2381 lines
87 KiB
Python
2381 lines
87 KiB
Python
"""
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Part of the implementation is borrowed and modified from ControlNet, publicly available at https://github.com/lllyasviel/ControlNet/blob/main/ldm/models/diffusion/ddpm.py
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"""
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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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from torch.optim.lr_scheduler import LambdaLR
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from einops import rearrange, repeat
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from contextlib import contextmanager, nullcontext
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from functools import partial
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import itertools
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from tqdm import tqdm
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from torchvision.utils import make_grid
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from omegaconf import ListConfig
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from inpaint.model.anytext.ldm.util import (
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log_txt_as_img,
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exists,
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default,
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ismap,
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isimage,
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mean_flat,
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count_params,
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instantiate_from_config,
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)
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from inpaint.model.anytext.ldm.modules.ema import LitEma
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from inpaint.model.anytext.ldm.modules.distributions.distributions import (
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normal_kl,
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DiagonalGaussianDistribution,
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)
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from inpaint.model.anytext.ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
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from inpaint.model.anytext.ldm.modules.diffusionmodules.util import (
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make_beta_schedule,
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extract_into_tensor,
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noise_like,
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)
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from inpaint.model.anytext.ldm.models.diffusion.ddim import DDIMSampler
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import cv2
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__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
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PRINT_DEBUG = False
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def print_grad(grad):
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# print('Gradient:', grad)
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# print(grad.shape)
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a = grad.max()
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b = grad.min()
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# print(f'mean={grad.mean():.4f}, max={a:.4f}, min={b:.4f}')
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s = 255.0 / (a - b)
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c = 255 * (-b / (a - b))
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grad = grad * s + c
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# print(f'mean={grad.mean():.4f}, max={grad.max():.4f}, min={grad.min():.4f}')
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img = grad[0].permute(1, 2, 0).detach().cpu().numpy()
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if img.shape[0] == 512:
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cv2.imwrite("grad-img.jpg", img)
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elif img.shape[0] == 64:
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cv2.imwrite("grad-latent.jpg", img)
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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 DDPM(torch.nn.Module):
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# classic DDPM with Gaussian diffusion, in image space
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def __init__(
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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="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=0.0,
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v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.0,
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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=0.0,
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make_it_fit=False,
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ucg_training=None,
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reset_ema=False,
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reset_num_ema_updates=False,
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):
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super().__init__()
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assert parameterization in [
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"eps",
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"x0",
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"v",
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], 'currently only supporting "eps" and "x0" and "v"'
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self.parameterization = parameterization
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print(
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f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
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)
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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 = 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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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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self.make_it_fit = make_it_fit
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if reset_ema:
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assert exists(ckpt_path)
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if ckpt_path is not None:
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self.init_from_ckpt(
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ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
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)
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if reset_ema:
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assert self.use_ema
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print(
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f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint."
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)
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self.model_ema = LitEma(self.model)
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if reset_num_ema_updates:
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print(
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" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ "
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)
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assert self.use_ema
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self.model_ema.reset_num_updates()
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self.register_schedule(
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given_betas=given_betas,
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beta_schedule=beta_schedule,
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timesteps=timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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self.loss_type = loss_type
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self.learn_logvar = learn_logvar
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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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else:
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self.register_buffer("logvar", logvar)
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self.ucg_training = ucg_training or dict()
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if self.ucg_training:
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self.ucg_prng = np.random.RandomState()
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def register_schedule(
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self,
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given_betas=None,
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beta_schedule="linear",
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timesteps=1000,
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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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):
