1444 lines
66 KiB
Python
1444 lines
66 KiB
Python
# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
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# Original filename: ldm/models/diffusion/ddpm.py
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# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
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# Some models such as LDSR require VQ to work correctly
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# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
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import torch
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import torch.nn as nn
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import numpy as np
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import pytorch_lightning as pl
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from torch.optim.lr_scheduler import LambdaLR
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from einops import rearrange, repeat
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from contextlib import contextmanager
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from functools import partial
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from tqdm import tqdm
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from torchvision.utils import make_grid
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
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from ldm.modules.ema import LitEma
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from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
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from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
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from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
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from ldm.models.diffusion.ddim import DDIMSampler
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import ldm.models.diffusion.ddpm
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__conditioning_keys__ = {'concat': 'c_concat',
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'crossattn': 'c_crossattn',
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'adm': 'y'}
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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def uniform_on_device(r1, r2, shape, device):
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return (r1 - r2) * torch.rand(*shape, device=device) + r2
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class DDPMV1(pl.LightningModule):
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# classic DDPM with Gaussian diffusion, in image space
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def __init__(self,
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unet_config,
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timesteps=1000,
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beta_schedule="linear",
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loss_type="l2",
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ckpt_path=None,
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ignore_keys=None,
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load_only_unet=False,
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monitor="val/loss",
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use_ema=True,
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first_stage_key="image",
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image_size=256,
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channels=3,
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log_every_t=100,
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clip_denoised=True,
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linear_start=1e-4,
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linear_end=2e-2,
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cosine_s=8e-3,
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given_betas=None,
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original_elbo_weight=0.,
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v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.,
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conditioning_key=None,
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parameterization="eps", # all assuming fixed variance schedules
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scheduler_config=None,
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use_positional_encodings=False,
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learn_logvar=False,
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logvar_init=0.,
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):
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super().__init__()
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assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
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self.parameterization = parameterization
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print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
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self.cond_stage_model = None
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self.clip_denoised = clip_denoised
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self.log_every_t = log_every_t
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self.first_stage_key = first_stage_key
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self.image_size = image_size # try conv?
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self.channels = channels
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self.use_positional_encodings = use_positional_encodings
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self.model = DiffusionWrapperV1(unet_config, conditioning_key)
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count_params(self.model, verbose=True)
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self.use_ema = use_ema
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if self.use_ema:
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self.model_ema = LitEma(self.model)
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print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
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self.use_scheduler = scheduler_config is not None
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if self.use_scheduler:
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self.scheduler_config = scheduler_config
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self.v_posterior = v_posterior
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self.original_elbo_weight = original_elbo_weight
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self.l_simple_weight = l_simple_weight
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if monitor is not None:
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self.monitor = monitor
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
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self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
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linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
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self.loss_type = loss_type
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self.learn_logvar = learn_logvar
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self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
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if self.learn_logvar:
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self.logvar = nn.Parameter(self.logvar, requires_grad=True)
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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if exists(given_betas):
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betas = given_betas
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else:
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
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cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.linear_start = linear_start
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self.linear_end = linear_end
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assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = partial(torch.tensor, dtype=torch.float32)
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self.register_buffer('betas', to_torch(betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
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1. - alphas_cumprod) + self.v_posterior * betas
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer('posterior_variance', to_torch(posterior_variance))
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
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self.register_buffer('posterior_mean_coef1', to_torch(
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betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
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self.register_buffer('posterior_mean_coef2', to_torch(
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(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
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if self.parameterization == "eps":
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lvlb_weights = self.betas ** 2 / (
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2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
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elif self.parameterization == "x0":
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lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
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else:
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raise NotImplementedError("mu not supported")
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# TODO how to choose this term
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lvlb_weights[0] = lvlb_weights[1]
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self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
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assert not torch.isnan(self.lvlb_weights).all()
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@contextmanager
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def ema_scope(self, context=None):
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if self.use_ema:
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self.model_ema.store(self.model.parameters())
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self.model_ema.copy_to(self.model)
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if context is not None:
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print(f"{context}: Switched to EMA weights")
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try:
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yield None
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finally:
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if self.use_ema:
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self.model_ema.restore(self.model.parameters())
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if context is not None:
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print(f"{context}: Restored training weights")
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def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
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sd = torch.load(path, map_location="cpu")
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if "state_dict" in list(sd.keys()):
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sd = sd["state_dict"]
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keys = list(sd.keys())
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for k in keys:
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for ik in ignore_keys or []:
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if k.startswith(ik):
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print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
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sd, strict=False)
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print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
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if len(missing) > 0:
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print(f"Missing Keys: {missing}")
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if len(unexpected) > 0:
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print(f"Unexpected Keys: {unexpected}")
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def q_mean_variance(self, x_start, t):
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"""
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Get the distribution q(x_t | x_0).
