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import numpy as np
import torch
import PIL.Image
from tqdm import tqdm
from typing import Optional, Union, List
import warnings
warnings.filterwarnings('ignore')
from torch.optim.adam import Adam
import torch.nn.functional as nnf
from diffusers import DDIMScheduler
##########
# helper #
##########
def diffusion_step(model, latents, context, t, guidance_scale, low_resource=False):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
return latents
def image2latent(vae, image):
with torch.no_grad():
if isinstance(image, PIL.Image.Image):
image = np.array(image)
if isinstance(image, np.ndarray):
dtype = next(vae.parameters()).dtype
device = next(vae.parameters()).device
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device=device, dtype=dtype)
latents = vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
def latent2image(vae, latents, return_type='np'):
assert isinstance(latents, torch.Tensor)
latents = 1 / 0.18215 * latents.detach()
image = vae.decode(latents)['sample']
if return_type in ['np', 'pil']:
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
if return_type == 'pil':
pilim = [PIL.Image.fromarray(imi) for imi in image]
pilim = pilim[0] if len(pilim)==1 else pilim
return pilim
else:
return image
def init_latent(latent, model, height, width, generator, batch_size):
if latent is None:
latent = torch.randn(
(1, model.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latent.expand(batch_size, model.unet.in_channels, height // 8, width // 8).to(model.device)
return latent, latents
def txt_to_emb(model, prompt):
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
return text_embeddings
@torch.no_grad()
def text2image_ldm(
model,
prompt: List[str],
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
uncond_embeddings=None,
start_time=50,
return_type='pil', ):
batch_size = len(prompt)
height = width = 512
if latent is not None:
height = latent.shape[-2] * 8
width = latent.shape[-1] * 8
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
if uncond_embeddings is None:
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt",)
uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
else:
uncond_embeddings_ = None
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(tqdm(model.scheduler.timesteps[-start_time:])):
if uncond_embeddings_ is None:
context = torch.cat([uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings])
else:
context = torch.cat([uncond_embeddings_, text_embeddings])
latents = diffusion_step(model, latents, context, t, guidance_scale, low_resource=False)
if return_type in ['pil', 'np']:
image = latent2image(model.vae, latents, return_type=return_type)
else:
image = latents
return image, latent
@torch.no_grad()
def text2image_ldm_imedit(
model,
thresh,
prompt: List[str],
target_prompt: List[str],
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
uncond_embeddings=None,
start_time=50,
return_type='pil'
):
batch_size = len(prompt)
height = width = 512
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
target_text_input = model.tokenizer(
target_prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
target_text_embeddings = model.text_encoder(target_text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
if uncond_embeddings is None:
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
else:
uncond_embeddings_ = None
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
model.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(tqdm(model.scheduler.timesteps[-start_time:])):
if i < (1 - thresh) * num_inference_steps:
if uncond_embeddings_ is None:
context = torch.cat([uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings])
else:
context = torch.cat([uncond_embeddings_, text_embeddings])
latents = diffusion_step(model, latents, context, t, guidance_scale, low_resource=False)
else:
if uncond_embeddings_ is None:
