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import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from .ddim import DDIMSampler
class DDIMSampler_DualContext(DDIMSampler):
@torch.no_grad()
def sample_text(self, *args, **kwargs):
self.cond_type = 'prompt'
return self.sample(*args, **kwargs)
@torch.no_grad()
def sample_vision(self, *args, **kwargs):
self.cond_type = 'vision'
return self.sample(*args, **kwargs)
@torch.no_grad()
def sample_mixed(self, *args, **kwargs):
self.cond_type = kwargs.pop('cond_mixed_p')
return self.sample(*args, **kwargs)
@torch.no_grad()
def sample(self,
steps,
shape,
xt=None,
conditioning=None,
eta=0.,
temperature=1.,
noise_dropout=0.,
verbose=True,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,):
self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
# sampling
print(f'Data shape for DDIM sampling is {shape}, eta {eta}')
samples, intermediates = self.ddim_sampling(
conditioning,
shape,
xt=xt,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,)
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self,
conditioning,
shape,
xt=None,
ddim_use_original_steps=False,
timesteps=None,
log_every_t=100,
temperature=1.,
noise_dropout=0.,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,):
device = self.model.betas.device
bs = shape[0]
if xt is None:
img = torch.randn(shape, device=device)
else:
img = xt
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]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((bs,), step, device=device, dtype=torch.long)
outs = self.p_sample_ddim(img, conditioning, ts, index=index, use_original_steps=ddim_use_original_steps,
temperature=temperature,
noise_dropout=noise_dropout,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
img, pred_x0 = outs
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, conditioning, t, index, repeat_noise=False, use_original_steps=False,
temperature=1., noise_dropout=0.,
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, conditioning, cond_type=self.cond_type)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
# c_in = torch.cat([unconditional_conditioning, conditioning])
# Added for vd-dc dual guidance
if isinstance(unconditional_conditioning, list):
c_in = [torch.cat([ui, ci]) for ui, ci in zip(unconditional_conditioning, conditioning)]
else:
c_in = torch.cat([unconditional_conditioning, conditioning])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, cond_type=self.cond_type).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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()
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
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