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:param x_start: the [N x C x ...] tensor of noiseless inputs.
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:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
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:return: A tuple (mean, variance, log_variance), all of x_start's shape.
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"""
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mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
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variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
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log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
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return mean, variance, log_variance
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def predict_start_from_noise(self, x_t, t, noise):
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return (
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extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
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extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
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)
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def q_posterior(self, x_start, x_t, t):
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posterior_mean = (
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extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
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extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
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)
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posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
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posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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def p_mean_variance(self, x, t, clip_denoised: bool):
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model_out = self.model(x, t)
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if self.parameterization == "eps":
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x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
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elif self.parameterization == "x0":
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x_recon = model_out
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if clip_denoised:
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x_recon.clamp_(-1., 1.)
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model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
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return model_mean, posterior_variance, posterior_log_variance
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@torch.no_grad()
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def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
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b, *_, device = *x.shape, x.device
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model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
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noise = noise_like(x.shape, device, repeat_noise)
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# no noise when t == 0
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
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return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
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@torch.no_grad()
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def p_sample_loop(self, shape, return_intermediates=False):
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device = self.betas.device
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b = shape[0]
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img = torch.randn(shape, device=device)
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intermediates = [img]
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for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
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img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
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clip_denoised=self.clip_denoised)
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if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
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intermediates.append(img)
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if return_intermediates:
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return img, intermediates
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return img
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@torch.no_grad()
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def sample(self, batch_size=16, return_intermediates=False):
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image_size = self.image_size
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channels = self.channels
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return self.p_sample_loop((batch_size, channels, image_size, image_size),
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return_intermediates=return_intermediates)
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def q_sample(self, x_start, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
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def get_loss(self, pred, target, mean=True):
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if self.loss_type == 'l1':
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loss = (target - pred).abs()
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if mean:
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loss = loss.mean()
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elif self.loss_type == 'l2':
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if mean:
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loss = torch.nn.functional.mse_loss(target, pred)
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else:
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loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
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else:
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raise NotImplementedError("unknown loss type '{loss_type}'")
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return loss
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def p_losses(self, x_start, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
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model_out = self.model(x_noisy, t)
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loss_dict = {}
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if self.parameterization == "eps":
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target = noise
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elif self.parameterization == "x0":
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target = x_start
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else:
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raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
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loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
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log_prefix = 'train' if self.training else 'val'
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loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
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loss_simple = loss.mean() * self.l_simple_weight
