179 lines
7.3 KiB
Python
179 lines
7.3 KiB
Python
import torch
|
|
import tqdm
|
|
import k_diffusion.sampling
|
|
import numpy as np
|
|
|
|
from modules import shared
|
|
from modules.models.diffusion.uni_pc import uni_pc
|
|
from modules.torch_utils import float64
|
|
|
|
|
|
@torch.no_grad()
|
|
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
|
|
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
|
alphas = alphas_cumprod[timesteps]
|
|
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
|
|
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
|
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
|
|
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones((x.shape[0]))
|
|
s_x = x.new_ones((x.shape[0], 1, 1, 1))
|
|
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
|
|
index = len(timesteps) - 1 - i
|
|
|
|
e_t = model(x, timesteps[index].item() * s_in, **extra_args)
|
|
|
|
a_t = alphas[index].item() * s_x
|
|
a_prev = alphas_prev[index].item() * s_x
|
|
sigma_t = sigmas[index].item() * s_x
|
|
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
|
|
|
|
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 * k_diffusion.sampling.torch.randn_like(x)
|
|
x = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
|
|
|
if callback is not None:
|
|
callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
|
|
|
|
return x
|
|
|
|
|
|
@torch.no_grad()
|
|
def ddim_cfgpp(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
|
|
""" Implements CFG++: Manifold-constrained Classifier Free Guidance For Diffusion Models (2024).
|
|
Uses the unconditional noise prediction instead of the conditional noise to guide the denoising direction.
|
|
The CFG scale is divided by 12.5 to map CFG from [0.0, 12.5] to [0, 1.0].
|
|
"""
|
|
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
|
alphas = alphas_cumprod[timesteps]
|
|
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
|
|
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
|
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
|
|
|
|
model.cond_scale_miltiplier = 1 / 12.5
|
|
model.need_last_noise_uncond = True
|
|
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones((x.shape[0]))
|
|
s_x = x.new_ones((x.shape[0], 1, 1, 1))
|
|
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
|
|
index = len(timesteps) - 1 - i
|
|
|
|
e_t = model(x, timesteps[index].item() * s_in, **extra_args)
|
|
last_noise_uncond = model.last_noise_uncond
|
|
|
|
a_t = alphas[index].item() * s_x
|
|
a_prev = alphas_prev[index].item() * s_x
|
|
sigma_t = sigmas[index].item() * s_x
|
|
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
|
|
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * last_noise_uncond
|
|
noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
|
|
x = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
|
|
|
if callback is not None:
|
|
callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
|
|
|
|
return x
|
|
|
|
|
|
@torch.no_grad()
|
|
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
|
|
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
|
alphas = alphas_cumprod[timesteps]
|
|
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
|
|
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
|
|
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones([x.shape[0]])
|
|
s_x = x.new_ones((x.shape[0], 1, 1, 1))
|
|
old_eps = []
|
|
|
|
def get_x_prev_and_pred_x0(e_t, index):
|
|
# select parameters corresponding to the currently considered timestep
|
|
a_t = alphas[index].item() * s_x
|
|
a_prev = alphas_prev[index].item() * s_x
|
|
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
|
|
|
|
# current prediction for x_0
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
|
|
# direction pointing to x_t
|
|
dir_xt = (1. - a_prev).sqrt() * e_t
|
|
x_prev = a_prev.sqrt() * pred_x0 + dir_xt
|
|
return x_prev, pred_x0
|
|
|
|
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
|
|
index = len(timesteps) - 1 - i
|
|
ts = timesteps[index].item() * s_in
|
|
t_next = timesteps[max(index - 1, 0)].item() * s_in
|
|
|
|
e_t = model(x, ts, **extra_args)
|
|
|
|
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 = model(x_prev, t_next, **extra_args)
|
|
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
|
|
else:
|
|
# 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)
|
|
|
|
old_eps.append(e_t)
|
|
if len(old_eps) >= 4:
|
|
old_eps.pop(0)
|
|
|
|
x = x_prev
|
|
|
|
if callback is not None:
|
|
callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
|
|
|
|
return x
|
|
|
|
|
|
class UniPCCFG(uni_pc.UniPC):
|
|
def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
|
|
super().__init__(None, *args, **kwargs)
|
|
|
|
def after_update(x, model_x):
|
|
callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
|
|
self.index += 1
|
|
|
|
self.cfg_model = cfg_model
|
|
self.extra_args = extra_args
|
|
self.callback = callback
|
|
self.index = 0
|
|
self.after_update = after_update
|
|
|
|
def get_model_input_time(self, t_continuous):
|
|
return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
|
|
|
|
def model(self, x, t):
|
|
t_input = self.get_model_input_time(t)
|
|
|
|
res = self.cfg_model(x, t_input, **self.extra_args)
|
|
|
|
return res
|
|
|
|
|
|
def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
|
|
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
|
|
|
|
ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
|
|
t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None # this is likely off by a bit - if someone wants to fix it please by all means
|
|
unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
|
|
x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
|
|
|
|
return x
|