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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_VD(DDIMSampler):
@torch.no_grad()
def sample(self,
steps,
shape,
xt=None,
conditioning=None,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
xtype='image',
ctype='prompt',
eta=0.,
temperature=1.,
noise_dropout=0.,
verbose=True,
log_every_t=100,):
self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
print(f'Data shape for DDIM sampling is {shape}, eta {eta}')
samples, intermediates = self.ddim_sampling(
shape,
xt=xt,
conditioning=conditioning,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
xtype=xtype,
ctype=ctype,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,)
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self,
shape,
xt=None,
conditioning=None,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
xtype='image',
ctype='prompt',
ddim_use_original_steps=False,
timesteps=None,
noise_dropout=0.,
temperature=1.,
log_every_t=100,):
device = self.model.device
bs = shape[0]
if xt is None:
xt = torch.randn(shape, device=device)
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 = {'pred_xt': [], 'pred_x0': []}
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")
pred_xt = xt
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(
pred_xt, conditioning, ts, index,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
xtype=xtype,
ctype=ctype,
use_original_steps=ddim_use_original_steps,
noise_dropout=noise_dropout,
temperature=temperature,)
pred_xt, pred_x0 = outs
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['pred_xt'].append(pred_xt)
intermediates['pred_x0'].append(pred_x0)
return pred_xt, intermediates
@torch.no_grad()
def p_sample_ddim(self, x, conditioning, t, index,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
xtype='image',
ctype='prompt',
repeat_noise=False,
use_original_steps=False,
noise_dropout=0.,
temperature=1.,):
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, xtype=xtype, ctype=ctype)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, conditioning])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, xtype=xtype, ctype=ctype).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
if xtype == 'image':
extended_shape = (b, 1, 1, 1)
elif xtype == 'text':
extended_shape = (b, 1)
a_t = torch.full(extended_shape, alphas[index], device=device)
a_prev = torch.full(extended_shape, alphas_prev[index], device=device)
sigma_t = torch.full(extended_shape, sigmas[index], device=device)
sqrt_one_minus_at = torch.full(extended_shape, 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
class DDIMSampler_VD_DualContext(DDIMSampler_VD):
@torch.no_grad()
def sample_dc(self,
steps,
shape,
xt=None,
first_conditioning=None,
second_conditioning=None,
unconditional_guidance_scale=1.,
xtype='image',
first_ctype='prompt',
second_ctype='prompt',
eta=0.,
temperature=1.,
mixed_ratio=0.5,
noise_dropout=0.,
verbose=True,
log_every_t=100,):
self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose)
print(f'Data shape for DDIM sampling is {shape}, eta {eta}')
samples, intermediates = self.ddim_sampling_dc(
shape,
xt=xt,
first_conditioning=first_conditioning,
second_conditioning=second_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale,
xtype=xtype,
first_ctype=first_ctype,
second_ctype=second_ctype,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,
mixed_ratio=mixed_ratio, )
return samples, intermediates
@torch.no_grad()
def ddim_sampling_dc(self,
shape,
xt=None,
first_conditioning=None,
second_conditioning=None,
unconditional_guidance_scale=1.,
xtype='image',
first_ctype='prompt',
second_ctype='prompt',
ddim_use_original_steps=False,
timesteps=None,
noise_dropout=0.,
temperature=1.,
mixed_ratio=0.5,
log_every_t=100,):
device = self.model.device
bs = shape[0]
if xt is None:
xt = torch.randn(shape, device=device)
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 = {'pred_xt': [], 'pred_x0': []}
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")
pred_xt = xt
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_dc(
pred_xt,
first_conditioning,
second_conditioning,
ts, index,
unconditional_guidance_scale=unconditional_guidance_scale,
xtype=xtype,
first_ctype=first_ctype,
second_ctype=second_ctype,
use_original_steps=ddim_use_original_steps,
noise_dropout=noise_dropout,
temperature=temperature,
mixed_ratio=mixed_ratio,)
pred_xt, pred_x0 = outs
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['pred_xt'].append(pred_xt)
intermediates['pred_x0'].append(pred_x0)
return pred_xt, intermediates
@torch.no_grad()
def p_sample_ddim_dc(self, x,
first_conditioning,
second_conditioning,
t, index,
unconditional_guidance_scale=1.,
xtype='image',
first_ctype='prompt',
second_ctype='prompt',
repeat_noise=False,
use_original_steps=False,
noise_dropout=0.,
temperature=1.,
mixed_ratio=0.5,):
b, *_, device = *x.shape, x.device
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
first_c = torch.cat(first_conditioning)
second_c = torch.cat(second_conditioning)
e_t_uncond, e_t = self.model.apply_model_dc(
x_in, t_in, first_c, second_c, xtype=xtype, first_ctype=first_ctype, second_ctype=second_ctype, mixed_ratio=mixed_ratio).chunk(2)
# e_t_uncond, e_t = self.model.apply_model(x_in, t_in, first_c, xtype='image', ctype='vision').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
if xtype == 'image':
extended_shape = (b, 1, 1, 1)
elif xtype == 'text':
extended_shape = (b, 1)
a_t = torch.full(extended_shape, alphas[index], device=device)
a_prev = torch.full(extended_shape, alphas_prev[index], device=device)
sigma_t = torch.full(extended_shape, sigmas[index], device=device)
sqrt_one_minus_at = torch.full(extended_shape, 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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