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loss_vlb = (self.lvlb_weights[t] * loss).mean()
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loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
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loss = loss_simple + self.original_elbo_weight * loss_vlb
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loss_dict.update({f'{log_prefix}/loss': loss})
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return loss, loss_dict
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def forward(self, x, *args, **kwargs):
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# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
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# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
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t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
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return self.p_losses(x, t, *args, **kwargs)
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def get_input(self, batch, k):
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x = batch[k]
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if len(x.shape) == 3:
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x = x[..., None]
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x = rearrange(x, 'b h w c -> b c h w')
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x = x.to(memory_format=torch.contiguous_format).float()
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return x
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def shared_step(self, batch):
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x = self.get_input(batch, self.first_stage_key)
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loss, loss_dict = self(x)
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return loss, loss_dict
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def training_step(self, batch, batch_idx):
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loss, loss_dict = self.shared_step(batch)
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self.log_dict(loss_dict, prog_bar=True,
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logger=True, on_step=True, on_epoch=True)
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self.log("global_step", self.global_step,
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prog_bar=True, logger=True, on_step=True, on_epoch=False)
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if self.use_scheduler:
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lr = self.optimizers().param_groups[0]['lr']
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self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
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return loss
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@torch.no_grad()
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def validation_step(self, batch, batch_idx):
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_, loss_dict_no_ema = self.shared_step(batch)
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with self.ema_scope():
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_, loss_dict_ema = self.shared_step(batch)
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loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
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self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
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self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
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def on_train_batch_end(self, *args, **kwargs):
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if self.use_ema:
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self.model_ema(self.model)
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def _get_rows_from_list(self, samples):
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n_imgs_per_row = len(samples)
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denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
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denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
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denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
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return denoise_grid
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@torch.no_grad()
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def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
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log = {}
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x = self.get_input(batch, self.first_stage_key)
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N = min(x.shape[0], N)
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n_row = min(x.shape[0], n_row)
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x = x.to(self.device)[:N]
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log["inputs"] = x
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# get diffusion row
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diffusion_row = []
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x_start = x[:n_row]
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for t in range(self.num_timesteps):
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if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
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t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
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t = t.to(self.device).long()
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noise = torch.randn_like(x_start)
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x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
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diffusion_row.append(x_noisy)
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log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
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if sample:
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# get denoise row
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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 LatentDiffusionV1(DDPMV1):
|
|
"""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,
|
|
*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
|
|
if conditioning_key is None:
|
|
conditioning_key = 'concat' if concat_mode else 'crossattn'
|
|
if cond_stage_config == '__is_unconditional__':
|
|
conditioning_key = None
|
|
ckpt_path = kwargs.pop("ckpt_path", None)
|
|
ignore_keys = kwargs.pop("ignore_keys", [])
|
|
super().__init__(*args, conditioning_key=conditioning_key, **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 Exception:
|
|
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.cond_stage_forward = cond_stage_forward
|
|
self.clip_denoised = False
|
|
self.bbox_tokenizer = None
|
|
|
|
self.restarted_from_ckpt = False
|
|
if ckpt_path is not None:
|
|
self.init_from_ckpt(ckpt_path, ignore_keys)
|
|
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
|
|
|
|
@rank_zero_only
|
|
@torch.no_grad()
|
|
def on_train_batch_start(self, batch, batch_idx, dataloader_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
|
|
print("### 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())
|
|
print(f"setting self.scale_factor to {self.scale_factor}")
|
|
print("### 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__":
|
|
print("Using first stage also as cond stage.")
