiple unets)'
unet_number = default(unet_number, 1)
assert not exists(self.only_train_unet_number) or self.only_train_unet_number == unet_number, 'you can only train on unet #{self.only_train_unet_number}'
images = cast_uint8_images_to_float(images)
cond_images = maybe(cast_uint8_images_to_float)(cond_images)
assert images.dtype == torch.float, f'images tensor needs to be floats but {images.dtype} dtype found instead'
unet_index = unet_number - 1
unet = default(unet, lambda: self.get_unet(unet_number))
assert not isinstance(unet, NullUnet), 'null unet cannot and should not be trained'
target_image_size = self.image_sizes[unet_index]
random_crop_size = self.random_crop_sizes[unet_index]
prev_image_size = self.image_sizes[unet_index - 1] if unet_index > 0 else None
hp = self.hparams[unet_index]
batch_size, c, *_, h, w, device, is_video = *images.shape, images.device, (images.ndim == 5)
frames = images.shape[2] if is_video else None
all_frame_dims = tuple(safe_get_tuple_index(el, 0) for el in calc_all_frame_dims(self.temporal_downsample_factor, frames))
ignore_time = kwargs.get('ignore_time', False)
target_frame_size = all_frame_dims[unet_index] if is_video and not ignore_time else None
prev_frame_size = all_frame_dims[unet_index - 1] if is_video and not ignore_time and unet_index > 0 else None
frames_to_resize_kwargs = lambda frames: dict(target_frames = frames) if exists(frames) else dict()
assert images.shape[1] == self.channels
assert h >= target_image_size and w >= target_image_size
if exists(texts) and not exists(text_embeds) and not self.unconditional:
assert all([*map(len, texts)]), 'text cannot be empty'
assert len(texts) == len(images), 'number of text captions does not match up with the number of images given'
with autocast(enabled = False):
text_embeds, text_masks = self.encode_text(texts, return_attn_mask = True)
text_embeds, text_masks = map(lambda t: t.to(images.device), (text_embeds, text_masks))
if not self.unconditional:
text_masks = default(text_masks, lambda: torch.any(text_embeds != 0., dim = -1))
assert not (self.condition_on_text and not exists(text_embeds)), 'text or text encodings must be passed into decoder if specified'
assert not (not self.condition_on_text and exists(text_embeds)), 'decoder specified not to be conditioned on text, yet it is presented'
assert not (exists(text_embeds) and text_embeds.shape[-1] != self.text_embed_dim), f'invalid text embedding dimension being passed in (should be {self.text_embed_dim})'
# handle video conditioning frames
if self.is_video and self.resize_cond_video_frames:
downsample_scale = self.temporal_downsample_factor[unet_index]
temporal_downsample_fn = partial(scale_video_time, downsample_scale = downsample_scale)
kwargs = maybe_transform_dict_key(kwargs, 'cond_video_frames', temporal_downsample_fn)
kwargs = maybe_transform_dict_key(kwargs, 'post_cond_video_frames', temporal_downsample_fn)
# low resolution conditioning
lowres_cond_img = lowres_aug_times = None
if exists(prev_image_size):
lowres_cond_img = self.resize_to(images, prev_image_size, **frames_to_resize_kwargs(prev_frame_size), clamp_range = self.input_image_range)
lowres_cond_img = self.resize_to(lowres_cond_img, target_image_size, **frames_to_resize_kwargs(target_frame_size), clamp_range = self.input_image_range)
if self.per_sample_random_aug_noise_level:
lowres_aug_times = self.lowres_noise_schedule.sample_random_times(batch_size, device = device)
else:
lowres_aug_time = self.lowres_noise_schedule.sample_random_times(1, device = device)
lowres_aug_times = repeat(lowres_aug_time, '1 -> b', b = batch_size)
images = self.resize_to(images, target_image_size, **frames_to_resize_kwargs(target_frame_size))
# normalize to [-1, 1]
images = self.normalize_img(images)
lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
# random cropping during training
# for upsamplers
if exists(random_crop_size):
aug = K.RandomCrop((random_crop_size, random_crop_size), p = 1.)
if is_video:
images, lowres_cond_img = map(lambda t: rearrange(t, 'b c f h w -> (b f) c h w'), (images, lowres_cond_img))
# make sure low res conditioner and image both get augmented the same way
# detailed https://kornia.readthedocs.io/en/latest/augmentation.module.html?highlight=randomcrop#kornia.augmentation.RandomCrop
images = aug(images)
lowres_cond_img = aug(lowres_cond_img, params = aug._params)
if is_video:
images, lowres_cond_img = map(lambda t: rearrange(t, '(b f) c h w -> b c f h w', f = frames), (images, lowres_cond_img))
# noise the lowres conditioning image
# at sample time, they then fix the noise level of 0.1 - 0.3
lowres_cond_img_noisy = None
if exists(lowres_cond_img):
lowres_cond_img_noisy, *_ = self.lowres_noise_schedule.q_sample(x_start = lowres_cond_img, t = lowres_aug_times, noise = torch.randn_like(lowres_cond_img))
# get the sigmas
sigmas = self.noise_distribution(hp.P_mean, hp.P_std, batch_size)
padded_sigmas = self.right_pad_dims_to_datatype(sigmas)
# noise
noise = torch.randn_like(images)
noised_images = images + padded_sigmas * noise # alphas are 1. in the paper
# unet kwargs
unet_kwargs = dict(
sigma_data = hp.sigma_data,
text_embeds = text_embeds,
text_mask = text_masks,
cond_images = cond_images,
lowres_noise_times = self.lowres_noise_schedule.get_condition(lowres_aug_times),
lowres_cond_img = lowres_cond_img_noisy,
cond_drop_prob = self.cond_drop_prob,
**kwargs
)
# self conditioning - https://arxiv.org/abs/2208.04202 - training will be 25% slower
# Because 'unet' can be an instance of DistributedDataParallel coming from the
# ImagenTrainer.unet_being_trained when invoking ImagenTrainer.forward(), we need to
# access the member 'module' of the wrapped unet instance.
self_cond = unet.module.self_cond if isinstance(unet, DistributedDataParallel) else unet.self_cond
if self_cond and random() < 0.5:
with torch.no_grad():
pred_x0 = self.preconditioned_network_forward(
unet.forward,
noised_images,
sigmas,
**unet_kwargs
).detach()
unet_kwargs = {**unet_kwargs, 'self_cond': pred_x0}
# get prediction
denoised_images = self.preconditioned_network_forward(
unet.forward,
noised_images,
sigmas,
**unet_kwargs
)
# losses
losses = F.mse_loss(denoised_images, images, reduction = 'none')
losses = reduce(losses, 'b ... -> b', 'mean')
# loss weighting
losses = losses * self.loss_weight(hp.sigma_data, sigmas)
# return average loss
return losses.mean()
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