2022-11-24 00:22:28 +00:00
|
|
|
"""SAMPLING ONLY."""
|
|
|
|
|
|
|
|
import torch
|
|
|
|
import numpy as np
|
|
|
|
from tqdm import tqdm
|
|
|
|
|
|
|
|
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
|
|
|
|
|
|
|
|
|
|
|
|
class DDIMSampler(object):
|
2022-12-20 15:18:33 +00:00
|
|
|
def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
|
2022-11-24 00:22:28 +00:00
|
|
|
super().__init__()
|
|
|
|
self.model = model
|
|
|
|
self.ddpm_num_timesteps = model.num_timesteps
|
|
|
|
self.schedule = schedule
|
2022-12-20 15:18:33 +00:00
|
|
|
self.device = device
|
2022-11-24 00:22:28 +00:00
|
|
|
|
|
|
|
def register_buffer(self, name, attr):
|
|
|
|
if type(attr) == torch.Tensor:
|
2022-12-20 15:18:33 +00:00
|
|
|
if attr.device != self.device:
|
|
|
|
attr = attr.to(self.device)
|
2022-11-24 00:22:28 +00:00
|
|
|
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,
|
|
|
|
S,
|
|
|
|
batch_size,
|
|
|
|
shape,
|
|
|
|
conditioning=None,
|
|
|
|
callback=None,
|
|
|
|
normals_sequence=None,
|
|
|
|
img_callback=None,
|
|
|
|
quantize_x0=False,
|
|
|
|
eta=0.,
|
|
|
|
mask=None,
|
|
|
|
x0=None,
|
|
|
|
temperature=1.,
|
|
|
|
noise_dropout=0.,
|
|
|
|
score_corrector=None,
|
|
|
|
corrector_kwargs=None,
|
|
|
|
verbose=True,
|
|
|
|
x_T=None,
|
|
|
|
log_every_t=100,
|
|
|
|
unconditional_guidance_scale=1.,
|
|
|
|
unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
|
|
|
dynamic_threshold=None,
|
|
|
|
ucg_schedule=None,
|
|
|
|
**kwargs
|
|
|
|
):
|
|
|
|
if conditioning is not None:
|
|
|
|
if isinstance(conditioning, dict):
|
|
|
|
ctmp = conditioning[list(conditioning.keys())[0]]
|
|
|
|
while isinstance(ctmp, list): ctmp = ctmp[0]
|
|
|
|
cbs = ctmp.shape[0]
|
|
|
|
if cbs != batch_size:
|
|
|
|
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
|
|
|
|
|
|
|
elif isinstance(conditioning, list):
|
|
|
|
for ctmp in conditioning:
|
|
|
|
if ctmp.shape[0] != batch_size:
|
|
|
|
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
|
|
|
|
|
|
|
else:
|
|
|
|
if conditioning.shape[0] != batch_size:
|
|
|
|
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
|
|
|
|
|
|
|
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
|
|
|
# sampling
|
|
|
|
C, H, W = shape
|
|
|
|
size = (batch_size, C, H, W)
|
|
|
|
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
|
|
|
|
|
|
|
samples, intermediates = self.ddim_sampling(conditioning, size,
|
|
|
|
callback=callback,
|
|
|
|
img_callback=img_callback,
|
|
|
|
quantize_denoised=quantize_x0,
|
|
|
|
mask=mask, x0=x0,
|
|
|
|
ddim_use_original_steps=False,
|
|
|
|
noise_dropout=noise_dropout,
|
|
|
|
temperature=temperature,
|
|
|
|
score_corrector=score_corrector,
|
|
|
|
corrector_kwargs=corrector_kwargs,
|
|
|
|
x_T=x_T,
|
|
|
|
log_every_t=log_every_t,
|
|
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
|
|
dynamic_threshold=dynamic_threshold,
|
|
|
|
ucg_schedule=ucg_schedule
|
|
|
|
)
|
|
|
|
return samples, intermediates
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
|
|
def ddim_sampling(self, cond, shape,
|
|
|
|
x_T=None, ddim_use_original_steps=False,
|
|
|
|
callback=None, timesteps=None, quantize_denoised=False,
|
|
|
|
mask=None, x0=None, img_callback=None, log_every_t=100,
|
|
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
|
|
|
|
ucg_schedule=None):
|
|
|
|
device = self.model.betas.device
|
|
|
|
b = shape[0]
|
|
|
|
if x_T is None:
|
|
|
|
img = torch.randn(shape, device=device)
|
|
|
|
else:
|
|
|
|
img = x_T
|
|
|
|
|
|
|
|
if timesteps is None:
|
|
|
|
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
|
|
|
elif timesteps is not None and not ddim_use_original_steps:
|
|
|
|
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
|
|
|