|
|
self.cond_stage_model = self.first_stage_model
|
|
elif config == "__is_unconditional__":
|
|
print(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
|
|
|
|
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, return_first_stage_outputs=False, force_c_encode=False,
|
|
cond_key=None, return_original_cond=False, bs=None):
|
|
x = super().get_input(batch, k)
|
|
if bs is not None:
|
|
x = x[:bs]
|
|
x = x.to(self.device)
|
|
encoder_posterior = self.encode_first_stage(x)
|
|
z = self.get_first_stage_encoding(encoder_posterior).detach()
|
|
|
|
if self.model.conditioning_key is not None:
|
|
if cond_key is None:
|
|
cond_key = self.cond_stage_key
|
|
if cond_key != self.first_stage_key:
|
|
if cond_key in ['caption', 'coordinates_bbox']:
|
|
xc = batch[cond_key]
|
|
elif cond_key == 'class_label':
|
|
xc = batch
|
|
else:
|
|
xc = super().get_input(batch, cond_key).to(self.device)
|
|
else:
|
|
xc = x
|
|
if not self.cond_stage_trainable or force_c_encode:
|
|
if isinstance(xc, dict) or isinstance(xc, list):
|
|
# import pudb; pudb.set_trace()
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
c = self.get_learned_conditioning(xc.to(self.device))
|
|
else:
|
|
c = xc
|
|
if bs is not None:
|
|
c = c[:bs]
|
|
|
|
if self.use_positional_encodings:
|
|
pos_x, pos_y = self.compute_latent_shifts(batch)
|
|
ckey = __conditioning_keys__[self.model.conditioning_key]
|
|
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
|
|
|
|
else:
|
|
c = None
|
|
xc = None
|
|
if self.use_positional_encodings:
|
|
pos_x, pos_y = self.compute_latent_shifts(batch)
|
|
c = {'pos_x': pos_x, 'pos_y': pos_y}
|
|
out = [z, c]
|
|
if return_first_stage_outputs:
|
|
xrec = self.decode_first_stage(z)
|
|
out.extend([x, xrec])
|
|
if return_original_cond:
|
|
out.append(xc)
|
|
return out
|
|
|
|
@torch.no_grad()
|
|
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
|
if predict_cids:
|
|
if z.dim() == 4:
|
|
z = torch.argmax(z.exp(), dim=1).long()
|
|
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
|
z = rearrange(z, 'b h w c -> b c h w').contiguous()
|
|
|
|
z = 1. / self.scale_factor * z
|
|
|
|
if hasattr(self, "split_input_params"):
|
|
if self.split_input_params["patch_distributed_vq"]:
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
uf = self.split_input_params["vqf"]
|
|
bs, nc, h, w = z.shape
|
|
if ks[0] > h or ks[1] > w:
|
|
ks = (min(ks[0], h), min(ks[1], w))
|
|
print("reducing Kernel")
|
|
|
|
if stride[0] > h or stride[1] > w:
|
|
stride = (min(stride[0], h), min(stride[1], w))
|
|
print("reducing stride")
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
|
|
|
|
z = unfold(z) # (bn, nc * prod(**ks), L)
|
|
# 1. Reshape to img shape
|
|
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
# 2. apply model loop over last dim
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
|
force_not_quantize=predict_cids or force_not_quantize)
|
|
for i in range(z.shape[-1])]
|
|
else:
|
|
|
|
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
|
|
for i in range(z.shape[-1])]
|
|
|
|
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
|
|
o = o * weighting
|
|
# Reverse 1. reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
decoded = fold(o)
|
|
decoded = decoded / normalization # norm is shape (1, 1, h, w)
|
|
return decoded
|
|
else:
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
|
else:
|
|
return self.first_stage_model.decode(z)
|
|
|
|
else:
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
|
else:
|
|
return self.first_stage_model.decode(z)
|
|
|
|
# same as above but without decorator
|
|
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
|
if predict_cids:
|
|
if z.dim() == 4:
|
|
z = torch.argmax(z.exp(), dim=1).long()
|
|
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
|
z = rearrange(z, 'b h w c -> b c h w').contiguous()
|
|
|
|
z = 1. / self.scale_factor * z
|
|
|
|
if hasattr(self, "split_input_params"):
|
|
if self.split_input_params["patch_distributed_vq"]:
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
uf = self.split_input_params["vqf"]
|
|
bs, nc, h, w = z.shape
|
|
if ks[0] > h or ks[1] > w:
|
|
ks = (min(ks[0], h), min(ks[1], w))
|
|
print("reducing Kernel")
|
|
|
|
if stride[0] > h or stride[1] > w:
|
|
stride = (min(stride[0], h), min(stride[1], w))
|
|
print("reducing stride")
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