context = torch.cat([uncond_embeddings[i].expand(*target_text_embeddings.shape), target_text_embeddings])
else:
context = torch.cat([uncond_embeddings_, target_text_embeddings])
latents = diffusion_step(model, latents, context, t, guidance_scale, low_resource=False)
if return_type in ['pil', 'np']:
image = latent2image(model.vae, latents, return_type=return_type)
else:
image = latents
return image, latent
###########
# wrapper #
###########
class NullInversion(object):
def __init__(self, model, num_ddim_steps, guidance_scale, device='cuda'):
self.model = model
self.device = device
self.num_ddim_steps=num_ddim_steps
self.guidance_scale = guidance_scale
self.tokenizer = self.model.tokenizer
self.prompt = None
self.context = None
def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, noise_pred, timestep, sample):
timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * noise_pred
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
def get_noise_pred(self, latents, t, is_forward=True, context=None):
latents_input = torch.cat([latents] * 2)
if context is None:
context = self.context
guidance_scale = 1 if is_forward else self.guidance_scale
noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
if is_forward:
latents = self.next_step(noise_pred, t, latents)
else:
latents = self.prev_step(noise_pred, t, latents)
return latents
@torch.no_grad()
def init_prompt(self, prompt: str):
uncond_input = self.model.tokenizer(
[""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
text_input = self.model.tokenizer(
[prompt],
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
self.context = torch.cat([uncond_embeddings, text_embeddings])
self.prompt = prompt
@torch.no_grad()
def ddim_loop(self, latent, emb):
# uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in range(self.num_ddim_steps):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, emb)
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
@property
def scheduler(self):
return self.model.scheduler
@torch.no_grad()
def ddim_invert(self, image, prompt):
assert isinstance(image, PIL.Image.Image)
scheduler_save = self.model.scheduler
self.model.scheduler = DDIMScheduler.from_config(self.model.scheduler.config)
self.model.scheduler.set_timesteps(self.num_ddim_steps)
with torch.no_grad():
emb = txt_to_emb(self.model, prompt)
latent = image2latent(self.model.vae, image)
ddim_latents = self.ddim_loop(latent, emb)
self.model.scheduler = scheduler_save
return ddim_latents[-1]
def null_optimization(self, latents, emb, nemb=None, num_inner_steps=10, epsilon=1e-5):
# force fp32
dtype = latents[0].dtype
uncond_embeddings = nemb.float() if nemb is not None else txt_to_emb(self.model, "").float()
cond_embeddings = emb.float()
latents = [li.float() for li in latents]
self.model.unet.to(torch.float32)
uncond_embeddings_list = []
latent_cur = latents[-1]
bar = tqdm(total=num_inner_steps * self.num_ddim_steps)
for i in range(self.num_ddim_steps):
uncond_embeddings = uncond_embeddings.clone().detach()
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
latent_prev = latents[len(latents) - i - 2]
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
for j in range(num_inner_steps):
noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
loss = nnf.mse_loss(latents_prev_rec, latent_prev)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
bar.update()
if loss_item < epsilon + i * 2e-5:
break
for j in range(j + 1, num_inner_steps):
bar.update()
uncond_embeddings_list.append(uncond_embeddings[:1].detach())
with torch.no_grad():
context = torch.cat([uncond_embeddings, cond_embeddings])
latent_cur = self.get_noise_pred(latent_cur, t, False, context)
bar.close()
uncond_embeddings_list = [ui.to(dtype) for ui in uncond_embeddings_list]
self.model.unet.to(dtype)
return uncond_embeddings_list
def null_invert(self, im, txt, ntxt=None, num_inner_steps=10, early_stop_epsilon=1e-5):
assert isinstance(im, PIL.Image.Image)
scheduler_save = self.model.scheduler
