audioEditing / inversion_utils.py
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import torch
from tqdm import tqdm
# from torchvision import transforms as T
from typing import List, Optional, Dict, Union
from models import PipelineWrapper
def mu_tilde(model, xt, x0, timestep):
"mu_tilde(x_t, x_0) DDPM paper eq. 7"
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 \
else model.scheduler.final_alpha_cumprod
alpha_t = model.scheduler.alphas[timestep]
beta_t = 1 - alpha_t
alpha_bar = model.scheduler.alphas_cumprod[timestep]
return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + \
((alpha_t**0.5 * (1-alpha_prod_t_prev)) / (1 - alpha_bar)) * xt
def sample_xts_from_x0(model, x0, num_inference_steps=50, x_prev_mode=False):
"""
Samples from P(x_1:T|x_0)
"""
# torch.manual_seed(43256465436)
alpha_bar = model.model.scheduler.alphas_cumprod
sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
alphas = model.model.scheduler.alphas
# betas = 1 - alphas
variance_noise_shape = (
num_inference_steps + 1,
model.model.unet.config.in_channels,
# model.unet.sample_size,
# model.unet.sample_size)
x0.shape[-2],
x0.shape[-1])
timesteps = model.model.scheduler.timesteps.to(model.device)
t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
xts = torch.zeros(variance_noise_shape).to(x0.device)
xts[0] = x0
x_prev = x0
for t in reversed(timesteps):
# idx = t_to_idx[int(t)]
idx = num_inference_steps-t_to_idx[int(t)]
if x_prev_mode:
xts[idx] = x_prev * (alphas[t] ** 0.5) + torch.randn_like(x0) * ((1-alphas[t]) ** 0.5)
x_prev = xts[idx].clone()
else:
xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t]
# xts = torch.cat([xts, x0 ],dim = 0)
return xts
def forward_step(model, model_output, timestep, sample):
next_timestep = min(model.scheduler.config.num_train_timesteps - 2,
timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps)
# 2. compute alphas, betas
alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
# alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 \
# else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 5. TODO: simple noising implementatiom
next_sample = model.scheduler.add_noise(pred_original_sample, model_output, torch.LongTensor([next_timestep]))
return next_sample
def inversion_forward_process(model: PipelineWrapper,
x0: torch.Tensor,
etas: Optional[float] = None,
prog_bar: bool = False,
prompts: List[str] = [""],
cfg_scales: List[float] = [3.5],
num_inference_steps: int = 50,
eps: Optional[float] = None,
cutoff_points: Optional[List[float]] = None,
numerical_fix: bool = False,
extract_h_space: bool = False,
extract_skipconns: bool = False,
x_prev_mode: bool = False):
if len(prompts) > 1 and extract_h_space:
raise NotImplementedError("How do you split cfg_scales for hspace? TODO")
if len(prompts) > 1 or prompts[0] != "":
text_embeddings_hidden_states, text_embeddings_class_labels, \
text_embeddings_boolean_prompt_mask = model.encode_text(prompts)
# text_embeddings = encode_text(model, prompt)
# # classifier free guidance
batch_size = len(prompts)
cfg_scales_tensor = torch.ones((batch_size, *x0.shape[1:]), device=model.device, dtype=x0.dtype)
# if len(prompts) > 1:
# if cutoff_points is None:
# cutoff_points = [i * 1 / batch_size for i in range(1, batch_size)]
# if len(cfg_scales) == 1:
# cfg_scales *= batch_size
# elif len(cfg_scales) < batch_size:
# raise ValueError("Not enough target CFG scales")
# cutoff_points = [int(x * cfg_scales_tensor.shape[2]) for x in cutoff_points]
# cutoff_points = [0, *cutoff_points, cfg_scales_tensor.shape[2]]
# for i, (start, end) in enumerate(zip(cutoff_points[:-1], cutoff_points[1:])):
# cfg_scales_tensor[i, :, end:] = 0
# cfg_scales_tensor[i, :, :start] = 0
# cfg_scales_tensor[i] *= cfg_scales[i]
# if prompts[i] == "":
# cfg_scales_tensor[i] = 0
# cfg_scales_tensor = T.functional.gaussian_blur(cfg_scales_tensor, kernel_size=15, sigma=1)
# else:
cfg_scales_tensor *= cfg_scales[0]
uncond_embedding_hidden_states, uncond_embedding_class_lables, uncond_boolean_prompt_mask = model.encode_text([""])
# uncond_embedding = encode_text(model, "")
timesteps = model.model.scheduler.timesteps.to(model.device)
variance_noise_shape = (
num_inference_steps,
model.model.unet.config.in_channels,
# model.unet.sample_size,
# model.unet.sample_size)
x0.shape[-2],
x0.shape[-1])
if etas is None or (type(etas) in [int, float] and etas == 0):
eta_is_zero = True
zs = None
else:
eta_is_zero = False
if type(etas) in [int, float]:
etas = [etas]*model.model.scheduler.num_inference_steps
xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps, x_prev_mode=x_prev_mode)
alpha_bar = model.model.scheduler.alphas_cumprod
zs = torch.zeros(size=variance_noise_shape, device=model.device)
hspaces = []
skipconns = []
t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
xt = x0
# op = tqdm(reversed(timesteps)) if prog_bar else reversed(timesteps)
op = tqdm(timesteps) if prog_bar else timesteps
for t in op:
# idx = t_to_idx[int(t)]
idx = num_inference_steps - t_to_idx[int(t)] - 1
# 1. predict noise residual
if not eta_is_zero:
xt = xts[idx+1][None]
with torch.no_grad():
out, out_hspace, out_skipconns = model.unet_forward(xt, timestep=t,
encoder_hidden_states=uncond_embedding_hidden_states,
class_labels=uncond_embedding_class_lables,
encoder_attention_mask=uncond_boolean_prompt_mask)
# out = model.unet.forward(xt, timestep= t, encoder_hidden_states=uncond_embedding)
if len(prompts) > 1 or prompts[0] != "":
cond_out, cond_out_hspace, cond_out_skipconns = model.unet_forward(
xt.expand(len(prompts), -1, -1, -1), timestep=t,
encoder_hidden_states=text_embeddings_hidden_states,
class_labels=text_embeddings_class_labels,
encoder_attention_mask=text_embeddings_boolean_prompt_mask)
# cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings)
if len(prompts) > 1 or prompts[0] != "":
# # classifier free guidance
noise_pred = out.sample + \
(cfg_scales_tensor * (cond_out.sample - out.sample.expand(batch_size, -1, -1, -1))
).sum(axis=0).unsqueeze(0)
if extract_h_space or extract_skipconns:
noise_h_space = out_hspace + cfg_scales[0] * (cond_out_hspace - out_hspace)
if extract_skipconns:
noise_skipconns = {k: [out_skipconns[k][j] + cfg_scales[0] *
(cond_out_skipconns[k][j] - out_skipconns[k][j])
for j in range(len(out_skipconns[k]))]
for k in out_skipconns}
else:
noise_pred = out.sample
if extract_h_space or extract_skipconns:
noise_h_space = out_hspace
if extract_skipconns:
noise_skipconns = out_skipconns
if extract_h_space or extract_skipconns:
hspaces.append(noise_h_space)
if extract_skipconns:
skipconns.append(noise_skipconns)
if eta_is_zero:
# 2. compute more noisy image and set x_t -> x_t+1
xt = forward_step(model.model, noise_pred, t, xt)
else:
# xtm1 = xts[idx+1][None]
xtm1 = xts[idx][None]
# pred of x0
if model.model.scheduler.config.prediction_type == 'epsilon':
pred_original_sample = (xt - (1 - alpha_bar[t]) ** 0.5 * noise_pred) / alpha_bar[t] ** 0.5
elif model.model.scheduler.config.prediction_type == 'v_prediction':
pred_original_sample = (alpha_bar[t] ** 0.5) * xt - ((1 - alpha_bar[t]) ** 0.5) * noise_pred
# direction to xt
prev_timestep = t - model.model.scheduler.config.num_train_timesteps // \
model.model.scheduler.num_inference_steps
alpha_prod_t_prev = model.get_alpha_prod_t_prev(prev_timestep)
variance = model.get_variance(t, prev_timestep)
if model.model.scheduler.config.prediction_type == 'epsilon':
radom_noise_pred = noise_pred
elif model.model.scheduler.config.prediction_type == 'v_prediction':
radom_noise_pred = (alpha_bar[t] ** 0.5) * noise_pred + ((1 - alpha_bar[t]) ** 0.5) * xt
pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance) ** (0.5) * radom_noise_pred
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
z = (xtm1 - mu_xt) / (etas[idx] * variance ** 0.5)
zs[idx] = z
# correction to avoid error accumulation
if numerical_fix:
xtm1 = mu_xt + (etas[idx] * variance ** 0.5)*z
xts[idx] = xtm1
