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900 lines
33 KiB
Python
900 lines
33 KiB
Python
"""
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wild mixture of
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https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
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https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
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https://github.com/CompVis/taming-transformers
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-- merci
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"""
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import logging
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from functools import partial
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torch.nn as nn
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from einops import rearrange
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from torchvision.utils import make_grid
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from tqdm import tqdm
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from imaginairy.modules.diffusion.util import (
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extract_into_tensor,
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make_beta_schedule,
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noise_like,
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)
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from imaginairy.modules.distributions import DiagonalGaussianDistribution
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from imaginairy.utils import instantiate_from_config, log_params
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logger = logging.getLogger(__name__)
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__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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def uniform_on_device(r1, r2, shape, device):
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return (r1 - r2) * torch.rand(*shape, device=device) + r2
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class DDPM(pl.LightningModule):
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"""
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classic DDPM with Gaussian diffusion, in image space
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Denoising diffusion probabilistic models
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"""
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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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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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):
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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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], 'currently only supporting "eps" and "x0"'
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self.parameterization = parameterization
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logger.debug(
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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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log_params(self.model)
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self.use_scheduler = scheduler_config is not None
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if self.use_scheduler:
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self.scheduler_config = scheduler_config
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self.v_posterior = v_posterior
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self.original_elbo_weight = original_elbo_weight
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self.l_simple_weight = l_simple_weight
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if monitor is not None:
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self.monitor = monitor
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if ckpt_path is not None:
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self.init_from_ckpt(
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ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
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)
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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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self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
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if self.learn_logvar:
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self.logvar = nn.Parameter(self.logvar, requires_grad=True)
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def register_schedule(
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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 given_betas is not None:
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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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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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else:
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raise NotImplementedError("mu not supported")
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# TODO how to choose this term
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lvlb_weights[0] = lvlb_weights[1]
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self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
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assert not torch.isnan(self.lvlb_weights).all()
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class LatentDiffusion(DDPM):
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"""main class"""
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def __init__(
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self,
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first_stage_config,
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cond_stage_config,
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num_timesteps_cond=None,
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cond_stage_key="image",
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cond_stage_trainable=False,
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concat_mode=True,
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cond_stage_forward=None,
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conditioning_key=None,
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scale_factor=1.0,
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scale_by_std=False,
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*args,
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**kwargs,
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):
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self.num_timesteps_cond = (
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1 if num_timesteps_cond is None else num_timesteps_cond
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)
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self.scale_by_std = scale_by_std
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assert self.num_timesteps_cond <= kwargs["timesteps"]
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# for backwards compatibility after implementation of DiffusionWrapper
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if conditioning_key is None:
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conditioning_key = "concat" if concat_mode else "crossattn"
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if cond_stage_config == "__is_unconditional__":
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conditioning_key = None
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ckpt_path = kwargs.pop("ckpt_path", None)
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ignore_keys = kwargs.pop("ignore_keys", [])
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super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
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self.concat_mode = concat_mode
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self.cond_stage_trainable = cond_stage_trainable
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self.cond_stage_key = cond_stage_key
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try:
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self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
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except: # noqa
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logger.exception("Bad num downs?")
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self.num_downs = 0
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if not scale_by_std:
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self.scale_factor = scale_factor
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else:
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self.register_buffer("scale_factor", torch.tensor(scale_factor))
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self.instantiate_first_stage(first_stage_config)
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self.instantiate_cond_stage(cond_stage_config)
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self.cond_stage_forward = cond_stage_forward
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self.clip_denoised = False
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self.bbox_tokenizer = None
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self.restarted_from_ckpt = False
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys)
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self.restarted_from_ckpt = True
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def make_cond_schedule(
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self,
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):
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self.cond_ids = torch.full(
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size=(self.num_timesteps,),
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fill_value=self.num_timesteps - 1,
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dtype=torch.long,
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)
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ids = torch.round(
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torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
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).long()
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self.cond_ids[: self.num_timesteps_cond] = ids
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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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super().register_schedule(
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given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
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)
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self.shorten_cond_schedule = self.num_timesteps_cond > 1
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if self.shorten_cond_schedule:
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self.make_cond_schedule()
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def instantiate_first_stage(self, config):
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model = instantiate_from_config(config)
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self.first_stage_model = model.eval()
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self.first_stage_model.train = disabled_train
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for param in self.first_stage_model.parameters():
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param.requires_grad = False
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def instantiate_cond_stage(self, config):
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if not self.cond_stage_trainable:
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if config == "__is_first_stage__":
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logger.debug("Using first stage also as cond stage.")
