stable-diffusion-webui-master/modules/sd_hijack_inpainting.py

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

# =================================================================================================
# Monkey patch DDIMSampler methods from RunwayML repo directly.
# Adapted from:
# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
# =================================================================================================
@torch.no_grad()
def sample_ddim(self,
            S,
            batch_size,
            shape,
            conditioning=None,
            callback=None,
            normals_sequence=None,
            img_callback=None,
            quantize_x0=False,
            eta=0.,
            mask=None,
            x0=None,
            temperature=1.,
            noise_dropout=0.,
            score_corrector=None,
            corrector_kwargs=None,
            verbose=True,
            x_T=None,
            log_every_t=100,
            unconditional_guidance_scale=1.,
            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):
            ctmp = conditioning[list(conditioning.keys())[0]]
            while isinstance(ctmp, list):
                ctmp = ctmp[0]
            cbs = ctmp.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 DDIM sampling is {size}, eta {eta}')

    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 p_sample_ddim(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):
    b, *_, device = *x.shape, x.device

    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)

    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. - 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


# =================================================================================================
# Monkey patch PLMSSampler methods.
# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
# Adapted from:
# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
# =================================================================================================
@torch.no_grad()
def sample_plms(self,
            S,
            batch_size,
            shape,
            conditioning=None,
            callback=None,
            normals_sequence=None,
            img_callback=None,
            quantize_x0=False,
            eta=0.,
            mask=None,
            x0=None,
            temperature=1.,
            noise_dropout=0.,
            score_corrector=None,
            corrector_kwargs=None,
            verbose=True,
            x_T=None,
            log_every_t=100,
            unconditional_guidance_scale=1.,
            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):
            ctmp = conditioning[list(conditioning.keys())[0]]
            while isinstance(ctmp, list):
                ctmp = ctmp[0]
            cbs = ctmp.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 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
    
# =================================================================================================
# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
# Adapted from:
# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
# =================================================================================================

@torch.no_grad()
def get_unconditional_conditioning(self, batch_size, null_label=None):
    if null_label is not None:
        xc = null_label
        if isinstance(xc, ListConfig):
            xc = list(xc)
        if isinstance(xc, dict) or isinstance(xc, list):
            c = self.get_learned_conditioning(xc)
        else:
            if hasattr(xc, "to"):
                xc = xc.to(self.device)
            c = self.get_learned_conditioning(xc)
    else:
        # todo: get null label from cond_stage_model
        raise NotImplementedError()
    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
    return c


class LatentInpaintDiffusion(LatentDiffusion):
    def __init__(
        self,
        concat_keys=("mask", "masked_image"),
        masked_image_key="masked_image",
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.masked_image_key = masked_image_key
        assert self.masked_image_key in concat_keys
        self.concat_keys = concat_keys


def should_hijack_inpainting(checkpoint_info):
    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")


def do_inpainting_hijack():
    # most of this stuff seems to no longer be needed because it is already included into SD2.0
    # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
    # this file should be cleaned up later if weverything tuens out to work fine

    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion

    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim

    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms