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"""
https://github.com/SHI-Labs/Versatile-Diffusion
"""
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,
condition=None,
unconditional_guidance_scale=1.,
xtype='image',
condition_types=['text'],
eta=0.,
temperature=1.,
mix_weight=None,
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,
condition=condition,
unconditional_guidance_scale=unconditional_guidance_scale,
xtype=xtype,
condition_types=condition_types,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
log_every_t=log_every_t,
mix_weight=mix_weight, )
return samples, intermediates
@torch.no_grad()
def ddim_sampling(self,
shape,
xt=None,
condition=None,
unconditional_guidance_scale=1.,
xtype=['image'],
condition_types=['text'],
ddim_use_original_steps=False,
timesteps=None,
noise_dropout=0.,
temperature=1.,
mix_weight=None,
log_every_t=100, ):
device = self.model.device
dtype = condition[0][0].dtype
if isinstance(shape[0], list):
bs = shape[0][0]
else:
bs = shape[0]
if xt is None:
if isinstance(shape[0], list):
xt = [torch.randn(shape_i, device=device, dtype=dtype) for shape_i in shape]
else:
xt = torch.randn(shape, device=device, dtype=dtype)
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,
condition,
ts, index,
unconditional_guidance_scale=unconditional_guidance_scale,
xtype=xtype,
condition_types=condition_types,
use_original_steps=ddim_use_original_steps,
noise_dropout=noise_dropout,
temperature=temperature,
mix_weight=mix_weight, )
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,
condition,
t, index,
unconditional_guidance_scale=1.,
xtype=['image'],
condition_types=['text'],
repeat_noise=False,
use_original_steps=False,
noise_dropout=0.,
temperature=1.,
mix_weight=None, ):
b, *_, device = *x[0].shape, x[0].device
x_in = []
for x_i in x:
x_in.append(torch.cat([x_i] * 2))
t_in = torch.cat([t] * 2)
out = self.model.model.diffusion_model(
x_in, t_in, condition, xtype=xtype, condition_types=condition_types, mix_weight=mix_weight)
e_t = []
for out_i in out:
e_t_uncond_i, e_t_i = out_i.chunk(2)
e_t_i = e_t_uncond_i + unconditional_guidance_scale * (e_t_i - e_t_uncond_i)
e_t_i = e_t_i.to(device)
e_t.append(e_t_i)
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
x_prev = []
pred_x0 = []
device = x[0].device
dtype = x[0].dtype
for i, xtype_i in enumerate(xtype):
if xtype_i in ['image', 'frontal', 'lateral']:
extended_shape = (b, 1, 1, 1)
elif xtype_i == 'video':
extended_shape = (b, 1, 1, 1, 1)
elif xtype_i == 'text':
extended_shape = (b, 1)
elif xtype_i == 'audio':
extended_shape = (b, 1, 1, 1)
a_t = torch.full(extended_shape, alphas[index], device=device, dtype=dtype)
a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=dtype)
sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=dtype)
sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=dtype)
# current prediction for x_0
pred_x0_i = (x[i] - sqrt_one_minus_at * e_t[i]) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t[i]
noise = sigma_t * noise_like(x[i], repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev_i = a_prev.sqrt() * pred_x0_i + dir_xt + noise
x_prev.append(x_prev_i)
pred_x0.append(pred_x0_i)
return x_prev, pred_x0
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