|
|
|
|
z = unfold(z) # (bn, nc * prod(**ks), L)
|
|
# 1. Reshape to img shape
|
|
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
# 2. apply model loop over last dim
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
|
force_not_quantize=predict_cids or force_not_quantize)
|
|
for i in range(z.shape[-1])]
|
|
else:
|
|
|
|
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
|
|
for i in range(z.shape[-1])]
|
|
|
|
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
|
|
o = o * weighting
|
|
# Reverse 1. reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
decoded = fold(o)
|
|
decoded = decoded / normalization # norm is shape (1, 1, h, w)
|
|
return decoded
|
|
else:
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
|
else:
|
|
return self.first_stage_model.decode(z)
|
|
|
|
else:
|
|
if isinstance(self.first_stage_model, VQModelInterface):
|
|
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
|
else:
|
|
return self.first_stage_model.decode(z)
|
|
|
|
@torch.no_grad()
|
|
def encode_first_stage(self, x):
|
|
if hasattr(self, "split_input_params"):
|
|
if self.split_input_params["patch_distributed_vq"]:
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
df = self.split_input_params["vqf"]
|
|
self.split_input_params['original_image_size'] = x.shape[-2:]
|
|
bs, nc, h, w = x.shape
|
|
if ks[0] > h or ks[1] > w:
|
|
ks = (min(ks[0], h), min(ks[1], w))
|
|
print("reducing Kernel")
|
|
|
|
if stride[0] > h or stride[1] > w:
|
|
stride = (min(stride[0], h), min(stride[1], w))
|
|
print("reducing stride")
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
|
|
z = unfold(x) # (bn, nc * prod(**ks), L)
|
|
# Reshape to img shape
|
|
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
|
|
for i in range(z.shape[-1])]
|
|
|
|
o = torch.stack(output_list, axis=-1)
|
|
o = o * weighting
|
|
|
|
# Reverse reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
decoded = fold(o)
|
|
decoded = decoded / normalization
|
|
return decoded
|
|
|
|
else:
|
|
return self.first_stage_model.encode(x)
|
|
else:
|
|
return self.first_stage_model.encode(x)
|
|
|
|
def shared_step(self, batch, **kwargs):
|
|
x, c = self.get_input(batch, self.first_stage_key)
|
|
loss = self(x, c)
|
|
return loss
|
|
|
|
def forward(self, x, c, *args, **kwargs):
|
|
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
|
if self.model.conditioning_key is not None:
|
|
assert c is not None
|
|
if self.cond_stage_trainable:
|
|
c = self.get_learned_conditioning(c)
|
|
if self.shorten_cond_schedule: # TODO: drop this option
|
|
tc = self.cond_ids[t].to(self.device)
|
|
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
|
|
return self.p_losses(x, c, t, *args, **kwargs)
|
|
|
|
def apply_model(self, x_noisy, t, cond, return_ids=False):
|
|
|
|
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}
|
|
|
|
if hasattr(self, "split_input_params"):
|
|
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
|
assert not return_ids
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
|
|
h, w = x_noisy.shape[-2:]
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
|
|
|
|
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
|
|
# Reshape to img shape
|
|
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
|
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
|
|
|
|
if self.cond_stage_key in ["image", "LR_image", "segmentation",
|
|
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
|
|
c_key = next(iter(cond.keys())) # get key
|
|
c = next(iter(cond.values())) # get value
|
|
assert (len(c) == 1) # todo extend to list with more than one elem
|
|
c = c[0] # get element
|
|
|
|
c = unfold(c)
|
|
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
|
|
|
|
elif self.cond_stage_key == 'coordinates_bbox':
|
|
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
|
|
|
|
# assuming padding of unfold is always 0 and its dilation is always 1
|
|
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
|
|
full_img_h, full_img_w = self.split_input_params['original_image_size']
|
|
# as we are operating on latents, we need the factor from the original image size to the
|
|
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
|
|
num_downs = self.first_stage_model.encoder.num_resolutions - 1
|
|
rescale_latent = 2 ** (num_downs)
|
|
|
|
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
|
|
