801 lines
35 KiB
Python
801 lines
35 KiB
Python
"""
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wild mixture of
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https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
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https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
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https://github.com/CompVis/taming-transformers
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-- merci
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"""
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from functools import partial
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from contextlib import contextmanager
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import numpy as np
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from tqdm import tqdm
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from einops import rearrange, repeat
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import logging
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mainlogger = logging.getLogger('mainlogger')
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import torch
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import torch.nn as nn
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from torchvision.utils import make_grid
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import pytorch_lightning as pl
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from ...utils.utils import instantiate_from_config
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from ...lvdm.ema import LitEma
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from ...lvdm.distributions import DiagonalGaussianDistribution
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from ...lvdm.models.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr
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from ...lvdm.basics import disabled_train
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from ...lvdm.common import (
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extract_into_tensor,
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noise_like,
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exists,
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default
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)
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import math
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from ...lvdm.models.autoencoder_dualref import VideoDecoder
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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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import comfy.model_management as mm
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device = mm.get_torch_device()
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class DDPM(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=[],
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load_only_unet=False,
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monitor=None,
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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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rescale_betas_zero_snr=False,
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):
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super().__init__()
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assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
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self.parameterization = parameterization
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mainlogger.info(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.channels = channels
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self.temporal_length = unet_config.params.temporal_length
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self.image_size = image_size # try conv?
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if isinstance(self.image_size, int):
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self.image_size = [self.image_size, self.image_size]
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self.use_positional_encodings = use_positional_encodings
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self.model = DiffusionWrapper(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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self.rescale_betas_zero_snr = rescale_betas_zero_snr
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if self.use_ema:
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self.model_ema = LitEma(self.model)
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mainlogger.info(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, 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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if self.rescale_betas_zero_snr:
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betas = rescale_zero_terminal_snr(betas)
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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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if self.parameterization != 'v':
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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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else:
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self.register_buffer('sqrt_recip_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod)))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod)))
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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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elif self.parameterization == "v":
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lvlb_weights = torch.ones_like(self.betas ** 2 / (
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2 * self.posterior_variance * to_torch(alphas) * (1 - self.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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mainlogger.info(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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mainlogger.info(f"{context}: Restored training weights")
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def init_from_ckpt(self, path, ignore_keys=list(), 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:
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if k.startswith(ik):
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mainlogger.info("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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mainlogger.info(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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mainlogger.info(f"Missing Keys: {missing}")
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if len(unexpected) > 0:
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mainlogger.info(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 get_sqrt_alpha_t_bar(self,x_start,t):
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return extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
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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 predict_start_from_z_and_v(self, x_t, t, v):
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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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return (
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extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
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)
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def predict_eps_from_z_and_v(self, x_t, t, v):
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return (
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extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
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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_v(self, x, noise, t):
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return (
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extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
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)
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def get_input(self, batch, k):
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x = batch[k]
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x = x.to(memory_format=torch.contiguous_format).float()
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return x
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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 = dict()
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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 = list()
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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"):
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samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
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log["samples"] = samples
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log["denoise_row"] = self._get_rows_from_list(denoise_row)
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if return_keys:
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if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
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return log
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else:
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return {key: log[key] for key in return_keys}
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return log
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class LatentDiffusion(DDPM):
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"""main class"""
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def __init__(self,
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first_stage_config,
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#cond_stage_config,
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num_timesteps_cond=None,
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cond_stage_key="caption",
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cond_stage_trainable=False,
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cond_stage_forward=None,
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conditioning_key=None,
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uncond_prob=0.2,
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uncond_type="empty_seq",
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scale_factor=1.0,
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scale_by_std=False,
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encoder_type="2d",
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only_model=False,
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noise_strength=0,
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use_dynamic_rescale=False,
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base_scale=0.7,
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turning_step=400,
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loop_video=False,
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fps_condition_type='fs',
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perframe_ae=False,
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# added
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logdir=None,
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rand_cond_frame=False,
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en_and_decode_n_samples_a_time=None,
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control_scale=1.0,
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*args, **kwargs):
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self.num_timesteps_cond = default(num_timesteps_cond, 1)
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self.scale_by_std = scale_by_std
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assert self.num_timesteps_cond <= kwargs['timesteps']
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# for backwards compatibility after implementation of DiffusionWrapper
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ckpt_path = kwargs.pop("ckpt_path", None)
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ignore_keys = kwargs.pop("ignore_keys", [])
|
|
conditioning_key = default(conditioning_key, 'crossattn')
|
|
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
|
|
|
|
self.cond_stage_trainable = cond_stage_trainable
|
|
self.cond_stage_key = cond_stage_key
|
|
self.noise_strength = noise_strength
|
|
self.use_dynamic_rescale = use_dynamic_rescale
|
|
self.loop_video = loop_video
|
|
self.fps_condition_type = fps_condition_type
|
|
self.perframe_ae = perframe_ae
|
|
self.control_scale = control_scale
|
|
|
|
self.logdir = logdir
|
|
self.rand_cond_frame = rand_cond_frame
|
|
self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
|
|
|
|
try:
|
|
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
|
except:
|
|
self.num_downs = 0
|
|
if not scale_by_std:
|
|
self.scale_factor = scale_factor
|
|
else:
|
|
self.register_buffer('scale_factor', torch.tensor(scale_factor))
|
|
|
|
if use_dynamic_rescale:
|
|
scale_arr1 = np.linspace(1.0, base_scale, turning_step)
|
|
scale_arr2 = np.full(self.num_timesteps, base_scale)
|
|
scale_arr = np.concatenate((scale_arr1, scale_arr2))
|
|
to_torch = partial(torch.tensor, dtype=torch.float32)
|
|
self.register_buffer('scale_arr', to_torch(scale_arr))
|
|
|
|
self.instantiate_first_stage(first_stage_config)
|
|
#self.instantiate_cond_stage(cond_stage_config)
|
|
self.first_stage_config = first_stage_config
|
|
#self.cond_stage_config = cond_stage_config
|
|
self.clip_denoised = False
|
|
|
|
#self.cond_stage_forward = cond_stage_forward
|
|
self.encoder_type = encoder_type
|
|
