mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
404 lines
13 KiB
Python
404 lines
13 KiB
Python
"""SAMPLING ONLY."""
|
|
|
|
import torch
|
|
import numpy as np
|
|
from tqdm import tqdm
|
|
from functools import partial
|
|
|
|
from ldm.modules.diffusionmodules.util import (
|
|
make_ddim_sampling_parameters,
|
|
make_ddim_timesteps,
|
|
noise_like,
|
|
)
|
|
|
|
|
|
class PLMSSampler(object):
|
|
def __init__(self, model, schedule='linear', device='cuda', **kwargs):
|
|
super().__init__()
|
|
self.model = model
|
|
self.ddpm_num_timesteps = model.num_timesteps
|
|
self.schedule = schedule
|
|
self.device = device
|
|
|
|
def register_buffer(self, name, attr):
|
|
if type(attr) == torch.Tensor:
|
|
if attr.device != torch.device(self.device):
|
|
attr = attr.to(torch.device(self.device))
|
|
|
|
setattr(self, name, attr)
|
|
|
|
def make_schedule(
|
|
self,
|
|
ddim_num_steps,
|
|
ddim_discretize='uniform',
|
|
ddim_eta=0.0,
|
|
verbose=True,
|
|
):
|
|
if ddim_eta != 0:
|
|
raise ValueError('ddim_eta must be 0 for PLMS')
|
|
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)
|
|
# print(f'Data shape for PLMS sampling is {size}')
|
|
|
|
samples, intermediates = self.plms_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 plms_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 = (
|
|
list(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 PLMS Sampling with {total_steps} timesteps")
|
|
|
|
iterator = tqdm(
|
|
time_range,
|
|
desc='PLMS Sampler',
|
|
total=total_steps,
|
|
dynamic_ncols=True,
|
|
)
|
|
old_eps = []
|
|
|
|
for i, step in enumerate(iterator):
|
|
index = total_steps - i - 1
|
|
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
|
ts_next = torch.full(
|
|
(b,),
|
|
time_range[min(i + 1, len(time_range) - 1)],
|
|
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
|
|
|
|
outs = self.p_sample_plms(
|
|
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,
|
|
old_eps=old_eps,
|
|
t_next=ts_next,
|
|
)
|
|
img, pred_x0, e_t = outs
|
|
old_eps.append(e_t)
|
|
if len(old_eps) >= 4:
|
|
old_eps.pop(0)
|
|
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_plms(
|
|
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,
|
|
old_eps=None,
|
|
t_next=None,
|
|
):
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
def get_model_output(x, t):
|
|
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)
|
|
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
|
|
)
|
|
|
|
return e_t
|
|
|
|
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
|
|
)
|
|
|
|
def get_x_prev_and_pred_x0(e_t, index):
|
|
# 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
|
|
return x_prev, pred_x0
|
|
|
|
e_t = get_model_output(x, t)
|
|
if len(old_eps) == 0:
|
|
# Pseudo Improved Euler (2nd order)
|
|
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
|
|
e_t_next = get_model_output(x_prev, t_next)
|
|
e_t_prime = (e_t + e_t_next) / 2
|
|
elif len(old_eps) == 1:
|
|
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (3 * e_t - old_eps[-1]) / 2
|
|
elif len(old_eps) == 2:
|
|
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
|
|
elif len(old_eps) >= 3:
|
|
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (
|
|
55 * e_t
|
|
- 59 * old_eps[-1]
|
|
+ 37 * old_eps[-2]
|
|
- 9 * old_eps[-3]
|
|
) / 24
|
|
|
|
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
|
|
|
|
return x_prev, pred_x0, e_t
|