# need to rescale the tl patch coordinates to be in between (0,1)
|
|
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
|
|
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
|
|
for patch_nr in range(z.shape[-1])]
|
|
|
|
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
|
|
patch_limits = [(x_tl, y_tl,
|
|
rescale_latent * ks[0] / full_img_w,
|
|
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
|
|
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
|
|
|
|
# tokenize crop coordinates for the bounding boxes of the respective patches
|
|
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
|
|
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
|
|
print(patch_limits_tknzd[0].shape)
|
|
# cut tknzd crop position from conditioning
|
|
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
|
|
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
|
|
print(cut_cond.shape)
|
|
|
|
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
|
|
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
|
|
print(adapted_cond.shape)
|
|
adapted_cond = self.get_learned_conditioning(adapted_cond)
|
|
print(adapted_cond.shape)
|
|
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
|
|
print(adapted_cond.shape)
|
|
|
|
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
|
|
|
|
else:
|
|
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
|
|
|
|
# apply model by loop over crops
|
|
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
|
|
assert not isinstance(output_list[0],
|
|
tuple) # todo cant deal with multiple model outputs check this never happens
|
|
|
|
o = torch.stack(output_list, axis=-1)
|
|
o = o * weighting
|
|
# Reverse reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
x_recon = fold(o) / normalization
|
|
|
|
else:
|
|
x_recon = self.model(x_noisy, t, **cond)
|
|
|
|
if isinstance(x_recon, tuple) and not return_ids:
|
|
return x_recon[0]
|
|
else:
|
|
return x_recon
|
|
|
|
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
|
|
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
|
|
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
|
|
|
|
def _prior_bpd(self, x_start):
|
|
"""
|
|
Get the prior KL term for the variational lower-bound, measured in
|
|
bits-per-dim.
|
|
This term can't be optimized, as it only depends on the encoder.
|
|
:param x_start: the [N x C x ...] tensor of inputs.
|
|
:return: a batch of [N] KL values (in bits), one per batch element.
|
|
"""
|
|
batch_size = x_start.shape[0]
|
|
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
|
|
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
|
|
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
|
|
return mean_flat(kl_prior) / np.log(2.0)
|
|
|
|
def p_losses(self, x_start, cond, 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_output = self.apply_model(x_noisy, t, cond)
|
|
|
|
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()
|
|
|
|
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
|
|
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
|
|
|
|
logvar_t = self.logvar[t].to(self.device)
|
|
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()
|
|
|
|
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
|
|
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 p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
|
|
return_x0=False, score_corrector=None, corrector_kwargs=None):
|
|
t_in = t
|
|
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
|
|
|
|
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 return_codebook_ids:
|
|
model_out, logits = model_out
|
|
|
|
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.)
|
|
if quantize_denoised:
|
|
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
|
|
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
|
if return_codebook_ids:
|
|
return model_mean, posterior_variance, posterior_log_variance, logits
|
|
elif 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_codebook_ids=False, quantize_denoised=False, return_x0=False,
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
|
|
b, *_, device = *x.shape, x.device
|
|
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
|
|
return_codebook_ids=return_codebook_ids,
|
|
quantize_denoised=quantize_denoised,
|
|
return_x0=return_x0,
|
|
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
|
if return_codebook_ids:
|
|
raise DeprecationWarning("Support dropped.")