assert(encoder_type in ["2d", "3d"])
|
|
self.uncond_prob = uncond_prob
|
|
self.classifier_free_guidance = True if uncond_prob > 0 else False
|
|
assert(uncond_type in ["zero_embed", "empty_seq"])
|
|
self.uncond_type = uncond_type
|
|
|
|
self.restarted_from_ckpt = False
|
|
if ckpt_path is not None:
|
|
self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
|
|
self.restarted_from_ckpt = True
|
|
|
|
|
|
def make_cond_schedule(self, ):
|
|
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
|
|
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
|
|
self.cond_ids[:self.num_timesteps_cond] = ids
|
|
|
|
def instantiate_first_stage(self, config):
|
|
model = instantiate_from_config(config)
|
|
self.first_stage_model = model.eval()
|
|
self.first_stage_model.train = disabled_train
|
|
for param in self.first_stage_model.parameters():
|
|
param.requires_grad = False
|
|
|
|
def instantiate_cond_stage(self, config):
|
|
if not self.cond_stage_trainable:
|
|
model = instantiate_from_config(config)
|
|
self.cond_stage_model = model.eval()
|
|
self.cond_stage_model.train = disabled_train
|
|
for param in self.cond_stage_model.parameters():
|
|
param.requires_grad = False
|
|
else:
|
|
model = instantiate_from_config(config)
|
|
self.cond_stage_model = model
|
|
|
|
def get_learned_conditioning(self, c):
|
|
if self.cond_stage_forward is None:
|
|
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
|
|
c = self.cond_stage_model.encode(c)
|
|
if isinstance(c, DiagonalGaussianDistribution):
|
|
c = c.mode()
|
|
else:
|
|
c = self.cond_stage_model(c)
|
|
else:
|
|
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
|
|
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
|
|
return c
|
|
|
|
def get_first_stage_encoding(self, encoder_posterior, noise=None):
|
|
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
|
|
z = encoder_posterior.sample(noise=noise)
|
|
elif isinstance(encoder_posterior, torch.Tensor):
|
|
z = encoder_posterior
|
|
else:
|
|
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
|
|
return self.scale_factor * z
|
|
|
|
@torch.no_grad()
|
|
def encode_first_stage(self, x):
|
|
if self.encoder_type == "2d" and x.dim() == 5:
|
|
b, _, t, _, _ = x.shape
|
|
x = rearrange(x, 'b c t h w -> (b t) c h w')
|
|
reshape_back = True
|
|
else:
|
|
reshape_back = False
|
|
|
|
## consume more GPU memory but faster
|
|
if not self.perframe_ae:
|
|
encoder_posterior = self.first_stage_model.encode(x)
|
|
results = self.get_first_stage_encoding(encoder_posterior).detach()
|
|
else: ## consume less GPU memory but slower
|
|
results = []
|
|
for index in range(x.shape[0]):
|
|
frame_batch = self.first_stage_model.encode(x[index:index+1,:,:,:])
|
|
frame_result = self.get_first_stage_encoding(frame_batch).detach()
|
|
results.append(frame_result)
|
|
results = torch.cat(results, dim=0)
|
|
|
|
if reshape_back:
|
|
results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
|
|
|
|
return results
|
|
|
|
def decode_core(self, z, **kwargs):
|
|
if self.encoder_type == "2d" and z.dim() == 5:
|
|
b, _, t, _, _ = z.shape
|
|
z = rearrange(z, 'b c t h w -> (b t) c h w')
|
|
reshape_back = True
|
|
else:
|
|
reshape_back = False
|
|
|
|
z = 1. / self.scale_factor * z
|
|
if not self.perframe_ae:
|
|
results = self.first_stage_model.decode(z, **kwargs)
|
|
else:
|
|
|
|
results = []
|
|
|
|
n_samples = default(self.en_and_decode_n_samples_a_time, self.temporal_length)
|
|
n_rounds = math.ceil(z.shape[0] / n_samples)
|
|
#with torch.autocast(mm.get_autocast_device(device), enabled=True):
|
|
for n in range(n_rounds):
|
|
if isinstance(self.first_stage_model.decoder, VideoDecoder):
|
|
kwargs.update({"timesteps": len(z[n * n_samples : (n + 1) * n_samples])})
|
|
else:
|
|
kwargs = {}
|
|
|
|
out = self.first_stage_model.decode(
|
|
z[n * n_samples : (n + 1) * n_samples], **kwargs
|
|
)
|
|
results.append(out)
|
|
results = torch.cat(results, dim=0)
|
|
|
|
if reshape_back:
|
|
results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
|
|
return results
|
|
|
|
@torch.no_grad()
|
|
def decode_first_stage(self, z, **kwargs):
|
|
return self.decode_core(z, **kwargs)
|
|
|
|
# same as above but without decorator
|
|
def differentiable_decode_first_stage(self, z, **kwargs):
|
|
return self.decode_core(z, **kwargs)
|
|
|
|
def forward(self, x, c, **kwargs):
|
|
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
|
if self.use_dynamic_rescale:
|
|
x = x * extract_into_tensor(self.scale_arr, t, x.shape)
|
|
return self.p_losses(x, c, t, **kwargs)
|
|
|
|
def apply_model(self, x_noisy, t, cond, **kwargs):
|
|
if isinstance(cond, dict):
|
|
# hybrid case, cond is exptected to be a dict
|
|
pass
|
|
else:
|
|
if not isinstance(cond, list):
|
|
cond = [cond]
|
|
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
|
|
cond = {key: [cond[0]]}
|
|
|
|
control_cond = cond["control_cond"]
|
|
|
|
if control_cond is not None:
|
|
control_cond = rearrange(control_cond, 'b c t h w-> (b t) c h w')
|
|
control_x = rearrange(x_noisy, 'b c t h w-> (b t) c h w')
|
|
control_context = repeat(cond["c_crossattn"][0], "b c l-> (repeat b) c l", repeat=16)
|
|
control = self.control_model(x=control_x, hint=control_cond, timesteps=t, context=control_context)
|
|
control = [c * self.control_model.control_scale for c in control]
|
|
else:
|
|
control = None
|
|
|
|
x_recon = self.model(x_noisy, t, c_crossattn=cond["c_crossattn"], c_concat=cond["c_concat"], control=control, **kwargs)
|
|
|
|
if isinstance(x_recon, tuple):
|
|
return x_recon[0]
|
|
else:
|
|
return x_recon
|
|
|
|
def _get_denoise_row_from_list(self, samples, desc=''):
|
|
denoise_row = []
|
|
for zd in tqdm(samples, desc=desc):
|
|
denoise_row.append(self.decode_first_stage(zd.to(self.device)))
|
|
n_log_timesteps = len(denoise_row)
|
|
|
|
denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W
|
|
|
|
if denoise_row.dim() == 5:
|
|
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
|
|
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
|
|
denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps)
|
|
elif denoise_row.dim() == 6:
|
|
# video, grid_size=[n_log_timesteps*bs, t]
|
|
video_length = denoise_row.shape[3]
|
|
denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w')
|
|
denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
|
|
denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
|
|
denoise_grid = make_grid(denoise_grid, nrow=video_length)
|
|
else:
|
|
raise ValueError
|
|
|
|
return denoise_grid
|
|
|
|
|
|
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs):
|
|
t_in = t
|
|
model_out = self.apply_model(x, t_in, c, **kwargs)
|
|
|
|
if score_corrector is not None:
|
|
assert self.parameterization == "eps"
|
|
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
|
|
|
|
if self.parameterization == "eps":
|
|
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
|
|
elif self.parameterization == "x0":
|
|
x_recon = model_out
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
if clip_denoised:
|
|
x_recon.clamp_(-1., 1.)
