VADER-VideoCrafter/lvdm/models/samplers/ddim.py

# Adapted from VideoCrafter: https://github.com/AILab-CVC/VideoCrafter
import numpy as np
from tqdm import tqdm
import torch
from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps
from lvdm.common import noise_like
import random
# import ipdb
# st = ipdb.set_trace


class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule
        self.counter = 0
        self.backprop_mode = 'last' # default
        self.training_mode = False  # default

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

    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
        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))
        self.use_scale = self.model.use_scale
        # print('DDIM scale', self.use_scale)

        if self.use_scale:
            self.register_buffer('scale_arr', to_torch(self.model.scale_arr))
            ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps]
            self.register_buffer('ddim_scale_arr', ddim_scale_arr)
            ddim_scale_arr = np.asarray([self.scale_arr.cpu()[0]] + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist())
            self.register_buffer('ddim_scale_arr_prev', ddim_scale_arr)

        # 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. - alphas_cumprod.cpu())))
        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / 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. - 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 sample(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,
               schedule_verbose=False,
               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
               ):
        
        # check condition bs
        if conditioning is not None:
            if isinstance(conditioning, dict):
                try:
                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                except:
                    cbs = conditioning[list(conditioning.keys())[0]][0].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=schedule_verbose)
        self.ddim_num_steps = S     # for ddim_sampling

        # make shape
        if len(shape) == 3:
            C, H, W = shape
            size = (batch_size, C, H, W)
        elif len(shape) == 4:
            C, T, H, W = shape
            size = (batch_size, C, T, H, W)
        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')

        samples, intermediates = self.ddim_sampling(conditioning, size,     # samples: batch, c, t, h, w
                                                    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,
                                                    verbose=verbose,
                                                    **kwargs)
        return samples, intermediates

    # @torch.no_grad()
    def ddim_sampling(self, cond, shape,
                      x_T=None, ddim_use_original_steps=False,
                      callback=None, timesteps=None, quantize_denoised=False,
                      mask=None, x0=None, img_callback=None, log_every_t=100,
                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,
                      cond_tau=1., target_size=None, start_timesteps=None,
                      **kwargs):
        device = self.model.betas.device        
        # print('ddim device', device)
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T
        
        if timesteps is None:
            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
            timesteps = self.ddim_timesteps[:subset_end]
            
        intermediates = {'x_inter': [img], 'pred_x0': [img]}
        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        if verbose:
            iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
        else:
            iterator = time_range

        init_x0 = False
        clean_cond = kwargs.pop("clean_cond", False)

        if self.training_mode == True:
            if self.backprop_mode == 'last':
                backprop_cutoff_idx = self.ddim_num_steps - 1
            elif self.backprop_mode == 'rand':
                backprop_cutoff_idx = random.randint(0, self.ddim_num_steps - 1)
            elif self.backprop_mode == 'specific':
                backprop_cutoff_idx = 15

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            if self.training_mode == True:
                if i >= backprop_cutoff_idx:
                    for name, param in self.model.named_parameters():
                        if "lora" in name:
                            param.requires_grad = True
                else:
                    for name, param in self.model.named_parameters():
                        param.requires_grad = False


            if start_timesteps is not None:
                assert x0 is not None
                if step > start_timesteps*time_range[0]:
                    continue
                elif not init_x0:
                    img = self.model.q_sample(x0, ts) 
                    init_x0 = True

            # use mask to blend noised original latent (img_orig) & new sampled latent (img)
            if mask is not None:
                assert x0 is not None
                if clean_cond:
                    img_orig = x0
                else:
                    img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass? <ddim inversion>
                img = img_orig * mask + (1. - mask) * img # keep original & modify use img
            
            index_clip =  int((1 - cond_tau) * total_steps)
            if index <= index_clip and target_size is not None:
                target_size_ = [target_size[0], target_size[1]//8, target_size[2]//8]
                img = torch.nn.functional.interpolate(
                img,
                size=target_size_,
                mode="nearest",
                )

            forward_context = torch.autograd.graph.save_on_cpu
            with forward_context():
                outs = self.p_sample_ddim(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,
                                        x0=x0,
                                        **kwargs)
            
            img, pred_x0 = outs

            if callback: callback(i)
            if img_callback: img_callback(pred_x0, 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

    # @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,
                      uc_type=None, conditional_guidance_scale_temporal=None, **kwargs):
        b, *_, device = *x.shape, x.device
        
        if x.dim() == 5:
            is_video = True
        else:
            is_video = False
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
        else:
            # with unconditional condition
            if isinstance(c, torch.Tensor):
                e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
            elif isinstance(c, dict):
                e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) # unet denoiser
            else:
                raise NotImplementedError

            if uc_type is None:
                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
            else:
                if uc_type == 'cfg_original':
                    e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond)
                elif uc_type == 'cfg_ours':
                    e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t)
                else:
                    raise NotImplementedError

            # temporal guidance
            if conditional_guidance_scale_temporal is not None:
                e_t_temporal = self.model.apply_model(x, t, c, **kwargs)
                e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs)
                e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image)

        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
        
        if is_video:
            size = (b, 1, 1, 1, 1)
        else:
            size = (b, 1, 1, 1)
        a_t = torch.full(size, alphas[index], device=device)
        a_prev = torch.full(size, alphas_prev[index], device=device)
        sigma_t = torch.full(size, sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(size, 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)
        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        if self.use_scale:
            scale_arr = self.model.scale_arr if use_original_steps else self.ddim_scale_arr
            scale_t = torch.full(size, scale_arr[index], device=device)
            scale_arr_prev = self.model.scale_arr_prev if use_original_steps else self.ddim_scale_arr_prev
            scale_t_prev = torch.full(size, scale_arr_prev[index], device=device)
            pred_x0 /= scale_t 
            x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise
        else:
            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise

        return x_prev, pred_x0


    @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)

        def extract_into_tensor(a, t, x_shape):
            b, *_ = t.shape
            out = a.gather(-1, t)
            return out.reshape(b, *((1,) * (len(x_shape) - 1)))

        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 decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
               use_original_steps=False):

        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 DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
        x_dec = x_latent
        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)
            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
                                          unconditional_guidance_scale=unconditional_guidance_scale,
                                          unconditional_conditioning=unconditional_conditioning)
        return x_dec