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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(
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beta_schedule,
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timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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alphas = 1.0 - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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# np.save('1.npy', alphas_cumprod)
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alphas_cumprod_prev = np.append(1.0, 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 (
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alphas_cumprod.shape[0] == self.num_timesteps
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), "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(
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"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
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)
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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 * (
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1.0 - alphas_cumprod_prev
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) / (1.0 - 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(
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"posterior_log_variance_clipped",
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to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
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)
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self.register_buffer(
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"posterior_mean_coef1",
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to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
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)
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self.register_buffer(
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"posterior_mean_coef2",
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to_torch(
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(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
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),
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)
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if self.parameterization == "eps":
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lvlb_weights = self.betas**2 / (
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2
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* self.posterior_variance
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* to_torch(alphas)
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* (1 - self.alphas_cumprod)
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)
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elif self.parameterization == "x0":
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lvlb_weights = (
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0.5
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* np.sqrt(torch.Tensor(alphas_cumprod))
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/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
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)
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elif self.parameterization == "v":
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lvlb_weights = torch.ones_like(
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self.betas**2
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/ (
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2
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* self.posterior_variance
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* to_torch(alphas)
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* (1 - self.alphas_cumprod)
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)
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)
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else:
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raise NotImplementedError("mu not supported")
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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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@torch.no_grad()
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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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print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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if self.make_it_fit:
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n_params = len(
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[
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name
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for name, _ in itertools.chain(
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self.named_parameters(), self.named_buffers()
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)
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]
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)
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for name, param in tqdm(
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itertools.chain(self.named_parameters(), self.named_buffers()),
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desc="Fitting old weights to new weights",
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total=n_params,
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):
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if not name in sd:
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continue
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old_shape = sd[name].shape
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new_shape = param.shape
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assert len(old_shape) == len(new_shape)
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if len(new_shape) > 2:
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# we only modify first two axes
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assert new_shape[2:] == old_shape[2:]
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# assumes first axis corresponds to output dim
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if not new_shape == old_shape:
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new_param = param.clone()
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old_param = sd[name]
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if len(new_shape) == 1:
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for i in range(new_param.shape[0]):
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new_param[i] = old_param[i % old_shape[0]]
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elif len(new_shape) >= 2:
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for i in range(new_param.shape[0]):
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for j in range(new_param.shape[1]):
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new_param[i, j] = old_param[
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i % old_shape[0], j % old_shape[1]
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]
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n_used_old = torch.ones(old_shape[1])
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for j in range(new_param.shape[1]):
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n_used_old[j % old_shape[1]] += 1
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n_used_new = torch.zeros(new_shape[1])
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for j in range(new_param.shape[1]):
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n_used_new[j] = n_used_old[j % old_shape[1]]
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n_used_new = n_used_new[None, :]
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while len(n_used_new.shape) < len(new_shape):
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n_used_new = n_used_new.unsqueeze(-1)
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new_param /= n_used_new
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sd[name] = new_param
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missing, unexpected = (
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self.load_state_dict(sd, strict=False)
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if not only_model
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else self.model.load_state_dict(sd, strict=False)
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)
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print(
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f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
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)
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if len(missing) > 0:
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print(f"Missing Keys:\n {missing}")
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if len(unexpected) > 0:
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print(f"\nUnexpected Keys:\n {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(
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self.log_one_minus_alphas_cumprod, t, x_start.shape
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)
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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)