|
|
|
|
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
|
|
|
if return_x0:
|
|
return model_mean, posterior_variance, posterior_log_variance, x_recon
|
|
else:
|
|
return model_mean, posterior_variance, posterior_log_variance
|
|
|
|
@torch.no_grad()
|
|
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs):
|
|
b, *_, device = *x.shape, x.device
|
|
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \
|
|
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs)
|
|
if return_x0:
|
|
model_mean, _, model_log_variance, x0 = outputs
|
|
else:
|
|
model_mean, _, model_log_variance = outputs
|
|
|
|
noise = noise_like(x.shape, device, repeat_noise) * temperature
|
|
if noise_dropout > 0.:
|
|
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
|
# no noise when t == 0
|
|
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
|
|
|
if return_x0:
|
|
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
|
|
else:
|
|
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
|
|
|
@torch.no_grad()
|
|
def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
|
|
timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs):
|
|
|
|
if not log_every_t:
|
|
log_every_t = self.log_every_t
|
|
device = self.betas.device
|
|
b = shape[0]
|
|
# sample an initial noise
|
|
if x_T is None:
|
|
img = torch.randn(shape, device=device)
|
|
else:
|
|
img = x_T
|
|
|
|
intermediates = [img]
|
|
if timesteps is None:
|
|
timesteps = self.num_timesteps
|
|
if start_T is not None:
|
|
timesteps = min(timesteps, start_T)
|
|
|
|
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps))
|
|
|
|
if mask is not None:
|
|
assert x0 is not None
|
|
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
|
|
|
|
for i in iterator:
|
|
ts = torch.full((b,), i, device=device, dtype=torch.long)
|
|
if self.shorten_cond_schedule:
|
|
assert self.model.conditioning_key != 'hybrid'
|
|
tc = self.cond_ids[ts].to(cond.device)
|
|
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
|
|
|
|
img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs)
|
|
if mask is not None:
|
|
img_orig = self.q_sample(x0, ts)
|
|
img = img_orig * mask + (1. - mask) * img
|
|
|
|
if i % log_every_t == 0 or i == timesteps - 1:
|
|
intermediates.append(img)
|
|
if callback: callback(i)
|
|
if img_callback: img_callback(img, i)
|
|
|
|
if return_intermediates:
|
|
return img, intermediates
|
|
return img
|
|
|
|
|
|
class LatentVisualDiffusion(LatentDiffusion):
|
|
def __init__(self, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self._init_embedder(img_cond_stage_config, freeze_embedder)
|
|
self.image_proj_model = instantiate_from_config(image_proj_stage_config)
|
|
|
|
def _init_embedder(self, config, freeze=True):
|
|
embedder = instantiate_from_config(config)
|
|
if freeze:
|
|
self.embedder = embedder.eval()
|
|
self.embedder.train = disabled_train
|
|
for param in self.embedder.parameters():
|
|
param.requires_grad = False
|
|
|
|
|
|
class DiffusionWrapper(pl.LightningModule):
|
|
def __init__(self, diff_model_config, conditioning_key):
|
|
super().__init__()
|
|
self.diffusion_model = instantiate_from_config(diff_model_config)
|
|
self.conditioning_key = conditioning_key
|
|
|
|
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, control = None,
|
|
c_adm=None, s=None, mask=None, **kwargs):
|
|
# temporal_context = fps is foNone
|
|
if self.conditioning_key is None:
|
|
out = self.diffusion_model(x, t)
|
|
elif self.conditioning_key == 'concat':
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
out = self.diffusion_model(xc, t, **kwargs)
|
|
elif self.conditioning_key == 'crossattn':
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(x, t, context=cc, **kwargs)
|
|
elif self.conditioning_key == 'hybrid':
|
|
## it is just right [b,c,t,h,w]: concatenate in channel dim
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc, control=control, **kwargs)
|
|
elif self.conditioning_key == 'resblockcond':
|
|
cc = c_crossattn[0]
|
|
out = self.diffusion_model(x, t, context=cc)
|
|
elif self.conditioning_key == 'adm':
|
|
cc = c_crossattn[0]
|
|
out = self.diffusion_model(x, t, y=cc)
|
|
elif self.conditioning_key == 'hybrid-adm':
|
|
assert c_adm is not None
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
|
|
elif self.conditioning_key == 'hybrid-time':
|
|
assert s is not None
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc, s=s)
|
|
elif self.conditioning_key == 'concat-time-mask':
|
|
# assert s is not None
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
out = self.diffusion_model(xc, t, context=None, s=s, mask=mask)
|
|
elif self.conditioning_key == 'concat-adm-mask':
|
|
# assert s is not None
|
|
if c_concat is not None:
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
else:
|
|
xc = x
|
|
out = self.diffusion_model(xc, t, context=None, y=s, mask=mask)
|
|
elif self.conditioning_key == 'hybrid-adm-mask':
|
|
cc = torch.cat(c_crossattn, 1)
|
|
if c_concat is not None:
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
else:
|
|
xc = x
|
|
out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask)
|
|
elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
|
|
# assert s is not None
|
|
assert c_adm is not None
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm)
|
|
elif self.conditioning_key == 'crossattn-adm':
|
|
assert c_adm is not None
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(x, t, context=cc, y=c_adm)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
return out |