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p2putil.py
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p2putil.py
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import sys
sys.path.append('./prompt-to-prompt')
from typing import Optional, Union, Tuple, List, Callable, Dict
# from tqdm.notebook import tqdm
from tqdm import tqdm
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler
import torch.nn.functional as nnf
import numpy as np
import abc
import ptp_utils
import seq_aligner
import shutil
from torch.optim.adam import Adam
from PIL import Image
from skimage.transform import resize
# scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
# device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# GUIDANCE_SCALE = 7.5
# NUM_DDIM_STEPS = 50
# MY_TOKEN = ''
# ldm_stable = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=MY_TOKEN, scheduler=scheduler).to(device)
class EmptyControl:
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
def __call__(self, attn, is_cross: bool, place_in_unet: str):
return attn
class NullInversion:
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, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
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 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
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 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 latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.model.vae.decode(latents)['sample']
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = (image * 255).astype(np.uint8)
return image
@torch.no_grad()
def image2latent(self, image):
with torch.no_grad():
if type(image) is Image:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device)
latents = self.model.vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
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):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in range(NUM_DDIM_STEPS):
t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings)
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_inversion(self, image):
latent = self.image2latent(image)
image_rec = self.latent2image(latent)
ddim_latents = self.ddim_loop(latent)
return image_rec, ddim_latents
def null_optimization(self, latents, num_inner_steps, epsilon):
uncond_embeddings, cond_embeddings = self.context.chunk(2)
uncond_embeddings_list = []
latent_cur = latents[-1]
# bar = tqdm(total=num_inner_steps * NUM_DDIM_STEPS)
for i in tqdm(range(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 + 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()
return uncond_embeddings_list
def invert(self, image, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False):
self.init_prompt(prompt)
ptp_utils.register_attention_control(self.model, None)
if isinstance(image, str):
image_gt = load_512(image, *offsets)
elif isinstance(image, np.ndarray):
image_gt = resize(image, (512, 512))
if image_gt.max()<=1:
image_gt = (image_gt * 255).astype(np.uint8)
else:
raise ValueError("image_path must be either a path to an image or a numpy array")
if verbose:
print("DDIM inversion...")
image_rec, ddim_latents = self.ddim_inversion(image_gt)
if verbose:
print("Null-text optimization...")
uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon)
return (image_gt, image_rec), ddim_latents[-1], uncond_embeddings
def __init__(self, model):
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False)
self.model = model
self.tokenizer = self.model.tokenizer
self.model.scheduler.set_timesteps(NUM_DDIM_STEPS)
self.prompt = None
self.context = None