|
|
model_mean, _, model_log_variance, logits = outputs
|
|
elif 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_codebook_ids:
|
|
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=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 progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
|
|
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
|
|
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
|
|
log_every_t=None):
|
|
if not log_every_t:
|
|
log_every_t = self.log_every_t
|
|
timesteps = self.num_timesteps
|
|
if batch_size is not None:
|
|
b = batch_size if batch_size is not None else shape[0]
|
|
shape = [batch_size] + list(shape)
|
|
else:
|
|
b = batch_size = shape[0]
|
|
if x_T is None:
|
|
img = torch.randn(shape, device=self.device)
|
|
else:
|
|
img = x_T
|
|
intermediates = []
|
|
if cond is not None:
|
|
if isinstance(cond, dict):
|
|
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
|
[x[:batch_size] for x in cond[key]] for key in cond}
|
|
else:
|
|
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
|
|
|
if start_T is not None:
|
|
timesteps = min(timesteps, start_T)
|
|
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
|
|
total=timesteps) if verbose else reversed(
|
|
range(0, timesteps))
|
|
if type(temperature) == float:
|
|
temperature = [temperature] * timesteps
|
|
|
|
for i in iterator:
|
|
ts = torch.full((b,), i, device=self.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, x0_partial = self.p_sample(img, cond, ts,
|
|
clip_denoised=self.clip_denoised,
|
|
quantize_denoised=quantize_denoised, return_x0=True,
|
|
temperature=temperature[i], noise_dropout=noise_dropout,
|
|
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
|
if mask is not None:
|
|
assert x0 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(x0_partial)
|
|
if callback:
|
|
callback(i)
|
|
if img_callback:
|
|
img_callback(img, i)
|
|
return img, intermediates
|
|
|
|
@torch.no_grad()
|
|
def p_sample_loop(self, cond, shape, return_intermediates=False,
|
|
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
|
|
mask=None, x0=None, img_callback=None, start_T=None,
|
|
log_every_t=None):
|
|
|
|
if not log_every_t:
|
|
log_every_t = self.log_every_t
|
|
device = self.betas.device
|
|
b = shape[0]
|
|
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,
|
|
quantize_denoised=quantize_denoised)
|
|
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, quantize_denoised=False,
|
|
mask=None, x0=None, shape=None,**kwargs):
|
|
if shape is None:
|
|
shape = (batch_size, self.channels, self.image_size, 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
|
|
[x[:batch_size] for x in 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, quantize_denoised=quantize_denoised,
|
|
mask=mask, x0=x0)
|
|
|
|
@torch.no_grad()
|
|
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
|
|
|
|
if ddim:
|
|
ddim_sampler = DDIMSampler(self)
|
|
shape = (self.channels, self.image_size, self.image_size)
|
|
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
|
|
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
|
|
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
|
|
plot_diffusion_rows=True, **kwargs):
|
|
|
|
use_ddim = ddim_steps is not None
|
|
|
|
log = {}
|
|
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
|
|
return_first_stage_outputs=True,
|
|
force_c_encode=True,
|
|
return_original_cond=True,
|
|
bs=N)
|
|
N = min(x.shape[0], N)
|
|
n_row = min(x.shape[0], n_row)
|
|
log["inputs"] = x
|
|
log["reconstruction"] = xrec
|
|
if self.model.conditioning_key is not None:
|
|
if hasattr(self.cond_stage_model, "decode"):
|
|
xc = self.cond_stage_model.decode(c)
|
|
log["conditioning"] = xc
|
|
elif self.cond_stage_key in ["caption"]:
|
|
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
|
|
log["conditioning"] = xc
|
|
elif self.cond_stage_key == 'class_label':
|
|
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
|
|
log['conditioning'] = xc
|
|
elif isimage(xc):
|
|
log["conditioning"] = xc
|
|
if ismap(xc):
|
|
log["original_conditioning"] = self.to_rgb(xc)
|
|
|
|
if plot_diffusion_rows:
|
|
# get diffusion row
|
|
diffusion_row = []
|
|
z_start = z[: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(z_start)
|
|
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
|
|
diffusion_row.append(self.decode_first_stage(z_noisy))
|
|
|
|
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
|
|
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
|
|
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
|
|
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
|
|
log["diffusion_row"] = diffusion_grid
|
|
|
|
if sample:
|
|
# get denoise row
|
|
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)
|
|
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
|
|
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
|
|
|
|
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
|
|
self.first_stage_model, IdentityFirstStage):
|
|
# also display when quantizing x0 while sampling
|
|
with self.ema_scope("Plotting Quantized Denoised"):
|
|
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
|
|
ddim_steps=ddim_steps,eta=ddim_eta,
|
|
quantize_denoised=True)
|
|
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
|
|
# quantize_denoised=True)
|
|
x_samples = self.decode_first_stage(samples.to(self.device))
|
|
log["samples_x0_quantized"] = x_samples
|
|
|
|
if inpaint:
|
|
# make a simple center square
|
|
h, w = z.shape[2], z.shape[3]
|
|
mask = torch.ones(N, h, w).to(self.device)
|
|
# zeros will be filled in
|
|
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
|
|
mask = mask[:, None, ...]
|
|
with self.ema_scope("Plotting Inpaint"):
|
|
|
|
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
|
|
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
|
x_samples = self.decode_first_stage(samples.to(self.device))
|
|
log["samples_inpainting"] = x_samples
|
|
log["mask"] = mask
|
|
|
|
# outpaint
|
|
with self.ema_scope("Plotting Outpaint"):
|
|
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
|
|
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
|
x_samples = self.decode_first_stage(samples.to(self.device))
|
|
log["samples_outpainting"] = x_samples
|
|
|
|
if plot_progressive_rows:
|
|
with self.ema_scope("Plotting Progressives"):
|
|
img, progressives = self.progressive_denoising(c,
|
|
shape=(self.channels, self.image_size, self.image_size),
|
|
batch_size=N)
|
|
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
|
|
log["progressive_row"] = prog_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.cond_stage_trainable:
|
|
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
|
|
params = params + list(self.cond_stage_model.parameters())
|
|
if self.learn_logvar:
|
|
print('Diffusion model optimizing logvar')
|
|
params.append(self.logvar)
|
|
opt = torch.optim.AdamW(params, lr=lr)
|
|
if self.use_scheduler:
|
|
assert 'target' in self.scheduler_config
|
|
scheduler = instantiate_from_config(self.scheduler_config)
|
|
|
|
print("Setting up LambdaLR scheduler...")
|
|
scheduler = [
|
|
{
|
|
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
|
|
'interval': 'step',
|
|
'frequency': 1
|
|
}]
|
|
return [opt], scheduler
|
|
return opt
|
|
|
|
@torch.no_grad()
|
|
def to_rgb(self, x):
|
|
x = x.float()
|
|
if not hasattr(self, "colorize"):
|
|
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
|
|
x = nn.functional.conv2d(x, weight=self.colorize)
|
|
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
|
|
return x
|
|
|
|
|
|
class DiffusionWrapperV1(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
|
|
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
|
|
|
|
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
|
|
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)
|
|
elif self.conditioning_key == 'crossattn':
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(x, t, context=cc)
|
|
elif self.conditioning_key == 'hybrid':
|
|
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 == 'adm':
|
|
cc = c_crossattn[0]
|
|
out = self.diffusion_model(x, t, y=cc)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
return out
|
|
|
|
|
|
class Layout2ImgDiffusionV1(LatentDiffusionV1):
|
|
# TODO: move all layout-specific hacks to this class
|
|
def __init__(self, cond_stage_key, *args, **kwargs):
|
|
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
|
|
super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
|
|
|
|
def log_images(self, batch, N=8, *args, **kwargs):
|
|
logs = super().log_images(*args, batch=batch, N=N, **kwargs)
|
|
|
|
key = 'train' if self.training else 'validation'
|
|
dset = self.trainer.datamodule.datasets[key]
|
|
mapper = dset.conditional_builders[self.cond_stage_key]
|
|
|
|
bbox_imgs = []
|
|
map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
|
|
for tknzd_bbox in batch[self.cond_stage_key][:N]:
|
|
bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
|
|
bbox_imgs.append(bboximg)
|
|
|
|
cond_img = torch.stack(bbox_imgs, dim=0)
|
|
logs['bbox_image'] = cond_img
|
|
return logs
|
|
|
|
ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
|
|
ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
|
|
ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
|
|
ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
|