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self.cond_stage_model = self.first_stage_model
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elif config == "__is_unconditional__":
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logger.debug(
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f"Training {self.__class__.__name__} as an unconditional model."
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)
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self.cond_stage_model = None
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# self.be_unconditional = True
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else:
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model = instantiate_from_config(config)
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self.cond_stage_model = model.eval()
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self.cond_stage_model.train = disabled_train
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for param in self.cond_stage_model.parameters():
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param.requires_grad = False
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else:
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assert config != "__is_first_stage__"
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assert config != "__is_unconditional__"
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model = instantiate_from_config(config)
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self.cond_stage_model = model
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def _get_denoise_row_from_list(
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self, samples, desc="", force_no_decoder_quantization=False
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):
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denoise_row = []
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for zd in tqdm(samples, desc=desc):
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denoise_row.append(
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self.decode_first_stage(
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zd.to(self.device), force_not_quantize=force_no_decoder_quantization
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)
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)
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n_imgs_per_row = len(denoise_row)
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denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
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denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")
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denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
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denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
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return denoise_grid
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def get_first_stage_encoding(self, encoder_posterior):
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if isinstance(encoder_posterior, DiagonalGaussianDistribution):
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z = encoder_posterior.sample()
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elif isinstance(encoder_posterior, torch.Tensor):
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z = encoder_posterior
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else:
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raise NotImplementedError(
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f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
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)
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return self.scale_factor * z
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def get_learned_conditioning(self, c):
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if self.cond_stage_forward is None:
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if hasattr(self.cond_stage_model, "encode") and callable(
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self.cond_stage_model.encode
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):
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c = self.cond_stage_model.encode(c)
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if isinstance(c, DiagonalGaussianDistribution):
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c = c.mode()
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else:
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c = self.cond_stage_model(c)
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else:
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assert hasattr(self.cond_stage_model, self.cond_stage_forward)
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c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
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return c
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def meshgrid(self, h, w):
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y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
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x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
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arr = torch.cat([y, x], dim=-1)
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return arr
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def delta_border(self, h, w):
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"""
