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import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class DDIMSampler(object):
def __init__(self, diffusion, model, schedule="linear", alpha_generator_func=None, set_alpha_scale=None):
super().__init__()
self.diffusion = diffusion
self.model = model
self.device = diffusion.betas.device
self.ddpm_num_timesteps = diffusion.num_timesteps
self.schedule = schedule
self.alpha_generator_func = alpha_generator_func
self.set_alpha_scale = set_alpha_scale
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
attr = attr.to(self.device)
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.):
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=False)
alphas_cumprod = self.diffusion.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.device)
self.register_buffer('betas', to_torch(self.diffusion.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.diffusion.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. - 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=False)
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, shape, input, uc=None, guidance_scale=1, mask=None, x0=None):
self.make_schedule(ddim_num_steps=S)
return self.ddim_sampling(shape, input, uc, guidance_scale, mask=mask, x0=x0)
@torch.no_grad()
def ddim_sampling(self, shape, input, uc, guidance_scale=1, mask=None, x0=None):
b = shape[0]
img = input["x"]
if img == None:
img = torch.randn(shape, device=self.device)
input["x"] = img
time_range = np.flip(self.ddim_timesteps)
total_steps = self.ddim_timesteps.shape[0]
#iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
iterator = time_range
if self.alpha_generator_func != None:
alphas = self.alpha_generator_func(len(iterator))
for i, step in enumerate(iterator):
# set alpha
if self.alpha_generator_func != None:
self.set_alpha_scale(self.model, alphas[i])
# run
index = total_steps - i - 1
input["timesteps"] = torch.full((b,), step, device=self.device, dtype=torch.long)
if mask is not None:
assert x0 is not None
img_orig = self.diffusion.q_sample( x0, input["timesteps"] )
img = img_orig * mask + (1. - mask) * img
input["x"] = img
img, pred_x0 = self.p_sample_ddim(input, index=index, uc=uc, guidance_scale=guidance_scale)
input["x"] = img
return img
@torch.no_grad()
def p_sample_ddim(self, input, index, uc=None, guidance_scale=1):
e_t = self.model(input)
if uc is not None and guidance_scale != 1:
unconditional_input = dict(x=input["x"], timesteps=input["timesteps"], context=uc)
if "inpainting_extra_input" in input:
unconditional_input["inpainting_extra_input"] = input["inpainting_extra_input"]
e_t_uncond = self.model( unconditional_input )
e_t = e_t_uncond + guidance_scale * (e_t - e_t_uncond)
# select parameters corresponding to the currently considered timestep
b = input["x"].shape[0]
a_t = torch.full((b, 1, 1, 1), self.ddim_alphas[index], device=self.device)
a_prev = torch.full((b, 1, 1, 1), self.ddim_alphas_prev[index], device=self.device)
sigma_t = torch.full((b, 1, 1, 1), self.ddim_sigmas[index], device=self.device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), self.ddim_sqrt_one_minus_alphas[index],device=self.device)
# current prediction for x_0
pred_x0 = (input["x"] - sqrt_one_minus_at * e_t) / a_t.sqrt()
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * torch.randn_like( input["x"] )
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0