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"""
Util functions based on Diffuser framework.
"""
import os
import torch
import cv2
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
from PIL import Image
from torchvision.utils import save_image
from torchvision.io import read_image
from diffusers import StableDiffusionPipeline
from pytorch_lightning import seed_everything
class MasaCtrlPipeline(StableDiffusionPipeline):
def next_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
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_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
def step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta: float=0.0,
verbose=False,
):
"""
predict the sampe the next step in the denoise process.
"""
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_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_prev)**0.5 * model_output
x_prev = alpha_prod_t_prev**0.5 * pred_x0 + pred_dir
return x_prev, pred_x0
@torch.no_grad()
def image2latent(self, image):
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
if type(image) is Image:
image = np.array(image)
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(DEVICE)
# input image density range [-1, 1]
latents = self.vae.encode(image)['latent_dist'].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def latent2image(self, latents, return_type='np'):
latents = 1 / 0.18215 * latents.detach()
image = self.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)
elif return_type == "pt":
image = (image / 2 + 0.5).clamp(0, 1)
return image
def latent2image_grad(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)['sample']
return image # range [-1, 1]
@torch.no_grad()
def __call__(
self,
prompt,
batch_size=1,
height=512,
width=512,
num_inference_steps=50,
guidance_scale=7.5,
eta=0.0,
latents=None,
unconditioning=None,
neg_prompt=None,
ref_intermediate_latents=None,
return_intermediates=False,
**kwds):
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
if isinstance(prompt, list):
batch_size = len(prompt)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(DEVICE))[0]
print("input text embeddings :", text_embeddings.shape)
if kwds.get("dir"):
dir = text_embeddings[-2] - text_embeddings[-1]
u, s, v = torch.pca_lowrank(dir.transpose(-1, -2), q=1, center=True)
text_embeddings[-1] = text_embeddings[-1] + kwds.get("dir") * v
print(u.shape)
print(v.shape)
# define initial latents
latents_shape = (batch_size, self.unet.config.in_channels, height//8, width//8)
if latents is None:
latents = torch.randn(latents_shape, device=DEVICE)
else:
assert latents.shape == latents_shape, f"The shape of input latent tensor {latents.shape} should equal to predefined one."
# unconditional embedding for classifier free guidance
if guidance_scale > 1.:
max_length = text_input.input_ids.shape[-1]
if neg_prompt:
uc_text = neg_prompt
else:
uc_text = ""
# uc_text = "ugly, tiling, poorly drawn hands, poorly drawn feet, body out of frame, cut off, low contrast, underexposed, distorted face"
unconditional_input = self.tokenizer(
[uc_text] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
# unconditional_input.input_ids = unconditional_input.input_ids[:, 1:]
unconditional_embeddings = self.text_encoder(unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat([unconditional_embeddings, text_embeddings], dim=0)
print("latents shape: ", latents.shape)
# iterative sampling
self.scheduler.set_timesteps(num_inference_steps)
# print("Valid timesteps: ", reversed(self.scheduler.timesteps))
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(self.scheduler.timesteps, desc="DDIM Sampler")):
if ref_intermediate_latents is not None:
# note that the batch_size >= 2
latents_ref = ref_intermediate_latents[-1 - i]
_, latents_cur = latents.chunk(2)
latents = torch.cat([latents_ref, latents_cur])
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
if unconditioning is not None and isinstance(unconditioning, list):
_, text_embeddings = text_embeddings.chunk(2)
text_embeddings = torch.cat([unconditioning[i].expand(*text_embeddings.shape), text_embeddings])
# predict tghe noise
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * (noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t -> x_t-1
latents, pred_x0 = self.step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
image = self.latent2image(latents, return_type="pt")
if return_intermediates:
pred_x0_list = [self.latent2image(img, return_type="pt") for img in pred_x0_list]
latents_list = [self.latent2image(img, return_type="pt") for img in latents_list]
return image, pred_x0_list, latents_list
return image
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=50,
guidance_scale=7.5,
eta=0.0,
return_intermediates=False,
**kwds):
"""
invert a real image into noise map with determinisc DDIM inversion
"""
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(DEVICE))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.image2latent(image)
start_latents = latents
# print(latents)
# exit()
# unconditional embedding for classifier free guidance
if guidance_scale > 1.:
max_length = text_input.input_ids.shape[-1]
unconditional_input = self.tokenizer(
[""] * batch_size,
padding="max_length",
max_length=77,
return_tensors="pt"
)
unconditional_embeddings = self.text_encoder(unconditional_input.input_ids.to(DEVICE))[0]
text_embeddings = torch.cat([unconditional_embeddings, text_embeddings], dim=0)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
# print("attributes: ", self.scheduler.__dict__)
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if guidance_scale > 1.:
model_inputs = torch.cat([latents] * 2)
else:
model_inputs = latents
# predict the noise
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
if guidance_scale > 1.:
noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0)
noise_pred = noise_pred_uncon + guidance_scale * (noise_pred_con - noise_pred_uncon)
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.next_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
if return_intermediates:
# return the intermediate laters during inversion
# pred_x0_list = [self.latent2image(img, return_type="pt") for img in pred_x0_list]
return latents, latents_list
return latents, start_latents
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