if zs is not None:
# zs[-1] = torch.zeros_like(zs[-1])
zs[0] = torch.zeros_like(zs[0])
# zs_cycle[0] = torch.zeros_like(zs[0])
if extract_h_space:
hspaces = torch.concat(hspaces, axis=0)
return xt, zs, xts, hspaces
if extract_skipconns:
hspaces = torch.concat(hspaces, axis=0)
return xt, zs, xts, hspaces, skipconns
return xt, zs, xts
def reverse_step(model, model_output, timestep, sample, eta=0, variance_noise=None):
# 1. get previous step value (=t-1)
prev_timestep = timestep - model.model.scheduler.config.num_train_timesteps // \
model.model.scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = model.model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = model.get_alpha_prod_t_prev(prev_timestep)
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if model.model.scheduler.config.prediction_type == 'epsilon':
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif model.model.scheduler.config.prediction_type == 'v_prediction':
pred_original_sample = (alpha_prod_t ** 0.5) * sample - (beta_prod_t ** 0.5) * model_output
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
# variance = self.scheduler._get_variance(timestep, prev_timestep)
variance = model.get_variance(timestep, prev_timestep)
# std_dev_t = eta * variance ** (0.5)
# Take care of asymetric reverse process (asyrp)
if model.model.scheduler.config.prediction_type == 'epsilon':
model_output_direction = model_output
elif model.model.scheduler.config.prediction_type == 'v_prediction':
model_output_direction = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
# 8. Add noice if eta > 0
if eta > 0:
if variance_noise is None:
variance_noise = torch.randn(model_output.shape, device=model.device)
sigma_z = eta * variance ** (0.5) * variance_noise
prev_sample = prev_sample + sigma_z
return prev_sample
def inversion_reverse_process(model: PipelineWrapper,
xT: torch.Tensor,
skips: torch.Tensor,
fix_alpha: float = 0.1,
etas: float = 0,
prompts: List[str] = [""],
neg_prompts: List[str] = [""],
cfg_scales: Optional[List[float]] = None,
prog_bar: bool = False,
zs: Optional[List[torch.Tensor]] = None,
# controller=None,
cutoff_points: Optional[List[float]] = None,
hspace_add: Optional[torch.Tensor] = None,
hspace_replace: Optional[torch.Tensor] = None,
skipconns_replace: Optional[Dict[int, torch.Tensor]] = None,
zero_out_resconns: Optional[Union[int, List]] = None,
asyrp: bool = False,
extract_h_space: bool = False,
extract_skipconns: bool = False):
batch_size = len(prompts)
text_embeddings_hidden_states, text_embeddings_class_labels, \
text_embeddings_boolean_prompt_mask = model.encode_text(prompts)
uncond_embedding_hidden_states, uncond_embedding_class_lables, \
uncond_boolean_prompt_mask = model.encode_text(neg_prompts)
# text_embeddings = encode_text(model, prompts)
# uncond_embedding = encode_text(model, [""] * batch_size)
masks = torch.ones((batch_size, *xT.shape[1:]), device=model.device, dtype=xT.dtype)
cfg_scales_tensor = torch.ones((batch_size, *xT.shape[1:]), device=model.device, dtype=xT.dtype)
# if batch_size > 1:
# if cutoff_points is None:
# cutoff_points = [i * 1 / batch_size for i in range(1, batch_size)]
# if len(cfg_scales) == 1:
# cfg_scales *= batch_size
# elif len(cfg_scales) < batch_size:
# raise ValueError("Not enough target CFG scales")
# cutoff_points = [int(x * cfg_scales_tensor.shape[2]) for x in cutoff_points]
# cutoff_points = [0, *cutoff_points, cfg_scales_tensor.shape[2]]
# for i, (start, end) in enumerate(zip(cutoff_points[:-1], cutoff_points[1:])):
# cfg_scales_tensor[i, :, end:] = 0
# cfg_scales_tensor[i, :, :start] = 0
# masks[i, :, end:] = 0
# masks[i, :, :start] = 0
# cfg_scales_tensor[i] *= cfg_scales[i]
# cfg_scales_tensor = T.functional.gaussian_blur(cfg_scales_tensor, kernel_size=15, sigma=1)
# masks = T.functional.gaussian_blur(masks, kernel_size=15, sigma=1)
# else:
cfg_scales_tensor *= cfg_scales[0]
if etas is None:
etas = 0
if type(etas) in [int, float]:
etas = [etas]*model.model.scheduler.num_inference_steps