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:param h: height
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:param w: width
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:return: normalized distance to image border,
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wtith min distance = 0 at border and max dist = 0.5 at image center
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"""
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lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
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arr = self.meshgrid(h, w) / lower_right_corner
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dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
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dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
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edge_dist = torch.min(
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torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
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)[0]
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return edge_dist
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def get_weighting(self, h, w, Ly, Lx, device):
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weighting = self.delta_border(h, w)
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weighting = torch.clip(
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weighting,
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self.split_input_params["clip_min_weight"],
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self.split_input_params["clip_max_weight"],
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)
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weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
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if self.split_input_params["tie_braker"]:
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L_weighting = self.delta_border(Ly, Lx)
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L_weighting = torch.clip(
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L_weighting,
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self.split_input_params["clip_min_tie_weight"],
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self.split_input_params["clip_max_tie_weight"],
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)
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L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
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weighting = weighting * L_weighting
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return weighting
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|
|
def get_fold_unfold(
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self, x, kernel_size, stride, uf=1, df=1
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): # todo load once not every time, shorten code
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"""
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:param x: img of size (bs, c, h, w)
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:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
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"""
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bs, nc, h, w = x.shape
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# number of crops in image
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Ly = (h - kernel_size[0]) // stride[0] + 1
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Lx = (w - kernel_size[1]) // stride[1] + 1
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if uf == 1 and df == 1:
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fold_params = dict(
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kernel_size=kernel_size, dilation=1, padding=0, stride=stride
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)
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unfold = torch.nn.Unfold(**fold_params)
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fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
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weighting = self.get_weighting(
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kernel_size[0], kernel_size[1], Ly, Lx, x.device
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).to(x.dtype)
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normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
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weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
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elif uf > 1 and df == 1:
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fold_params = dict(
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kernel_size=kernel_size, dilation=1, padding=0, stride=stride
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)
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unfold = torch.nn.Unfold(**fold_params)
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fold_params2 = dict(
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kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
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dilation=1,
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padding=0,
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stride=(stride[0] * uf, stride[1] * uf),
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)
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fold = torch.nn.Fold(