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* 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)
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* 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(
|
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self.posterior_log_variance_clipped, t, x_t.shape
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)
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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|
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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_(-1.0, 1.0)
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|
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model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
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x_start=x_recon, x_t=x, t=t
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)
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return model_mean, posterior_variance, posterior_log_variance
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|
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@torch.no_grad()
|
|
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(
|
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x=x, t=t, clip_denoised=clip_denoised
|
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)
|
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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()
|
|
def p_sample_loop(self, shape, return_intermediates=False):
|
|
device = self.betas.device
|
|
b = shape[0]
|
|
img = torch.randn(shape, device=device)
|
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intermediates = [img]
|
|
for i in tqdm(
|
|
reversed(range(0, self.num_timesteps)),
|
|
desc="Sampling t",
|
|
total=self.num_timesteps,
|
|
):
|
|
img = self.p_sample(
|
|
img,
|
|
torch.full((b,), i, device=device, dtype=torch.long),
|
|
clip_denoised=self.clip_denoised,
|
|
)
|
|
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
|
|
intermediates.append(img)
|
|
if return_intermediates:
|
|
return img, intermediates
|
|
return img
|
|
|
|
@torch.no_grad()
|
|
def sample(self, batch_size=16, return_intermediates=False):
|
|
image_size = self.image_size
|
|
channels = self.channels
|
|
return self.p_sample_loop(
|
|
(batch_size, channels, image_size, image_size),
|
|
return_intermediates=return_intermediates,
|
|
)
|
|
|
|
def q_sample(self, x_start, t, noise=None):
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
return (
|
|
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
|
|
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
|
|
* noise
|
|
)
|
|
|
|
def get_v(self, x, noise, t):
|
|
return (
|
|
extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise
|
|
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
|
|
)
|
|
|
|
def get_loss(self, pred, target, mean=True):
|
|
if self.loss_type == "l1":
|
|
loss = (target - pred).abs()
|
|
if mean:
|
|
loss = loss.mean()
|
|
elif self.loss_type == "l2":
|
|
if mean:
|
|
loss = torch.nn.functional.mse_loss(target, pred)
|
|
else:
|
|
loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
|
|
else:
|
|
raise NotImplementedError("unknown loss type '{loss_type}'")
|
|
|
|
return loss
|
|
|
|
def p_losses(self, x_start, t, noise=None):
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
|
model_out = self.model(x_noisy, t)
|
|
|
|
loss_dict = {}
|
|
if self.parameterization == "eps":
|
|
target = noise
|
|
elif self.parameterization == "x0":
|
|
target = x_start
|
|
elif self.parameterization == "v":
|
|
target = self.get_v(x_start, noise, t)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Parameterization {self.parameterization} not yet supported"
|
|
)
|
|
|
|
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
|
|
|
|
log_prefix = "train" if self.training else "val"
|
|
|
|
loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
|
|
loss_simple = loss.mean() * self.l_simple_weight
|
|
|
|
loss_vlb = (self.lvlb_weights[t] * loss).mean()
|
|
loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})
|
|
|
|
loss = loss_simple + self.original_elbo_weight * loss_vlb
|
|
|
|
loss_dict.update({f"{log_prefix}/loss": loss})
|
|
|
|
return loss, loss_dict
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
|
|
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
|
|
t = torch.randint(
|
|
0, self.num_timesteps, (x.shape[0],), device=self.device
|
|
).long()
|
|
return self.p_losses(x, t, *args, **kwargs)
|
|
|
|
def get_input(self, batch, k):
|
|
x = batch[k]
|
|
if len(x.shape) == 3:
|
|
x = x[..., None]
|
|
x = rearrange(x, "b h w c -> b c h w")
|
|
x = x.to(memory_format=torch.contiguous_format).float()
|
|
return x
|
|
|
|
def shared_step(self, batch):
|
|
x = self.get_input(batch, self.first_stage_key)
|
|
loss, loss_dict = self(x)
|
|
return loss, loss_dict
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
for k in self.ucg_training:
|
|
p = self.ucg_training[k]["p"]
|
|
val = self.ucg_training[k]["val"]
|
|
if val is None:
|
|
val = ""
|
|
for i in range(len(batch[k])):
|
|
if self.ucg_prng.choice(2, p=[1 - p, p]):
|
|
batch[k][i] = val
|
|
|
|
loss, loss_dict = self.shared_step(batch)
|
|
|
|
self.log_dict(
|
|
loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
|
|
)
|
|
|
|
self.log(
|
|
"global_step",
|
|
self.global_step,
|
|
prog_bar=True,
|
|
logger=True,
|
|
on_step=True,
|
|
on_epoch=False,
|
|
)
|
|
|
|
if self.use_scheduler:
|
|
lr = self.optimizers().param_groups[0]["lr"]
|
|
self.log(
|
|
"lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
|
|
)
|
|
|
|
return loss
|
|
|
|
@torch.no_grad()
|
|
def validation_step(self, batch, batch_idx):
|
|
_, loss_dict_no_ema = self.shared_step(batch)
|
|
with self.ema_scope():
|
|
_, loss_dict_ema = self.shared_step(batch)
|
|
loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
|
|
self.log_dict(
|
|
loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
|
|
)
|
|
self.log_dict(
|
|
loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
|
|
)
|
|
|
|
def on_train_batch_end(self, *args, **kwargs):
|
|
if self.use_ema:
|
|
self.model_ema(self.model)
|
|
|
|
def _get_rows_from_list(self, samples):
|
|
n_imgs_per_row = len(samples)
|
|
denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
|
|
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
|
|
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
|
|
return denoise_grid
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
|
|
log = dict()
|
|
x = self.get_input(batch, self.first_stage_key)
|
|
N = min(x.shape[0], N)
|
|
n_row = min(x.shape[0], n_row)
|
|
x = x.to(self.device)[:N]
|
|
log["inputs"] = x
|
|
|
|
# get diffusion row
|
|
diffusion_row = list()
|
|
x_start = x[:n_row]
|
|
|
|
for t in range(self.num_timesteps):
|
|
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
|
t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
|
|
t = t.to(self.device).long()
|
|
noise = torch.randn_like(x_start)
|
|
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
|
diffusion_row.append(x_noisy)
|
|
|
|
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
|
|
|
|
if sample:
|
|
# get denoise row
|
|
with self.ema_scope("Plotting"):
|
|
samples, denoise_row = self.sample(
|
|
batch_size=N, return_intermediates=True
|
|
)
|
|
|
|
log["samples"] = samples
|
|
log["denoise_row"] = self._get_rows_from_list(denoise_row)
|
|
|
|
if return_keys:
|
|
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
|
|
return log
|
|
else:
|
|
return {key: log[key] for key in return_keys}
|
|
return log
|
|
|
|
def configure_optimizers(self):
|
|
lr = self.learning_rate
|
|
params = list(self.model.parameters())
|
|
if self.learn_logvar:
|
|
params = params + [self.logvar]
|
|
opt = torch.optim.AdamW(params, lr=lr)
|
|
return opt
|
|
|
|
|
|
class LatentDiffusion(DDPM):
|
|
"""main class"""
|
|
|
|
def __init__(
|
|
self,
|
|
first_stage_config,
|
|
cond_stage_config,
|
|
num_timesteps_cond=None,
|
|
cond_stage_key="image",
|
|
cond_stage_trainable=False,
|
|
concat_mode=True,
|
|
cond_stage_forward=None,
|
|
conditioning_key=None,
|
|
scale_factor=1.0,
|
|
scale_by_std=False,
|
|
force_null_conditioning=False,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
self.force_null_conditioning = force_null_conditioning
|
|
self.num_timesteps_cond = default(num_timesteps_cond, 1)
|
|
self.scale_by_std = scale_by_std
|
|
assert self.num_timesteps_cond <= kwargs["timesteps"]
|
|
# for backwards compatibility after implementation of DiffusionWrapper
|
|
if conditioning_key is None:
|
|
conditioning_key = "concat" if concat_mode else "crossattn"
|
|
if (
|
|
cond_stage_config == "__is_unconditional__"
|
|
and not self.force_null_conditioning
|
|
):
|
|
conditioning_key = None
|
|
ckpt_path = kwargs.pop("ckpt_path", None)
|
|
reset_ema = kwargs.pop("reset_ema", False)
|
|
reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
|
|
ignore_keys = kwargs.pop("ignore_keys", [])
|
|
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
|
|
self.concat_mode = concat_mode
|
|
self.cond_stage_trainable = cond_stage_trainable
|
|
self.cond_stage_key = cond_stage_key
|
|
try:
|
|
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
|
except:
|
|
self.num_downs = 0
|
|
if not scale_by_std:
|
|
self.scale_factor = scale_factor
|
|
else:
|
|
self.register_buffer("scale_factor", torch.tensor(scale_factor))
|
|
self.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
|
|
if reset_ema:
|
|
assert self.use_ema
|
|
print(
|
|
f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint."
|
|
)
|
|
self.model_ema = LitEma(self.model)
|
|
if reset_num_ema_updates:
|
|
print(
|
|
" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ "
|
|
)
|
|
assert self.use_ema
|
|
self.model_ema.reset_num_updates()
|
|
|
|
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
|
|
|
|
@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 == 1.0
|
|
), "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", 1.0 / 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,
|
|
return_x=False,
|
|
mask_k=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 mask_k is not None:
|
|
mx = super().get_input(batch, mask_k)
|
|
if bs is not None:
|
|
mx = mx[:bs]
|
|
mx = mx.to(self.device)
|
|
encoder_posterior = self.encode_first_stage(mx)
|
|
mx = self.get_first_stage_encoding(encoder_posterior).detach()
|
|
|
|
if self.model.conditioning_key is not None and not self.force_null_conditioning:
|
|
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", "txt"]:
|
|
xc = batch[cond_key]
|
|
elif cond_key in ["class_label", "cls"]:
|
|
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):
|
|
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_x:
|
|
out.extend([x])
|
|
if return_original_cond:
|
|
out.append(xc)
|
|
if mask_k:
|
|
out.append(mx)
|
|
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 = 1.0 / self.scale_factor * z
|
|
return self.first_stage_model.decode(z)
|
|
|
|
def decode_first_stage_grad(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 = 1.0 / self.scale_factor * z
|
|
return self.first_stage_model.decode(z)
|
|
|
|
@torch.no_grad()
|
|
def encode_first_stage(self, x):
|
|
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()
|
|
# t = torch.randint(500, 501, (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 expected to be a dict
|
|
pass
|
|
else:
|
|
if not isinstance(cond, list):
|
|
cond = [cond]
|
|
key = (
|
|
"c_concat" if self.model.conditioning_key == "concat" else "c_crossattn"
|
|
)
|
|
cond = {key: cond}
|
|
|
|
x_recon = self.model(x_noisy, t, **cond)
|
|
|
|
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_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_(-1.0, 1.0)
|
|
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=1.0,
|
|
noise_dropout=0.0,
|
|
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 > 0.0:
|
|
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=1.0,
|
|
noise_dropout=0.0,
|
|
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 list(map(lambda x: x[:batch_size], 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 + (1.0 - 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 + (1.0 - mask) * img
|
|
|
|
if i % log_every_t == 0 or i == timesteps - 1:
|
|
intermediates.append(img)
|
|
if callback:
|
|
callback(i)
|
|
if img_callback:
|
|
img_callback(img, i)
|
|
|
|
if return_intermediates:
|
|
return img, intermediates
|
|
return img
|
|
|
|
@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 list(map(lambda x: x[:batch_size], 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 get_unconditional_conditioning(self, batch_size, null_label=None):
|
|
if null_label is not None:
|
|
xc = null_label
|
|
if isinstance(xc, ListConfig):
|
|
xc = list(xc)
|
|
if isinstance(xc, dict) or isinstance(xc, list):
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
if hasattr(xc, "to"):
|
|
xc = xc.to(self.device)
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
if self.cond_stage_key in ["class_label", "cls"]:
|
|
xc = self.cond_stage_model.get_unconditional_conditioning(
|
|
batch_size, device=self.device
|
|
)
|
|
return self.get_learned_conditioning(xc)
|
|
else:
|
|
raise NotImplementedError("todo")
|
|
if isinstance(c, list): # in case the encoder gives us a list
|
|
for i in range(len(c)):
|
|
c[i] = repeat(c[i], "1 ... -> b ...", b=batch_size).to(self.device)
|
|
else:
|
|
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
|
|
return c
|
|
|
|
@torch.no_grad()
|
|
def log_images(
|
|
self,
|
|
batch,
|
|
N=8,
|
|
n_row=4,
|
|
sample=True,
|
|
ddim_steps=50,
|
|
ddim_eta=0.0,
|
|
return_keys=None,
|
|
quantize_denoised=True,
|
|
inpaint=True,
|
|
plot_denoise_rows=False,
|
|
plot_progressive_rows=True,
|
|
plot_diffusion_rows=True,
|
|
unconditional_guidance_scale=1.0,
|
|
unconditional_guidance_label=None,
|
|
use_ema_scope=True,
|
|
**kwargs,
|
|
):
|
|
ema_scope = self.ema_scope if use_ema_scope else nullcontext
|
|
use_ddim = ddim_steps is not None
|
|
|
|
log = dict()
|
|
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", "txt"]:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch[self.cond_stage_key],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
log["conditioning"] = xc
|
|
elif self.cond_stage_key in ["class_label", "cls"]:
|
|
try:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch["human_label"],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
log["conditioning"] = xc
|
|
except KeyError:
|
|
# probably no "human_label" in batch
|
|
pass
|
|
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 = list()
|
|
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 ema_scope("Sampling"):
|
|
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 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 unconditional_guidance_scale > 1.0:
|
|
uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
|
|
if self.model.conditioning_key == "crossattn-adm":
|
|
uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
|
|
with ema_scope("Sampling with classifier-free guidance"):
|
|
samples_cfg, _ = self.sample_log(
|
|
cond=c,
|
|
batch_size=N,
|
|
ddim=use_ddim,
|
|
ddim_steps=ddim_steps,
|
|
eta=ddim_eta,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=uc,
|
|
)
|
|
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
|
log[
|
|
f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
|
|
] = x_samples_cfg
|
|
|
|
if inpaint:
|
|
# make a simple center square
|
|
b, h, w = z.shape[0], 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] = 0.0
|
|
mask = mask[:, None, ...]