timesteps = self.ddim_timesteps[:subset_end]
|
|
|
|
|
|
|
|
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
|
|
|
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
|
|
|
|
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
|
|
|
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
|
|
|
|
|
|
|
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
|
|
|
|
|
|
|
|
for i, step in enumerate(iterator):
|
|
|
|
index = total_steps - i - 1
|
|
|
|
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
|
|
|
|
|
|
|
if mask is not None:
|
|
|
|
assert x0 is not None
|
|
|
|
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
|
|
|
|
img = img_orig * mask + (1. - mask) * img
|
|
|
|
|
|
|
|
if ucg_schedule is not None:
|
|
|
|
assert len(ucg_schedule) == len(time_range)
|
|
|
|
unconditional_guidance_scale = ucg_schedule[i]
|
|
|
|
|
|
|
|
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
|
|
|
quantize_denoised=quantize_denoised, temperature=temperature,
|
|
|
|
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
|
|
|
corrector_kwargs=corrector_kwargs,
|
|
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
|
|
dynamic_threshold=dynamic_threshold)
|
|
|
|
img, pred_x0 = outs
|
|
|
|
if callback: callback(i)
|
|
|
|
if img_callback: img_callback(pred_x0, i)
|
|
|
|
|
|
|
|
if index % log_every_t == 0 or index == total_steps - 1:
|
|
|
|
intermediates['x_inter'].append(img)
|
|
|
|
intermediates['pred_x0'].append(pred_x0)
|
|
|
|
|
|
|
|
return img, intermediates
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
|
|
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None,
|
|
|
|
dynamic_threshold=None):
|
|
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
|
|
|
|
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
|
|
|
model_output = self.model.apply_model(x, t, c)
|
|
|
|
else:
|
|
|
|
x_in = torch.cat([x] * 2)
|
|
|
|
t_in = torch.cat([t] * 2)
|
|
|
|
if isinstance(c, dict):
|
|
|
|
assert isinstance(unconditional_conditioning, dict)
|
|
|
|
c_in = dict()
|
|
|
|
for k in c:
|
|
|
|
if isinstance(c[k], list):
|
|
|
|
c_in[k] = [torch.cat([
|
|
|
|
unconditional_conditioning[k][i],
|
|
|
|
c[k][i]]) for i in range(len(c[k]))]
|
|
|
|
else:
|
|
|
|
c_in[k] = torch.cat([
|
|
|
|
unconditional_conditioning[k],
|
|
|
|
c[k]])
|
|
|
|
elif isinstance(c, list):
|
|
|
|
c_in = list()
|
|
|
|
assert isinstance(unconditional_conditioning, list)
|
|
|
|
for i in range(len(c)):
|
|
|
|
c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
|
|
|
|
else:
|
|
|
|
c_in = torch.cat([unconditional_conditioning, c])
|
|
|
|
model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
|
|
|
|
model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
|
|
|
|
|
|
|
|
if self.model.parameterization == "v":
|
|
|
|
e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
|
|
|
|
else:
|
|
|
|
e_t = model_output
|
|
|
|
|
|
|
|
if score_corrector is not None:
|
|
|
|
assert self.model.parameterization == "eps", 'not implemented'
|
|
|
|
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
|
|
|
|
|
|
|
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
|
|
|
|
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
|
|
|
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
|
|
|
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
|
|
|
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
|
|
|
|
|
|
|
# current prediction for x_0
|
|
|
|
if self.model.parameterization != "v":
|
|
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
|
|
else:
|
|
|
|
pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
|
|
|
|
|
|
|
|
if quantize_denoised:
|
|
|
|
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
|
|
|
|
|
|
|
if dynamic_threshold is not None:
|
|
|
|
raise NotImplementedError()
|
|
|
|
|
|
|
|
# direction pointing to x_t
|
|
|
|
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
|
|
|
noise = sigma_t * noise_like(x.shape, device, 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 encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
|
|
|
|
unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
|
|
|
|
num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
|
|
|
|
|
|
|
|
assert t_enc <= num_reference_steps
|
|
|
|
num_steps = t_enc
|
|
|
|
|
|
|
|
if use_original_steps:
|
|
|
|
alphas_next = self.alphas_cumprod[:num_steps]
|
|
|
|
alphas = self.alphas_cumprod_prev[:num_steps]
|
|
|
|
else:
|
|
|
|
alphas_next = self.ddim_alphas[:num_steps]
|
|
|
|
alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
|
|
|
|
|
|
|
|
x_next = x0
|
|
|
|
intermediates = []
|
|
|
|
inter_steps = []
|
|
|
|
for i in tqdm(range(num_steps), desc='Encoding Image'):
|
|
|
|
t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
|
|
|
|
if unconditional_guidance_scale == 1.:
|
|
|
|
noise_pred = self.model.apply_model(x_next, t, c)
|
|
|
|
else:
|
|
|
|
assert unconditional_conditioning is not None
|
|
|
|
e_t_uncond, noise_pred = torch.chunk(
|
|
|
|
self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
|
|
|
|
torch.cat((unconditional_conditioning, c))), 2)
|
|
|
|
noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
|
|
|
|
|
|
|
|
xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
|
|
|
|
weighted_noise_pred = alphas_next[i].sqrt() * (
|
|
|
|
(1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
|
|
|
|
x_next = xt_weighted + weighted_noise_pred
|
|
|
|
if return_intermediates and i % (
|
|
|
|
num_steps // return_intermediates) == 0 and i < num_steps - 1:
|
|
|
|
intermediates.append(x_next)
|
|
|
|
inter_steps.append(i)
|
|
|
|
elif return_intermediates and i >= num_steps - 2:
|
|
|
|
intermediates.append(x_next)
|
|
|
|
inter_steps.append(i)
|
|
|
|
if callback: callback(i)
|
|
|
|
|
|
|
|
out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
|
|
|
|
if return_intermediates:
|
|
|
|
out.update({'intermediates': intermediates})
|
|
|
|
return x_next, out
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
|
|
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
|
|
|
|
# fast, but does not allow for exact reconstruction
|
|
|
|
# t serves as an index to gather the correct alphas
|
|
|
|
if use_original_steps:
|
|
|
|
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
|
|
|
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
|
|
|
|
else:
|
|
|
|
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
|
|
|
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
|
|
|
|
|
|
|
if noise is None:
|
|
|
|
noise = torch.randn_like(x0)
|
|
|
|
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
|
|
|
|
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
|
|
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
|
|
|
|
use_original_steps=False, callback=None):
|
|
|
|
|
|
|
|
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
|
|
|
|
timesteps = timesteps[:t_start]
|
|
|
|
|
|
|
|
time_range = np.flip(timesteps)
|
|
|
|
total_steps = timesteps.shape[0]
|
|
|
|
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
|
|
|
|
|
|
|
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
|
|
|
x_dec = x_latent
|
|
|
|
for i, step in enumerate(iterator):
|
|
|
|
index = total_steps - i - 1
|
|
|
|
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
|
|
|
|
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
|
|
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
|
|
unconditional_conditioning=unconditional_conditioning)
|
|
|
|
if callback: callback(i)
|
|
|
|
return x_dec
|