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output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
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)
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|
|
|
weighting = self.get_weighting(
|
|
kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
|
|
).to(x.dtype)
|
|
normalization = fold(weighting).view(
|
|
1, 1, h * uf, w * uf
|
|
) # normalizes the overlap
|
|
weighting = weighting.view(
|
|
(1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
|
|
)
|
|
|
|
elif df > 1 and uf == 1:
|
|
fold_params = dict(
|
|
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
|
|
)
|
|
unfold = torch.nn.Unfold(**fold_params)
|
|
|
|
fold_params2 = dict(
|
|
kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
|
|
dilation=1,
|
|
padding=0,
|
|
stride=(stride[0] // df, stride[1] // df),
|
|
)
|
|
fold = torch.nn.Fold(
|
|
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
|
|
)
|
|
|
|
weighting = self.get_weighting(
|
|
kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
|
|
).to(x.dtype)
|
|
normalization = fold(weighting).view(
|
|
1, 1, h // df, w // df
|
|
) # normalizes the overlap
|
|
weighting = weighting.view(
|
|
(1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
|
|
)
|
|
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
return fold, unfold, normalization, weighting
|
|
|
|
@torch.no_grad()
|
|
def get_input(
|
|
self,
|
|
batch,
|
|
k,
|
|
return_first_stage_outputs=False,
|
|
force_c_encode=False,
|
|
cond_key=None,
|
|
return_original_cond=False,
|
|
bs=None,
|
|
):
|
|
x = super().get_input(batch, k)
|
|
if bs is not None:
|
|
x = x[:bs]
|
|
x = x.to(self.device)
|
|
encoder_posterior = self.encode_first_stage(x)
|
|
z = self.get_first_stage_encoding(encoder_posterior).detach()
|
|
|
|
if self.model.conditioning_key is not None:
|
|
if cond_key is None:
|
|
cond_key = self.cond_stage_key
|
|
if cond_key != self.first_stage_key:
|
|
if cond_key in ["caption", "coordinates_bbox"]:
|
|
xc = batch[cond_key]
|
|
elif cond_key == "class_label":
|
|
xc = batch
|
|
else:
|
|
xc = super().get_input(batch, cond_key).to(self.device)
|
|
else:
|
|
xc = x
|
|
if not self.cond_stage_trainable or force_c_encode:
|
|
if isinstance(xc, dict) or isinstance(xc, list):
|
|
# import pudb; pudb.set_trace()
|
|
c = self.get_learned_conditioning(xc)
|
|
else:
|
|
c = self.get_learned_conditioning(xc.to(self.device))
|
|
else:
|
|
c = xc
|
|
if bs is not None:
|
|
c = c[:bs]
|
|
|
|
if self.use_positional_encodings:
|
|
pos_x, pos_y = self.compute_latent_shifts(batch)
|
|
ckey = __conditioning_keys__[self.model.conditioning_key]
|
|
c = {ckey: c, "pos_x": pos_x, "pos_y": pos_y}
|
|
|
|
else:
|
|
c = None
|
|
xc = None
|
|
if self.use_positional_encodings:
|
|
pos_x, pos_y = self.compute_latent_shifts(batch)
|
|
c = {"pos_x": pos_x, "pos_y": pos_y}
|
|
out = [z, c]
|
|
if return_first_stage_outputs:
|
|
xrec = self.decode_first_stage(z)
|
|
out.extend([x, xrec])
|
|
if return_original_cond:
|
|
out.append(xc)
|
|
return out
|
|
|
|
@torch.no_grad()
|
|
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
|
if predict_cids:
|
|
if z.dim() == 4:
|
|
z = torch.argmax(z.exp(), dim=1).long()
|
|
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
|
z = rearrange(z, "b h w c -> b c h w").contiguous()
|
|
|
|
z = 1.0 / self.scale_factor * z
|
|
|
|
return self.first_stage_model.decode(z)
|
|
|
|
@torch.no_grad()
|
|
def encode_first_stage(self, x):
|
|
if (
|
|
hasattr(self, "split_input_params")
|
|
and self.split_input_params["patch_distributed_vq"]
|
|
):
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
df = self.split_input_params["vqf"]
|
|
self.split_input_params["original_image_size"] = x.shape[-2:]
|
|
bs, nc, h, w = x.shape
|
|
if ks[0] > h or ks[1] > w:
|
|
ks = (min(ks[0], h), min(ks[1], w))
|
|
logger.info("reducing Kernel")
|
|
|
|
if stride[0] > h or stride[1] > w:
|
|
stride = (min(stride[0], h), min(stride[1], w))
|
|
logger.info("reducing stride")
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(
|
|
x, ks, stride, df=df
|
|
)
|
|
z = unfold(x) # (bn, nc * prod(**ks), L)
|
|
# Reshape to img shape
|
|
z = z.view(
|
|
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
|
|
) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
output_list = [
|
|
self.first_stage_model.encode(z[:, :, :, :, i])
|
|
for i in range(z.shape[-1])
|
|
]
|
|
|
|
o = torch.stack(output_list, axis=-1)
|
|
o = o * weighting
|
|
|
|
# Reverse reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
decoded = fold(o)
|
|
decoded = decoded / normalization
|
|
return decoded
|
|
|
|
return self.first_stage_model.encode(x)
|
|
|
|
def apply_model(self, x_noisy, t, cond, return_ids=False):
|
|
|
|
if isinstance(cond, dict):
|
|
# hybrid case, cond is exptected to be a dict
|
|
pass
|
|
else:
|
|
if not isinstance(cond, list):
|
|
cond = [cond]
|
|
key = (
|
|
"c_concat" if self.model.conditioning_key == "concat" else "c_crossattn"
|
|
)
|
|
cond = {key: cond}
|
|
|
|
if hasattr(self, "split_input_params"):
|
|
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
|
assert not return_ids
|
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
|
|
|
h, w = x_noisy.shape[-2:] # noqa
|
|
|
|
fold, unfold, normalization, weighting = self.get_fold_unfold(
|
|
x_noisy, ks, stride
|
|
)
|
|
|
|
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
|
|
# Reshape to img shape
|
|
z = z.view(
|
|
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
|
|
) # (bn, nc, ks[0], ks[1], L )
|
|
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
|
|
|
|
if (
|
|
self.cond_stage_key in ["image", "LR_image", "segmentation", "bbox_img"]