|
|
with 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
|
|
mask = 1.0 - mask
|
|
with 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 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 = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
|
|
return x
|
|
|
|
|
|
class DiffusionWrapper(torch.nn.Module):
|
|
def __init__(self, diff_model_config, conditioning_key):
|
|
super().__init__()
|
|
self.sequential_cross_attn = diff_model_config.pop(
|
|
"sequential_crossattn", False
|
|
)
|
|
self.diffusion_model = instantiate_from_config(diff_model_config)
|
|
self.conditioning_key = conditioning_key
|
|
assert self.conditioning_key in [
|
|
None,
|
|
"concat",
|
|
"crossattn",
|
|
"hybrid",
|
|
"adm",
|
|
"hybrid-adm",
|
|
"crossattn-adm",
|
|
]
|
|
|
|
def forward(
|
|
self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=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":
|
|
if not self.sequential_cross_attn:
|
|
cc = torch.cat(c_crossattn, 1)
|
|
else:
|
|
cc = c_crossattn
|
|
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 == "hybrid-adm":
|
|
assert c_adm is not None
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc, y=c_adm)
|
|
elif self.conditioning_key == "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)
|
|
elif self.conditioning_key == "adm":
|
|
cc = c_crossattn[0]
|
|
out = self.diffusion_model(x, t, y=cc)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
return out
|
|
|
|
|
|
class LatentUpscaleDiffusion(LatentDiffusion):
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
low_scale_config,
|
|
low_scale_key="LR",
|
|
noise_level_key=None,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
# assumes that neither the cond_stage nor the low_scale_model contain trainable params
|
|
assert not self.cond_stage_trainable
|
|
self.instantiate_low_stage(low_scale_config)
|
|
self.low_scale_key = low_scale_key
|
|
self.noise_level_key = noise_level_key
|
|
|
|
def instantiate_low_stage(self, config):
|
|
model = instantiate_from_config(config)
|
|
self.low_scale_model = model.eval()
|
|
self.low_scale_model.train = disabled_train
|
|
for param in self.low_scale_model.parameters():
|
|
param.requires_grad = False
|
|
|
|
@torch.no_grad()
|
|
def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
|
|
if not log_mode:
|
|
z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
|
|
else:
|
|
z, c, x, xrec, xc = super().get_input(
|
|
batch,
|
|
self.first_stage_key,
|
|
return_first_stage_outputs=True,
|
|
force_c_encode=True,
|
|
return_original_cond=True,
|
|
bs=bs,
|
|
)
|
|
x_low = batch[self.low_scale_key][:bs]
|
|
x_low = rearrange(x_low, "b h w c -> b c h w")
|
|
x_low = x_low.to(memory_format=torch.contiguous_format).float()
|
|
zx, noise_level = self.low_scale_model(x_low)
|
|
if self.noise_level_key is not None:
|
|
# get noise level from batch instead, e.g. when extracting a custom noise level for bsr
|
|
raise NotImplementedError("TODO")
|
|
|
|
all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
|
|
if log_mode:
|
|
# TODO: maybe disable if too expensive
|
|
x_low_rec = self.low_scale_model.decode(zx)
|
|
return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
|
|
return z, all_conds
|
|
|
|
@torch.no_grad()
|
|
def log_images(
|
|
self,
|
|
batch,
|
|
N=8,
|
|
n_row=4,
|
|
sample=True,
|
|
ddim_steps=200,
|
|
ddim_eta=1.0,
|
|
return_keys=None,
|
|
plot_denoise_rows=False,
|
|
plot_progressive_rows=True,
|
|
plot_diffusion_rows=True,
|
|
unconditional_guidance_scale=1.0,
|
|
unconditional_guidance_label=None,
|
|
use_ema_scope=True,
|
|
**kwargs,
|
|
):
|
|
ema_scope = self.ema_scope if use_ema_scope else nullcontext
|
|
use_ddim = ddim_steps is not None
|
|
|
|
log = dict()
|
|
z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(
|
|
batch, self.first_stage_key, bs=N, log_mode=True
|
|
)
|
|
N = min(x.shape[0], N)
|
|
n_row = min(x.shape[0], n_row)
|
|
log["inputs"] = x
|
|
log["reconstruction"] = xrec
|
|
log["x_lr"] = x_low
|
|
log[
|
|
f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"
|
|
] = x_low_rec
|
|
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", "txt"]:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch[self.cond_stage_key],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
log["conditioning"] = xc
|
|
elif self.cond_stage_key in ["class_label", "cls"]:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch["human_label"],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
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 = list()
|
|
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 ema_scope("Sampling"):
|
|
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 unconditional_guidance_scale > 1.0:
|
|
uc_tmp = self.get_unconditional_conditioning(
|
|
N, unconditional_guidance_label
|
|
)
