InvokeAI/ldm/models/diffusion/sampler.py

'''
ldm.models.diffusion.sampler

Base class for ldm.models.diffusion.ddim, ldm.models.diffusion.ksampler, etc

'''
from math import ceil

import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.invoke.devices import choose_torch_device

from ldm.modules.diffusionmodules.util import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
    noise_like,
    extract_into_tensor,
)

class Sampler(object):
    def __init__(self, model, schedule='linear', steps=None, device=None, **kwargs):
        self.model = model
        self.ddim_timesteps = None
        self.ddpm_num_timesteps = steps
        self.schedule = schedule
        self.device   = device or choose_torch_device()

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device(self.device):
                attr = attr.to(torch.float32).to(torch.device(self.device))
        setattr(self, name, attr)

    # This method was copied over from ddim.py and probably does stuff that is
    # ddim-specific. Disentangle at some point.
    def make_schedule(
            self,
            ddim_num_steps,
            ddim_discretize='uniform',
            ddim_eta=0.0,
            verbose=False,
    ):
        self.total_steps = ddim_num_steps
        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 stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
        # fast, but does not allow for exact reconstruction
        # t serves as an index to gather the correct alphas
        if use_original_steps:
            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
        else:
            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas

        if noise is None:
            noise = torch.randn_like(x0)
        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
            * noise
        )

    @torch.no_grad()
    def sample(
        self,
        S,          # S is steps
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,   # TODO: this is very confusing because it is called "step_callback" elsewhere. Change.
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=False,
        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,
    ):

        # check to see if make_schedule() has run, and if not, run it
        if self.ddim_timesteps is None:
            self.make_schedule(
                ddim_num_steps=S,
                ddim_eta = eta,
                verbose = False,
            )

        ts = self.get_timesteps(S)

        # sampling
        C, H, W = shape
        shape = (batch_size, C, H, W)
        samples, intermediates = self.do_sampling(
            conditioning,
            shape,
            timesteps=ts,
            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,
            steps=S,
        )
        return samples, intermediates

    #torch.no_grad()
    def do_sampling(
            self,
            cond,
            shape,
            timesteps=None,
            x_T=None,
            ddim_use_original_steps=False,
            callback=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,
            steps=None,
    ):
        b = shape[0]
        time_range = (
            list(reversed(range(0, timesteps)))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )

        total_steps=steps

        iterator = tqdm(
            time_range,
            desc=f'{self.__class__.__name__}',
            total=total_steps,
            dynamic_ncols=True,
        )
        old_eps = []
        self.prepare_to_sample(t_enc=total_steps)
        img = self.get_initial_image(x_T,shape,total_steps)

        # probably don't need this at all
        intermediates = {'x_inter': [img], 'pred_x0': [img]}

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full(
                (b,),
                step,
                device=self.device,
                dtype=torch.long
            )
            ts_next = torch.full(
                (b,),
                time_range[min(i + 1, len(time_range) - 1)],
                device=self.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(
                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(img,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

    # NOTE that decode() and sample() are almost the same code, and do the same thing.
    # The variable names are changed in order to be confusing.
    @torch.no_grad()
    def decode(
            self,
            x_latent,
            cond,
            t_start,
            img_callback=None,
            unconditional_guidance_scale=1.0,
            unconditional_conditioning=None,
            use_original_steps=False,
            init_latent       = None,
            mask              = None,
    ):

        timesteps = (
            np.arange(self.ddpm_num_timesteps)
            if use_original_steps
            else self.ddim_timesteps
        )
        timesteps = timesteps[:t_start]

        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f'>> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)')

        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
        x_dec    = x_latent
        x0       = init_latent
        self.prepare_to_sample(t_enc=total_steps)
        
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full(
                (x_latent.shape[0],),
                step,
                device=x_latent.device,
                dtype=torch.long,
            )

            ts_next = torch.full(
                (x_latent.shape[0],),
                time_range[min(i + 1, len(time_range) - 1)],
                device=self.device,
                dtype=torch.long,
            )

            if mask is not None:
                assert x0 is not None
                xdec_orig = self.q_sample(x0, ts)  # TODO: deterministic forward pass?
                x_dec = xdec_orig * mask + (1.0 - mask) * x_dec

            outs = self.p_sample(
                x_dec,
                cond,
                ts,
                index=index,
                use_original_steps=use_original_steps,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                t_next = ts_next,
            )
            
            x_dec, pred_x0, e_t = outs
            if img_callback:
                img_callback(x_dec,i)

        return x_dec

    def get_initial_image(self,x_T,shape,timesteps=None):
        if x_T is None:
            return torch.randn(shape, device=self.device)
        else:
            return x_T
    
    def p_sample(
            self,
            img,
            cond,
            ts,
            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,
            steps=None,
    ):
        raise NotImplementedError("p_sample() must be implemented in a descendent class")

    def prepare_to_sample(self,t_enc,**kwargs):
        '''
        Hook that will be called right before the very first invocation of p_sample()
        to allow subclass to do additional initialization. t_enc corresponds to the actual
        number of steps that will be run, and may be less than total steps if img2img is
        active.
        '''
        pass

    def get_timesteps(self,ddim_steps):
        '''
        The ddim and plms samplers work on timesteps. This method is called after
        ddim_timesteps are created in make_schedule(), and selects the portion of
        timesteps that will be used for sampling, depending on the t_enc in img2img.
        '''
        return self.ddim_timesteps[:ddim_steps]
    
    def q_sample(self,x0,ts):
        '''
        Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to 
        return self.model.inner_model.q_sample(x0,ts)
        '''
        return self.model.q_sample(x0,ts)


    @classmethod
    def apply_weighted_conditioning_list(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
        x_in = torch.cat([x] * 2)
        t_in = torch.cat([t] * 2) # aka sigmas

        deltas = None
        uncond_latents = None
        weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]

        # below is fugly omg
        num_actual_conditionings = len(c_or_weighted_c_list)
        conditionings = [uc] + [c for c,weight in weighted_cond_list]
        weights = [1] + [weight for c,weight in weighted_cond_list]
        chunk_count = ceil(len(conditionings)/2)
        assert(len(conditionings)>=2, "need at least one uncond and one cond")
        deltas = None
        for chunk_index in range(chunk_count):
            offset = chunk_index*2
            chunk_size = min(2, len(conditionings)-offset)

            if chunk_size == 1:
                c_in = conditionings[offset]
                latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
                latents_b = None
            else:
                c_in = torch.cat(conditionings[offset:offset+2])
                latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)

            # first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
            if chunk_index == 0:
                uncond_latents = latents_a
                deltas = latents_b - uncond_latents
            else:
                deltas = torch.cat((deltas, latents_a - uncond_latents))
                if latents_b is not None:
                    deltas = torch.cat((deltas, latents_b - uncond_latents))

        # merge the weighted deltas together into a single merged delta
        per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
        normalize = False
        if normalize:
            per_delta_weights /= torch.sum(per_delta_weights)
        reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
        deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)

        # old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
        # assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))

        return uncond_latents + deltas_merged * global_guidance_scale