"""SAMPLING ONLY."""
# CrossAttn precision handling
import os
import einops
import numpy as np
import torch
from tqdm import tqdm
from ControlNet.ldm.modules.diffusionmodules.util import (
extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps,
noise_like)
_ATTN_PRECISION = os.environ.get('ATTN_PRECISION', 'fp32')
def register_attention_control(model, controller=None):
def ca_forward(self, place_in_unet):
def forward(x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
context = controller(context, is_cross, place_in_unet)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(
lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h),
(q, k, v))
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION == 'fp32':
with torch.autocast(enabled=False, device_type='cuda'):
q, k = q.float(), k.float()
sim = torch.einsum('b i d, b j d -> b i j', q,
k) * self.scale
else:
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
del q, k
if mask is not None:
mask = einops.rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = einops.repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
out = torch.einsum('b i j, b j d -> b i d', sim, v)
out = einops.rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.cur_step = 0
if controller is None:
controller = DummyController()
def register_recr(net_, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
elif hasattr(net_, 'children'):
for net__ in net_.children():
register_recr(net__, place_in_unet)
sub_nets = model.named_children()
for net in sub_nets:
if 'input_blocks' in net[0]:
register_recr(net[1], 'down')
elif 'output_blocks' in net[0]:
register_recr(net[1], 'up')
elif 'middle_block' in net[0]:
register_recr(net[1], 'mid')
class DDIMVSampler(object):
def __init__(self, model, schedule='linear', **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
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'
def to_torch(x):
return 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))
# 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,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
xtrg=None,
noise_rescale=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
dynamic_threshold=None,
ucg_schedule=None,
controller=None,
strength=0.0,
**kwargs):
if conditioning is not None:
if isinstance(conditioning, dict):
ctmp = conditioning[list(conditioning.keys())[0]]
while isinstance(ctmp, list):
ctmp = ctmp[0]
cbs = ctmp.shape[0]
if cbs != batch_size:
print(f'Warning: Got {cbs} conditionings'
f'but batch-size is {batch_size}')
elif isinstance(conditioning, list):
for ctmp in conditioning:
if ctmp.shape[0] != batch_size:
print(f'Warning: Got {cbs} conditionings'
f'but batch-size is {batch_size}')
else:
if conditioning.shape[0] != batch_size:
print(f'Warning: Got {conditioning.shape[0]}'
f'conditionings but batch-size is {batch_size}')
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
samples, intermediates = self.ddim_sampling(
conditioning,
size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask,
x0=x0,
xtrg=xtrg,
noise_rescale=noise_rescale,
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,
dynamic_threshold=dynamic_threshold,
ucg_schedule=ucg_schedule,
controller=controller,
strength=strength,
)
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,
xtrg=None,
noise_rescale=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,
dynamic_threshold=None,
ucg_schedule=None,
controller=None,
strength=0.0):
if strength == 1 and x0 is not None:
return x0, None
register_attention_control(self.model.model.diffusion_model,
controller)
device = self.model.betas.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]
print(f'Running DDIM Sampling with {total_steps} timesteps')
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
if controller is not None:
controller.set_total_step(total_steps)
if mask is None:
mask = [None] * total_steps
dir_xt = 0
for i, step in enumerate(iterator):
if controller is not None:
controller.set_step(i)
index = total_steps - i - 1
ts = torch.full((b, ), step, device=device, dtype=torch.long)
if strength >= 0 and i == int(
total_steps * strength) and x0 is not None:
img = self.model.q_sample(x0, ts)
if mask is not None and xtrg is not None:
# TODO: deterministic forward pass?
if type(mask) == list:
weight = mask[i]
else:
weight = mask
if weight is not None:
rescale = torch.maximum(1. - weight, (1 - weight**2)**0.5 *
controller.inner_strength)
if noise_rescale is not None:
rescale = (1. - weight) * (
1 - noise_rescale) + rescale * noise_rescale
img_ref = self.model.q_sample(xtrg, ts)
img = img_ref * weight + (1. - weight) * (
img - dir_xt) + rescale * dir_xt
if ucg_schedule is not None:
assert len(ucg_schedule) == len(time_range)
unconditional_guidance_scale = ucg_schedule[i]
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,
dynamic_threshold=dynamic_threshold,
controller=controller,
return_dir=True)
img, pred_x0, dir_xt = 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,
dynamic_threshold=None,
controller=None,
return_dir=False):
b, *_, device = *x.shape, x.device
if unconditional_conditioning is None or \
unconditional_guidance_scale == 1.:
model_output = self.model.apply_model(x, t, c)
else:
model_t = self.model.apply_model(x, t, c)
model_uncond = self.model.apply_model(x, t,
unconditional_conditioning)
model_output = model_uncond + unconditional_guidance_scale * (
model_t - model_uncond)
if self.model.parameterization == 'v':
e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
else:
e_t = model_output
if score_corrector is not None:
assert self.model.parameterization == 'eps', 'not implemented'
e_t = score_corrector.modify_score(self.model, e_t, x, t, c,
**corrector_kwargs)
if use_original_steps:
alphas = self.model.alphas_cumprod
alphas_prev = self.model.alphas_cumprod_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod
sigmas = self.model.ddim_sigmas_for_original_num_steps
else:
alphas = self.ddim_alphas
alphas_prev = self.ddim_alphas_prev
sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
sigmas = 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
if self.model.parameterization != 'v':
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
else:
pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
if dynamic_threshold is not None:
raise NotImplementedError()
'''
if mask is not None and xtrg is not None:
pred_x0 = xtrg * mask + (1. - mask) * pred_x0
'''
if controller is not None:
pred_x0 = controller.update_x0(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)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
if return_dir:
return x_prev, pred_x0, dir_xt
return x_prev, pred_x0
@torch.no_grad()
def encode(self,
x0,
c,
t_enc,
use_original_steps=False,
return_intermediates=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
callback=None):
timesteps = np.arange(self.ddpm_num_timesteps
) if use_original_steps else self.ddim_timesteps
num_reference_steps = timesteps.shape[0]
assert t_enc <= num_reference_steps
num_steps = t_enc
if use_original_steps:
alphas_next = self.alphas_cumprod[:num_steps]
alphas = self.alphas_cumprod_prev[:num_steps]
else:
alphas_next = self.ddim_alphas[:num_steps]
alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
x_next = x0
intermediates = []
inter_steps = []
for i in tqdm(range(num_steps), desc='Encoding Image'):
t = torch.full((x0.shape[0], ),
timesteps[i],
device=self.model.device,
dtype=torch.long)
if unconditional_guidance_scale == 1.:
noise_pred = self.model.apply_model(x_next, t, c)
else:
assert unconditional_conditioning is not None
e_t_uncond, noise_pred = torch.chunk(
self.model.apply_model(
torch.cat((x_next, x_next)), torch.cat((t, t)),
torch.cat((unconditional_conditioning, c))), 2)
noise_pred = e_t_uncond + unconditional_guidance_scale * (
noise_pred - e_t_uncond)
xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
weighted_noise_pred = alphas_next[i].sqrt() * (
(1 / alphas_next[i] - 1).sqrt() -
(1 / alphas[i] - 1).sqrt()) * noise_pred
x_next = xt_weighted + weighted_noise_pred
if return_intermediates and i % (num_steps // return_intermediates
) == 0 and i < num_steps - 1:
intermediates.append(x_next)
inter_steps.append(i)
elif return_intermediates and i >= num_steps - 2:
intermediates.append(x_next)
inter_steps.append(i)
if callback:
callback(i)
out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
if return_intermediates:
out.update({'intermediates': intermediates})
return x_next, out
@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)
if t >= len(sqrt_alphas_cumprod):
return noise
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,
callback=None):
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)
if callback:
callback(i)
return x_dec
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def adaptive_instance_normalization(content_feat, style_feat):
assert (content_feat.size()[:2] == style_feat.size()[:2])
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat -
content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)