|
|
# TODO explore better "unconditional" choices for the other keys
|
|
# maybe guide away from empty text label and highest noise level and maximally degraded zx?
|
|
uc = dict()
|
|
for k in c:
|
|
if k == "c_crossattn":
|
|
assert isinstance(c[k], list) and len(c[k]) == 1
|
|
uc[k] = [uc_tmp]
|
|
elif k == "c_adm": # todo: only run with text-based guidance?
|
|
assert isinstance(c[k], torch.Tensor)
|
|
# uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
|
|
uc[k] = c[k]
|
|
elif isinstance(c[k], list):
|
|
uc[k] = [c[k][i] for i in range(len(c[k]))]
|
|
else:
|
|
uc[k] = c[k]
|
|
|
|
with ema_scope("Sampling with classifier-free guidance"):
|
|
samples_cfg, _ = self.sample_log(
|
|
cond=c,
|
|
batch_size=N,
|
|
ddim=use_ddim,
|
|
ddim_steps=ddim_steps,
|
|
eta=ddim_eta,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=uc,
|
|
)
|
|
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
|
log[
|
|
f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
|
|
] = x_samples_cfg
|
|
|
|
if plot_progressive_rows:
|
|
with 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
|
|
|
|
return log
|
|
|
|
|
|
class LatentFinetuneDiffusion(LatentDiffusion):
|
|
"""
|
|
Basis for different finetunas, such as inpainting or depth2image
|
|
To disable finetuning mode, set finetune_keys to None
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
concat_keys: tuple,
|
|
finetune_keys=(
|
|
"model.diffusion_model.input_blocks.0.0.weight",
|
|
"model_ema.diffusion_modelinput_blocks00weight",
|
|
),
|
|
keep_finetune_dims=4,
|
|
# if model was trained without concat mode before and we would like to keep these channels
|
|
c_concat_log_start=None, # to log reconstruction of c_concat codes
|
|
c_concat_log_end=None,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
ckpt_path = kwargs.pop("ckpt_path", None)
|
|
ignore_keys = kwargs.pop("ignore_keys", list())
|
|
super().__init__(*args, **kwargs)
|
|
self.finetune_keys = finetune_keys
|
|
self.concat_keys = concat_keys
|
|
self.keep_dims = keep_finetune_dims
|
|
self.c_concat_log_start = c_concat_log_start
|
|
self.c_concat_log_end = c_concat_log_end
|
|
if exists(self.finetune_keys):
|
|
assert exists(ckpt_path), "can only finetune from a given checkpoint"
|
|
if exists(ckpt_path):
|
|
self.init_from_ckpt(ckpt_path, ignore_keys)
|
|
|
|
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
|
|
sd = torch.load(path, map_location="cpu")
|
|
if "state_dict" in list(sd.keys()):
|
|
sd = sd["state_dict"]
|
|
keys = list(sd.keys())
|
|
for k in keys:
|
|
for ik in ignore_keys:
|
|
if k.startswith(ik):
|
|
print("Deleting key {} from state_dict.".format(k))
|
|
del sd[k]
|
|
|
|
# make it explicit, finetune by including extra input channels
|
|
if exists(self.finetune_keys) and k in self.finetune_keys:
|
|
new_entry = None
|
|
for name, param in self.named_parameters():
|
|
if name in self.finetune_keys:
|
|
print(
|
|
f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only"
|
|
)
|
|
new_entry = torch.zeros_like(param) # zero init
|
|
assert exists(new_entry), "did not find matching parameter to modify"
|
|
new_entry[:, : self.keep_dims, ...] = sd[k]
|
|
sd[k] = new_entry
|
|
|
|
missing, unexpected = (
|
|
self.load_state_dict(sd, strict=False)
|
|
if not only_model
|
|
else self.model.load_state_dict(sd, strict=False)
|
|
)
|
|
print(
|
|
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
|
|
)
|
|
if len(missing) > 0:
|
|
print(f"Missing Keys: {missing}")
|
|
if len(unexpected) > 0:
|
|
print(f"Unexpected Keys: {unexpected}")
|
|
|
|
@torch.no_grad()
|
|
def log_images(
|
|
self,
|
|
batch,
|
|
N=8,
|
|
n_row=4,
|
|
sample=True,
|
|
ddim_steps=200,
|
|
ddim_eta=1.0,
|
|
return_keys=None,
|
|
quantize_denoised=True,
|
|
inpaint=True,
|
|
plot_denoise_rows=False,
|
|
plot_progressive_rows=True,
|
|
plot_diffusion_rows=True,
|
|
unconditional_guidance_scale=1.0,
|
|
unconditional_guidance_label=None,
|
|
use_ema_scope=True,
|
|
**kwargs,
|
|
):
|
|
ema_scope = self.ema_scope if use_ema_scope else nullcontext
|
|
use_ddim = ddim_steps is not None
|
|
|
|
log = dict()
|
|
z, c, x, xrec, xc = self.get_input(
|
|
batch, self.first_stage_key, bs=N, return_first_stage_outputs=True
|
|
)
|
|
