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import os
import imageio
import numpy as np
from typing import Union
import torch
import torchvision
from tqdm import tqdm
from einops import rearrange
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
videos = rearrange(videos, "b c t h w -> t b c h w")
outputs = []
for x in videos:
x = torchvision.utils.make_grid(x, nrow=n_rows)
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
outputs.append(x)
os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.mimsave(path, outputs, fps=fps)
# DDIM Inversion
@torch.no_grad()
def init_prompt(prompt, pipeline):
uncond_input = pipeline.tokenizer(
[""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
return_tensors="pt"
)
uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
text_input = pipeline.tokenizer(
[prompt],
padding="max_length",
max_length=pipeline.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
context = torch.cat([uncond_embeddings, text_embeddings])
return context
def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
timestep, next_timestep = min(
timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
# try:
alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
# except:
# alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] #if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
alpha_prod_t_next = ddim_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(latents, t, context, unet):
noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
@torch.no_grad()
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
context = init_prompt(prompt, pipeline)
uncond_embeddings, cond_embeddings = context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
for i in tqdm(range(num_inv_steps)):
t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
noise_pred_unc = get_noise_pred_single(latent, t, uncond_embeddings, pipeline.unet)
noise_pred = noise_pred_unc + 9.0 * (noise_pred_unc - noise_pred)
latent = next_step(noise_pred, t, latent, ddim_scheduler)
all_latent.append(latent)
return all_latent
@torch.no_grad()
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
return ddim_latents |