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"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize,
num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,
verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
steps,
shape,
x_info,
c_info,
eta=0.,
temperature=1.,
noise_dropout=0.,
verbose=True,
log_every_t=100,):
self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
print(f'Data shape for DDIM sampling is {shape}, eta {eta}')
samples, intermediates = self.ddim_sampling(
shape,
x_info=x_info,
c_info=c_info,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,)
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self,
shape,
x_info,
c_info,
noise_dropout=0.,
temperature=1.,
log_every_t=100,):
device = self.model.device
dtype = c_info['conditioning'].dtype
bs = shape[0]
timesteps = self.ddim_timesteps
if ('xt' in x_info) and (x_info['xt'] is not None):
xt = x_info['xt'].astype(dtype).to(device)
x_info['x'] = xt
elif ('x0' in x_info) and (x_info['x0'] is not None):
x0 = x_info['x0'].type(dtype).to(device)
ts = timesteps[x_info['x0_forward_timesteps']].repeat(bs)
ts = torch.Tensor(ts).long().to(device)
timesteps = timesteps[:x_info['x0_forward_timesteps']]
x0_nz = self.model.q_sample(x0, ts)
x_info['x'] = x0_nz
else:
x_info['x'] = torch.randn(shape, device=device, dtype=dtype)
intermediates = {'pred_xt': [], 'pred_x0': []}
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((bs,), step, device=device, dtype=torch.long)
outs = self.p_sample_ddim(
x_info, c_info, ts, index,
noise_dropout=noise_dropout,
temperature=temperature,)
pred_xt, pred_x0 = outs
x_info['x'] = pred_xt
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['pred_xt'].append(pred_xt)
intermediates['pred_x0'].append(pred_x0)
return pred_xt, intermediates
@torch.no_grad()
def p_sample_ddim(self, x_info, c_info, t, index,
repeat_noise=False,
use_original_steps=False,
noise_dropout=0.,
temperature=1.,):
x = x_info['x']
unconditional_guidance_scale = c_info['unconditional_guidance_scale']
b, *_, device = *x.shape, x.device
if unconditional_guidance_scale == 1.:
c_info['c'] = c_info['conditioning']
e_t = self.model.apply_model(x_info, t, c_info)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([c_info['unconditional_conditioning'], c_info['conditioning']])
x_info['x'] = x_in
c_info['c'] = c_in
e_t_uncond, e_t = self.model.apply_model(x_info, t_in, c_info).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
extended_shape = [b] + [1]*(len(e_t.shape)-1)
a_t = torch.full(extended_shape, alphas[index], device=device, dtype=x.dtype)
a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=x.dtype)
sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=x.dtype)
sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=x.dtype)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@torch.no_grad()
def sample_multicontext(self,
steps,
shape,
x_info,
c_info_list,
eta=0.,
temperature=1.,
noise_dropout=0.,
verbose=True,
log_every_t=100,):
self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
print(f'Data shape for DDIM sampling is {shape}, eta {eta}')
samples, intermediates = self.ddim_sampling_multicontext(
shape,
x_info=x_info,
c_info_list=c_info_list,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,)
return samples, intermediates
@torch.no_grad()
def ddim_sampling_multicontext(self,
shape,
x_info,
c_info_list,
noise_dropout=0.,
temperature=1.,
log_every_t=100,):
device = self.model.device
dtype = c_info_list[0]['conditioning'].dtype
bs = shape[0]
timesteps = self.ddim_timesteps
if ('xt' in x_info) and (x_info['xt'] is not None):
xt = x_info['xt'].astype(dtype).to(device)
x_info['x'] = xt
elif ('x0' in x_info) and (x_info['x0'] is not None):
x0 = x_info['x0'].type(dtype).to(device)
ts = timesteps[x_info['x0_forward_timesteps']].repeat(bs)
ts = torch.Tensor(ts).long().to(device)
timesteps = timesteps[:x_info['x0_forward_timesteps']]
x0_nz = self.model.q_sample(x0, ts)
x_info['x'] = x0_nz
else:
x_info['x'] = torch.randn(shape, device=device, dtype=dtype)
intermediates = {'pred_xt': [], 'pred_x0': []}
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((bs,), step, device=device, dtype=torch.long)
outs = self.p_sample_ddim_multicontext(
x_info, c_info_list, ts, index,
noise_dropout=noise_dropout,
temperature=temperature,)
pred_xt, pred_x0 = outs
x_info['x'] = pred_xt
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['pred_xt'].append(pred_xt)
intermediates['pred_x0'].append(pred_x0)
return pred_xt, intermediates
@torch.no_grad()
def p_sample_ddim_multicontext(
self, x_info, c_info_list, t, index,
repeat_noise=False,
use_original_steps=False,
noise_dropout=0.,
temperature=1.,):
x = x_info['x']
b, *_, device = *x.shape, x.device
unconditional_guidance_scale = None
for c_info in c_info_list:
if unconditional_guidance_scale is None:
unconditional_guidance_scale = c_info['unconditional_guidance_scale']
else:
assert unconditional_guidance_scale==c_info['unconditional_guidance_scale'], \
"A different unconditional guidance scale between different context is not allowed!"
if unconditional_guidance_scale == 1.:
c_info['c'] = c_info['conditioning']
else:
c_in = torch.cat([c_info['unconditional_conditioning'], c_info['conditioning']])
c_info['c'] = c_in
if unconditional_guidance_scale == 1.:
e_t = self.model.apply_model_multicontext(x_info, t, c_info_list)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
x_info['x'] = x_in
e_t_uncond, e_t = self.model.apply_model_multicontext(x_info, t_in, c_info_list).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
extended_shape = [b] + [1]*(len(e_t.shape)-1)
a_t = torch.full(extended_shape, alphas[index], device=device, dtype=x.dtype)
a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=x.dtype)
sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=x.dtype)
sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=x.dtype)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
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