assert len(etas) == model.model.scheduler.num_inference_steps
timesteps = model.model.scheduler.timesteps.to(model.device)
# xt = xT.expand(1, -1, -1, -1)
xt = xT[skips.max()].unsqueeze(0)
op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:]
t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0]:])}
hspaces = []
skipconns = []
for it, t in enumerate(op):
# idx = t_to_idx[int(t)]
idx = model.model.scheduler.num_inference_steps - t_to_idx[int(t)] - \
(model.model.scheduler.num_inference_steps - zs.shape[0] + 1)
# # Unconditional embedding
with torch.no_grad():
uncond_out, out_hspace, out_skipconns = model.unet_forward(
xt, timestep=t,
encoder_hidden_states=uncond_embedding_hidden_states,
class_labels=uncond_embedding_class_lables,
encoder_attention_mask=uncond_boolean_prompt_mask,
mid_block_additional_residual=(None if hspace_add is None else
(1 / (cfg_scales[0] + 1)) *
(hspace_add[-zs.shape[0]:][it] if hspace_add.shape[0] > 1
else hspace_add)),
replace_h_space=(None if hspace_replace is None else
(hspace_replace[-zs.shape[0]:][it].unsqueeze(0) if hspace_replace.shape[0] > 1
else hspace_replace)),
zero_out_resconns=zero_out_resconns,
replace_skip_conns=(None if skipconns_replace is None else
(skipconns_replace[-zs.shape[0]:][it] if len(skipconns_replace) > 1
else skipconns_replace))
) # encoder_hidden_states = uncond_embedding)
# # Conditional embedding
if prompts:
with torch.no_grad():
cond_out, cond_out_hspace, cond_out_skipconns = model.unet_forward(
xt.expand(batch_size, -1, -1, -1),
timestep=t,
encoder_hidden_states=text_embeddings_hidden_states,
class_labels=text_embeddings_class_labels,
encoder_attention_mask=text_embeddings_boolean_prompt_mask,
mid_block_additional_residual=(None if hspace_add is None else
(cfg_scales[0] / (cfg_scales[0] + 1)) *
(hspace_add[-zs.shape[0]:][it] if hspace_add.shape[0] > 1
else hspace_add)),
replace_h_space=(None if hspace_replace is None else
(hspace_replace[-zs.shape[0]:][it].unsqueeze(0) if hspace_replace.shape[0] > 1
else hspace_replace)),
zero_out_resconns=zero_out_resconns,
replace_skip_conns=(None if skipconns_replace is None else
(skipconns_replace[-zs.shape[0]:][it] if len(skipconns_replace) > 1
else skipconns_replace))
) # encoder_hidden_states = text_embeddings)
z = zs[idx] if zs is not None else None
# print(f'idx: {idx}')
# print(f't: {t}')
z = z.unsqueeze(0)
# z = z.expand(batch_size, -1, -1, -1)
if prompts:
# # classifier free guidance
# noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
noise_pred = uncond_out.sample + \
(cfg_scales_tensor * (cond_out.sample - uncond_out.sample.expand(batch_size, -1, -1, -1))
).sum(axis=0).unsqueeze(0)
if extract_h_space or extract_skipconns:
noise_h_space = out_hspace + cfg_scales[0] * (cond_out_hspace - out_hspace)
if extract_skipconns:
noise_skipconns = {k: [out_skipconns[k][j] + cfg_scales[0] *
(cond_out_skipconns[k][j] - out_skipconns[k][j])
for j in range(len(out_skipconns[k]))]
for k in out_skipconns}
else:
noise_pred = uncond_out.sample
if extract_h_space or extract_skipconns:
noise_h_space = out_hspace
if extract_skipconns:
noise_skipconns = out_skipconns
if extract_h_space or extract_skipconns:
hspaces.append(noise_h_space)
if extract_skipconns:
skipconns.append(noise_skipconns)
# 2. compute less noisy image and set x_t -> x_t-1
xt = reverse_step(model, noise_pred, t, xt, eta=etas[idx], variance_noise=z)
# if controller is not None:
# xt = controller.step_callback(xt)
# "fix" xt
apply_fix = ((skips.max() - skips) > it)
if apply_fix.any():
apply_fix = (apply_fix * fix_alpha).unsqueeze(1).unsqueeze(2).unsqueeze(3).to(xT.device)
xt = (masks * (xt.expand(batch_size, -1, -1, -1) * (1 - apply_fix) +
apply_fix * xT[skips.max() - it - 1].expand(batch_size, -1, -1, -1))
).sum(axis=0).unsqueeze(0)
if extract_h_space:
return xt, zs, torch.concat(hspaces, axis=0)
if extract_skipconns:
return xt, zs, torch.concat(hspaces, axis=0), skipconns
return xt, zs