stable-diffusion-webui/modules/sd_hijack_inpainting.py

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