mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
104 lines
4.5 KiB
Python
104 lines
4.5 KiB
Python
import os
|
|
import torch
|
|
|
|
from einops import repeat
|
|
from omegaconf import ListConfig
|
|
|
|
import ldm.models.diffusion.ddpm
|
|
import ldm.models.diffusion.ddim
|
|
import ldm.models.diffusion.plms
|
|
|
|
from ldm.models.diffusion.ddpm import LatentDiffusion
|
|
from ldm.models.diffusion.plms import PLMSSampler
|
|
from ldm.models.diffusion.ddim import DDIMSampler, noise_like
|
|
from ldm.models.diffusion.sampling_util import norm_thresholding
|
|
|
|
|
|
@torch.no_grad()
|
|
def p_sample_plms(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, old_eps=None, t_next=None, dynamic_threshold=None):
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
def get_model_output(x, t):
|
|
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
|
e_t = 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]])
|
|
else:
|
|
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)
|
|
if dynamic_threshold is not None:
|
|
pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
|
|
# 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
|
|
|
|
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
|
|
|
|
|
|
def do_inpainting_hijack():
|
|
# p_sample_plms is needed because PLMS can't work with dicts as conditionings
|
|
|
|
ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
|