|
|
and self.model.conditioning_key
|
|
): # todo check for completeness
|
|
c_key = next(iter(cond.keys())) # get key
|
|
c = next(iter(cond.values())) # get value
|
|
assert len(c) == 1 # todo extend to list with more than one elem
|
|
c = c[0] # get element
|
|
|
|
c = unfold(c)
|
|
c = c.view(
|
|
(c.shape[0], -1, ks[0], ks[1], c.shape[-1])
|
|
) # (bn, nc, ks[0], ks[1], L )
|
|
|
|
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
|
|
|
|
elif self.cond_stage_key == "coordinates_bbox":
|
|
assert (
|
|
"original_image_size" in self.split_input_params
|
|
), "BoudingBoxRescaling is missing original_image_size"
|
|
|
|
# assuming padding of unfold is always 0 and its dilation is always 1
|
|
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
|
|
full_img_h, full_img_w = self.split_input_params["original_image_size"]
|
|
# as we are operating on latents, we need the factor from the original image size to the
|
|
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
|
|
num_downs = self.first_stage_model.encoder.num_resolutions - 1
|
|
rescale_latent = 2 ** (num_downs)
|
|
|
|
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
|
|
# need to rescale the tl patch coordinates to be in between (0,1)
|
|
tl_patch_coordinates = [
|
|
(
|
|
rescale_latent
|
|
* stride[0]
|
|
* (patch_nr % n_patches_per_row)
|
|
/ full_img_w,
|
|
rescale_latent
|
|
* stride[1]
|
|
* (patch_nr // n_patches_per_row)
|
|
/ full_img_h,
|
|
)
|
|
for patch_nr in range(z.shape[-1])
|
|
]
|
|
|
|
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
|
|
patch_limits = [
|
|
(
|
|
x_tl,
|
|
y_tl,
|
|
rescale_latent * ks[0] / full_img_w,
|
|
rescale_latent * ks[1] / full_img_h,
|
|
)
|
|
for x_tl, y_tl in tl_patch_coordinates
|
|
]
|
|
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
|
|
|
|
# tokenize crop coordinates for the bounding boxes of the respective patches
|
|
patch_limits_tknzd = [
|
|
torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[ # noqa
|
|
None
|
|
].to( # noqa
|
|
self.device
|
|
)
|
|
for bbox in patch_limits
|
|
] # list of length l with tensors of shape (1, 2)
|
|
|
|
# cut tknzd crop position from conditioning
|
|
assert isinstance(cond, dict), "cond must be dict to be fed into model"
|
|
cut_cond = cond["c_crossattn"][0][..., :-2].to(self.device)
|
|
|
|
adapted_cond = torch.stack(
|
|
[torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]
|
|
)
|
|
adapted_cond = rearrange(adapted_cond, "l b n -> (l b) n")
|
|
|
|
adapted_cond = self.get_learned_conditioning(adapted_cond)
|
|
|
|
adapted_cond = rearrange(
|
|
adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1]
|
|
)
|
|
|
|
cond_list = [{"c_crossattn": [e]} for e in adapted_cond]
|
|
|
|
else:
|
|
cond_list = [
|
|
cond for i in range(z.shape[-1])
|
|
] # Todo make this more efficient
|
|
|
|
# apply model by loop over crops
|
|
output_list = [
|
|
self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])
|
|
]
|
|
assert not isinstance(
|
|
output_list[0], tuple
|
|
) # todo cant deal with multiple model outputs check this never happens
|
|
|
|
o = torch.stack(output_list, axis=-1)
|
|
o = o * weighting
|
|
# Reverse reshape to img shape
|
|
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
|
# stitch crops together
|
|
x_recon = fold(o) / normalization
|
|
|
|
else:
|
|
x_recon = self.model(x_noisy, t, **cond)
|
|
|
|
if isinstance(x_recon, tuple) and not return_ids:
|
|
return x_recon[0]
|
|
|
|
return x_recon
|
|
|
|
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, _, _ = 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
|
|
if return_x0:
|
|
return model_mean, posterior_variance, posterior_log_variance, x_recon
|
|
|
|
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_x0:
|
|
model_mean, _, model_log_variance, x0 = outputs
|
|
else:
|
|
model_mean, _, model_log_variance = outputs
|
|
|
|
noise = noise_like(x.shape, device, repeat_noise) * temperature
|
|
if noise_dropout > 0.0:
|
|
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
|
# no noise when t == 0
|
|
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
|
|
|
if return_x0:
|
|
return (
|
|
model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
|
|
x0,
|
|
)
|
|
|
|
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
|
|
|
def q_sample(self, x_start, t, noise=None):
|
|
noise = (
|
|
noise
|
|
if noise is not None
|
|
else torch.randn_like(x_start, device="cpu").to(x_start.device)
|
|
)
|
|
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
|
|
)
|
|
|
|
|
|
class DiffusionWrapper(pl.LightningModule):
|
|
def __init__(self, diff_model_config, conditioning_key):
|
|
super().__init__()
|
|
self.diffusion_model = instantiate_from_config(diff_model_config)
|
|
self.conditioning_key = conditioning_key
|
|
assert self.conditioning_key in [None, "concat", "crossattn", "hybrid", "adm"]
|
|
|
|
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
|
|
if self.conditioning_key is None:
|
|
out = self.diffusion_model(x, t)
|
|
elif self.conditioning_key == "concat":
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
out = self.diffusion_model(xc, t)
|
|
elif self.conditioning_key == "crossattn":
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(x, t, context=cc)
|
|
elif self.conditioning_key == "hybrid":
|
|
xc = torch.cat([x] + c_concat, dim=1)
|
|
cc = torch.cat(c_crossattn, 1)
|
|
out = self.diffusion_model(xc, t, context=cc)
|
|
elif self.conditioning_key == "adm":
|
|
cc = c_crossattn[0]
|
|
out = self.diffusion_model(x, t, y=cc)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
return out
|