self.model.scheduler = DDIMScheduler.from_config(self.model.scheduler.config)
self.model.scheduler.set_timesteps(self.num_ddim_steps)
with torch.no_grad():
nemb = txt_to_emb(self.model, ntxt) \
if ntxt is not None else txt_to_emb(self.model, "")
emb = txt_to_emb(self.model, txt)
latent = image2latent(self.model.vae, im)
# ddim inversion
ddim_latents = self.ddim_loop(latent, emb)
# nulltext inversion
uncond_embeddings = self.null_optimization(
ddim_latents, emb, nemb, num_inner_steps, early_stop_epsilon)
self.model.scheduler = scheduler_save
return ddim_latents[-1], uncond_embeddings
def null_optimization_dual(
self, latents0, latents1, emb0, emb1, nemb=None,
num_inner_steps=10, epsilon=1e-5):
# force fp32
dtype = latents0[0].dtype
uncond_embeddings = nemb.float() if nemb is not None else txt_to_emb(self.model, "").float()
cond_embeddings0, cond_embeddings1 = emb0.float(), emb1.float()
latents0 = [li.float() for li in latents0]
latents1 = [li.float() for li in latents1]
self.model.unet.to(torch.float32)
uncond_embeddings_list = []
latent_cur0 = latents0[-1]
latent_cur1 = latents1[-1]
bar = tqdm(total=num_inner_steps * self.num_ddim_steps)
for i in range(self.num_ddim_steps):
uncond_embeddings = uncond_embeddings.clone().detach()
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
latent_prev0 = latents0[len(latents0) - i - 2]
latent_prev1 = latents1[len(latents1) - i - 2]
t = self.model.scheduler.timesteps[i]
with torch.no_grad():
noise_pred_cond0 = self.get_noise_pred_single(latent_cur0, t, cond_embeddings0)
noise_pred_cond1 = self.get_noise_pred_single(latent_cur1, t, cond_embeddings1)
for j in range(num_inner_steps):
noise_pred_uncond0 = self.get_noise_pred_single(latent_cur0, t, uncond_embeddings)
noise_pred_uncond1 = self.get_noise_pred_single(latent_cur1, t, uncond_embeddings)
noise_pred0 = noise_pred_uncond0 + self.guidance_scale*(noise_pred_cond0-noise_pred_uncond0)
noise_pred1 = noise_pred_uncond1 + self.guidance_scale*(noise_pred_cond1-noise_pred_uncond1)
latents_prev_rec0 = self.prev_step(noise_pred0, t, latent_cur0)
latents_prev_rec1 = self.prev_step(noise_pred1, t, latent_cur1)
loss = nnf.mse_loss(latents_prev_rec0, latent_prev0) + \
nnf.mse_loss(latents_prev_rec1, latent_prev1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
bar.update()
if loss_item < epsilon + i * 2e-5:
break
for j in range(j + 1, num_inner_steps):
bar.update()
uncond_embeddings_list.append(uncond_embeddings[:1].detach())
with torch.no_grad():
context0 = torch.cat([uncond_embeddings, cond_embeddings0])
context1 = torch.cat([uncond_embeddings, cond_embeddings1])
latent_cur0 = self.get_noise_pred(latent_cur0, t, False, context0)
latent_cur1 = self.get_noise_pred(latent_cur1, t, False, context1)
bar.close()
uncond_embeddings_list = [ui.to(dtype) for ui in uncond_embeddings_list]
self.model.unet.to(dtype)
return uncond_embeddings_list
def null_invert_dual(
self, im0, im1, txt0, txt1, ntxt=None,
num_inner_steps=10, early_stop_epsilon=1e-5, ):
assert isinstance(im0, PIL.Image.Image)
assert isinstance(im1, PIL.Image.Image)
scheduler_save = self.model.scheduler
self.model.scheduler = DDIMScheduler.from_config(self.model.scheduler.config)
self.model.scheduler.set_timesteps(self.num_ddim_steps)
with torch.no_grad():
nemb = txt_to_emb(self.model, ntxt) \
if ntxt is not None else txt_to_emb(self.model, "")
latent0 = image2latent(self.model.vae, im0)
latent1 = image2latent(self.model.vae, im1)
emb0 = txt_to_emb(self.model, txt0)
emb1 = txt_to_emb(self.model, txt1)
# ddim inversion
ddim_latents_0 = self.ddim_loop(latent0, emb0)
ddim_latents_1 = self.ddim_loop(latent1, emb1)
# nulltext inversion
nembs = self.null_optimization_dual(
ddim_latents_0, ddim_latents_1, emb0, emb1, nemb, num_inner_steps, early_stop_epsilon)
self.model.scheduler = scheduler_save
return ddim_latents_0[-1], ddim_latents_1[-1], nembs
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