c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
|
|
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", "txt"]:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch[self.cond_stage_key],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
log["conditioning"] = xc
|
|
elif self.cond_stage_key in ["class_label", "cls"]:
|
|
xc = log_txt_as_img(
|
|
(x.shape[2], x.shape[3]),
|
|
batch["human_label"],
|
|
size=x.shape[2] // 25,
|
|
)
|
|
log["conditioning"] = xc
|
|
elif isimage(xc):
|
|
log["conditioning"] = xc
|
|
if ismap(xc):
|
|
log["original_conditioning"] = self.to_rgb(xc)
|
|
|
|
if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
|
|
log["c_concat_decoded"] = self.decode_first_stage(
|
|
c_cat[:, self.c_concat_log_start : self.c_concat_log_end]
|
|
)
|
|
|
|
if plot_diffusion_rows:
|
|
# get diffusion row
|
|
diffusion_row = list()
|
|
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 ema_scope("Sampling"):
|
|
samples, z_denoise_row = self.sample_log(
|
|
cond={"c_concat": [c_cat], "c_crossattn": [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 unconditional_guidance_scale > 1.0:
|
|
uc_cross = self.get_unconditional_conditioning(
|
|
N, unconditional_guidance_label
|
|
)
|
|
uc_cat = c_cat
|
|
uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
|
|
with ema_scope("Sampling with classifier-free guidance"):
|
|
samples_cfg, _ = self.sample_log(
|
|
cond={"c_concat": [c_cat], "c_crossattn": [c]},
|
|
batch_size=N,
|
|
ddim=use_ddim,
|
|
ddim_steps=ddim_steps,
|
|
eta=ddim_eta,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=uc_full,
|
|
)
|
|
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
|
log[
|
|
f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
|
|
] = x_samples_cfg
|
|
|
|
return log
|
|
|
|
|
|
class LatentInpaintDiffusion(LatentFinetuneDiffusion):
|
|
"""
|
|
can either run as pure inpainting model (only concat mode) or with mixed conditionings,
|
|
e.g. mask as concat and text via cross-attn.
|
|
To disable finetuning mode, set finetune_keys to None
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
concat_keys=("mask", "masked_image"),
|
|
masked_image_key="masked_image",
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(concat_keys, *args, **kwargs)
|
|
self.masked_image_key = masked_image_key
|
|
assert self.masked_image_key in concat_keys
|
|
|
|
@torch.no_grad()
|
|
def get_input(
|
|
self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
|
|
):
|
|
# note: restricted to non-trainable encoders currently
|
|
assert (
|
|
not self.cond_stage_trainable
|
|
), "trainable cond stages not yet supported for inpainting"
|
|
z, c, x, xrec, xc = super().get_input(
|
|
batch,
|
|
self.first_stage_key,
|
|
return_first_stage_outputs=True,
|
|
force_c_encode=True,
|
|
return_original_cond=True,
|
|
bs=bs,
|
|
)
|
|
|
|
assert exists(self.concat_keys)
|
|
c_cat = list()
|
|
for ck in self.concat_keys:
|
|
cc = (
|
|
rearrange(batch[ck], "b h w c -> b c h w")
|
|
.to(memory_format=torch.contiguous_format)
|
|
.float()
|
|
)
|
|
if bs is not None:
|
|
cc = cc[:bs]
|
|
cc = cc.to(self.device)
|
|
bchw = z.shape
|
|
if ck != self.masked_image_key:
|
|
cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
|
|
else:
|
|
cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
|
|
c_cat.append(cc)
|
|
c_cat = torch.cat(c_cat, dim=1)
|
|
all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
|
|
if return_first_stage_outputs:
|
|
return z, all_conds, x, xrec, xc
|
|
return z, all_conds
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, *args, **kwargs):
|
|
log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
|
|
log["masked_image"] = (
|
|
rearrange(args[0]["masked_image"], "b h w c -> b c h w")
|
|
.to(memory_format=torch.contiguous_format)
|
|
.float()
|
|
)
|
|
return log
|
|
|
|
|
|
class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
|
|
"""
|
|
condition on monocular depth estimation
|
|
"""
|
|
|
|
def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
|
|
super().__init__(concat_keys=concat_keys, *args, **kwargs)
|
|
self.depth_model = instantiate_from_config(depth_stage_config)
|
|
self.depth_stage_key = concat_keys[0]
|
|
|
|
@torch.no_grad()
|
|
def get_input(
|
|
