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"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from audioldm.latent_diffusion.util import (
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
extract_into_tensor,
)
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=0.0, 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(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod",
to_torch(np.sqrt(1.0 / 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(1.0 - 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=0.0,
mask=None,
x0=None,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs,
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != 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)
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,
)
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=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=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 = gr.Progress().tqdm(time_range, desc="DDIM Sampler", total=total_steps)
iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps, leave=False)
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 + (1.0 - mask) * img
) # In the first sampling step, img is pure gaussian noise
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,
)
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 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,
):
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 = gr.Progress().tqdm(time_range, desc="Decoding image", total=total_steps)
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,
)
return x_dec
@torch.no_grad()
def p_sample_ddim(
self,
x,
c,
t,
index,
repeat_noise=False,
use_original_steps=False,
quantize_denoised=False,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
):
b, *_, device = *x.shape, x.device
if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
# When unconditional_guidance_scale == 1: only e_t
# When unconditional_guidance_scale == 0: only unconditional
# When unconditional_guidance_scale > 1: add more unconditional guidance
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
if score_corrector is not None:
assert self.model.parameterization == "eps"
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
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise # TODO
return x_prev, pred_x0