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第一次完整测例跑完

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olivame
2026-01-18 00:30:10 +08:00
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src/unifolm_wma/models/samplers/ddim.py Normal file
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import numpy as np
import torch
import copy
from unifolm_wma.utils.diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg
from unifolm_wma.utils.common import noise_like
from unifolm_wma.utils.common import extract_into_tensor
from tqdm import tqdm
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.counter = 0
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'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model
.device)
if self.model.use_dynamic_rescale:
self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps]
self.ddim_scale_arr_prev = torch.cat(
[self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]])
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,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
schedule_verbose=False,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
precision=None,
fs=None,
timestep_spacing='uniform', #uniform_trailing for starting from last timestep
guidance_rescale=0.0,
**kwargs):
# Check condition bs
if conditioning is not None:
if isinstance(conditioning, dict):
try:
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
except:
cbs = conditioning[list(
conditioning.keys())[0]][0].shape[0]
if cbs != batch_size:
print(
f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
)
else:
if conditioning.shape[0] != batch_size:
print(
f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
)
self.make_schedule(ddim_num_steps=S,
ddim_discretize=timestep_spacing,
ddim_eta=eta,
verbose=schedule_verbose)
# Make shape
if len(shape) == 3:
C, H, W = shape
size = (batch_size, C, H, W)
elif len(shape) == 4:
C, T, H, W = shape
size = (batch_size, C, T, H, W)
samples, actions, states, intermediates = self.ddim_sampling(
conditioning,
size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask,
x0=x0,
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,
verbose=verbose,
precision=precision,
fs=fs,
guidance_rescale=guidance_rescale,
**kwargs)
return samples, actions, states, 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,
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,
verbose=True,
precision=None,
fs=None,
guidance_rescale=0.0,
**kwargs):
device = self.model.betas.device
dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action
dp_ddim_scheduler_state = self.model.dp_noise_scheduler_state
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
action = torch.randn((b, 16, self.model.agent_action_dim),
device=device)
state = torch.randn((b, 16, self.model.agent_state_dim),
device=device)
else:
img = x_T
action = torch.randn((b, 16, self.model.agent_action_dim),
device=device)
state = torch.randn((b, 16, self.model.agent_state_dim),
device=device)
if precision is not None:
if precision == 16:
img = img.to(dtype=torch.float16)
action = action.to(dtype=torch.float16)
state = state.to(dtype=torch.float16)
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],
'x_inter_action': [action],
'pred_x0_action': [action],
'x_inter_state': [state],
'pred_x0_state': [state],
}
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]
if verbose:
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
else:
iterator = time_range
clean_cond = kwargs.pop("clean_cond", False)
dp_ddim_scheduler_action.set_timesteps(len(timesteps))
dp_ddim_scheduler_state.set_timesteps(len(timesteps))
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b, ), step, device=device, dtype=torch.long)
# Use mask to blend noised original latent (img_orig) & new sampled latent (img)
if mask is not None:
assert x0 is not None
if clean_cond:
img_orig = x0
else:
img_orig = self.model.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
outs = self.p_sample_ddim(
img,
action,
state,
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,
mask=mask,
x0=x0,
fs=fs,
guidance_rescale=guidance_rescale,
**kwargs)
img, pred_x0, model_output_action, model_output_state = outs
action = dp_ddim_scheduler_action.step(
model_output_action,
step,
action,
generator=None,
).prev_sample
state = dp_ddim_scheduler_state.step(
model_output_state,
step,
state,
generator=None,
).prev_sample
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)
intermediates['x_inter_action'].append(action)
intermediates['x_inter_state'].append(state)
return img, action, state, intermediates
@torch.no_grad()
def p_sample_ddim(self,
x,
x_action,
x_state,
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,
uc_type=None,
conditional_guidance_scale_temporal=None,
mask=None,
x0=None,
guidance_rescale=0.0,
**kwargs):
b, *_, device = *x.shape, x.device
if x.dim() == 5:
is_video = True
else:
is_video = False
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
model_output, model_output_action, model_output_state = self.model.apply_model(
x, x_action, x_state, t, c, **kwargs) # unet denoiser
else:
# do_classifier_free_guidance
if isinstance(c, torch.Tensor) or isinstance(c, dict):
e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model(
x, x_action, x_state, t, c, **kwargs)
e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
x, x_action, x_state, t, unconditional_conditioning,
**kwargs)
else:
raise NotImplementedError
model_output = e_t_uncond + unconditional_guidance_scale * (
e_t_cond - e_t_uncond)
model_output_action = e_t_uncond_action + unconditional_guidance_scale * (
e_t_cond_action - e_t_uncond_action)
model_output_state = e_t_uncond_state + unconditional_guidance_scale * (
e_t_cond_state - e_t_uncond_state)
if guidance_rescale > 0.0:
model_output = rescale_noise_cfg(
model_output, e_t_cond, guidance_rescale=guidance_rescale)
model_output_action = rescale_noise_cfg(
model_output_action,
e_t_cond_action,
guidance_rescale=guidance_rescale)
model_output_state = rescale_noise_cfg(
model_output_state,
e_t_cond_state,
guidance_rescale=guidance_rescale)
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)
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.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
if is_video:
size = (b, 1, 1, 1, 1)
else:
size = (b, 1, 1, 1)
a_t = torch.full(size, alphas[index], device=device)
a_prev = torch.full(size, alphas_prev[index], device=device)
sigma_t = torch.full(size, sigmas[index], device=device)
sqrt_one_minus_at = torch.full(size,
sqrt_one_minus_alphas[index],
device=device)
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 self.model.use_dynamic_rescale:
scale_t = torch.full(size,
self.ddim_scale_arr[index],
device=device)
prev_scale_t = torch.full(size,
self.ddim_scale_arr_prev[index],
device=device)
rescale = (prev_scale_t / scale_t)
pred_x0 *= rescale
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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, model_output_action, model_output_state
@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
@torch.no_grad()
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
# fast, but does not allow for exact reconstruction
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)
return (
extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) *
noise)