self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
|
|
):
|
|
# note: restricted to non-trainable encoders currently
|
|
assert (
|
|
not self.cond_stage_trainable
|
|
), "trainable cond stages not yet supported for depth2img"
|
|
z, c, x, xrec, xc = super().get_input(
|
|
batch,
|
|
self.first_stage_key,
|
|
return_first_stage_outputs=True,
|
|
force_c_encode=True,
|
|
return_original_cond=True,
|
|
bs=bs,
|
|
)
|
|
|
|
assert exists(self.concat_keys)
|
|
assert len(self.concat_keys) == 1
|
|
c_cat = list()
|
|
for ck in self.concat_keys:
|
|
cc = batch[ck]
|
|
if bs is not None:
|
|
cc = cc[:bs]
|
|
cc = cc.to(self.device)
|
|
cc = self.depth_model(cc)
|
|
cc = torch.nn.functional.interpolate(
|
|
cc,
|
|
size=z.shape[2:],
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
|
|
depth_min, depth_max = torch.amin(
|
|
cc, dim=[1, 2, 3], keepdim=True
|
|
), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
|
|
cc = 2.0 * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.0
|
|
c_cat.append(cc)
|
|
c_cat = torch.cat(c_cat, dim=1)
|
|
all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
|
|
if return_first_stage_outputs:
|
|
return z, all_conds, x, xrec, xc
|
|
return z, all_conds
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, *args, **kwargs):
|
|
log = super().log_images(*args, **kwargs)
|
|
depth = self.depth_model(args[0][self.depth_stage_key])
|
|
depth_min, depth_max = torch.amin(
|
|
depth, dim=[1, 2, 3], keepdim=True
|
|
), torch.amax(depth, dim=[1, 2, 3], keepdim=True)
|
|
log["depth"] = 2.0 * (depth - depth_min) / (depth_max - depth_min) - 1.0
|
|
return log
|
|
|
|
|
|
class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
|
|
"""
|
|
condition on low-res image (and optionally on some spatial noise augmentation)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
concat_keys=("lr",),
|
|
reshuffle_patch_size=None,
|
|
low_scale_config=None,
|
|
low_scale_key=None,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(concat_keys=concat_keys, *args, **kwargs)
|
|
self.reshuffle_patch_size = reshuffle_patch_size
|
|
self.low_scale_model = None
|
|
if low_scale_config is not None:
|
|
print("Initializing a low-scale model")
|
|
assert exists(low_scale_key)
|
|
self.instantiate_low_stage(low_scale_config)
|
|
self.low_scale_key = low_scale_key
|
|
|
|
def instantiate_low_stage(self, config):
|
|
model = instantiate_from_config(config)
|
|
self.low_scale_model = model.eval()
|
|
self.low_scale_model.train = disabled_train
|
|
for param in self.low_scale_model.parameters():
|
|
param.requires_grad = False
|
|
|
|
@torch.no_grad()
|
|
def get_input(
|
|
self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
|
|
):
|
|
# note: restricted to non-trainable encoders currently
|
|
assert (
|
|
not self.cond_stage_trainable
|
|
), "trainable cond stages not yet supported for upscaling-ft"
|
|
z, c, x, xrec, xc = super().get_input(
|
|
batch,
|
|
self.first_stage_key,
|
|
return_first_stage_outputs=True,
|
|
force_c_encode=True,
|
|
return_original_cond=True,
|
|
bs=bs,
|
|
)
|
|
|
|
assert exists(self.concat_keys)
|
|
assert len(self.concat_keys) == 1
|
|
# optionally make spatial noise_level here
|
|
c_cat = list()
|
|
noise_level = None
|
|
for ck in self.concat_keys:
|
|
cc = batch[ck]
|
|
cc = rearrange(cc, "b h w c -> b c h w")
|
|
if exists(self.reshuffle_patch_size):
|
|
assert isinstance(self.reshuffle_patch_size, int)
|
|
cc = rearrange(
|
|
cc,
|
|
"b c (p1 h) (p2 w) -> b (p1 p2 c) h w",
|
|
p1=self.reshuffle_patch_size,
|
|
p2=self.reshuffle_patch_size,
|
|
)
|
|
if bs is not None:
|
|
cc = cc[:bs]
|
|
cc = cc.to(self.device)
|
|
if exists(self.low_scale_model) and ck == self.low_scale_key:
|
|
cc, noise_level = self.low_scale_model(cc)
|
|
c_cat.append(cc)
|
|
c_cat = torch.cat(c_cat, dim=1)
|
|
if exists(noise_level):
|
|
all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
|
|
else:
|
|
all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
|
|
if return_first_stage_outputs:
|
|
return z, all_conds, x, xrec, xc
|
|
return z, all_conds
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, *args, **kwargs):
|
|
log = super().log_images(*args, **kwargs)
|
|
log["lr"] = rearrange(args[0]["lr"], "b h w c -> b c h w")
|
|
return log
|