import glob
import os
import numpy as np
import cv2
from pathlib import Path
import torch
import torch.nn as nn
import torchvision.transforms as T
import argparse
from PIL import Image
import yaml
import inspect
from tqdm import tqdm
from transformers import logging
from diffusers import DDIMScheduler, StableDiffusionPipeline
from tokenflow_utils import *
from utils import save_video, seed_everything
# suppress partial model loading warning
logging.set_verbosity_error()
VAE_BATCH_SIZE = 10
class TokenFlow(nn.Module):
def __init__(self, config,
pipe,
frames = None,
inverted_latents = None, #X0,...,XT,
zs = None):
super().__init__()
self.config = config
self.device = config["device"]
sd_version = config["sd_version"]
self.sd_version = sd_version
if sd_version == '2.1':
model_key = "stabilityai/stable-diffusion-2-1-base"
elif sd_version == '2.0':
model_key = "stabilityai/stable-diffusion-2-base"
elif sd_version == '1.5':
model_key = "runwayml/stable-diffusion-v1-5"
elif sd_version == 'depth':
model_key = "stabilityai/stable-diffusion-2-depth"
else:
raise ValueError(f'Stable-diffusion version {sd_version} not supported.')
# Create SD models
print('Loading SD model')
# pipe = StableDiffusionPipeline.from_pretrained(model_key, torch_dtype=torch.float16).to("cuda")
# pipe.enable_xformers_memory_efficient_attention()
self.vae = pipe.vae
self.tokenizer = pipe.tokenizer
self.text_encoder = pipe.text_encoder
self.unet = pipe.unet
self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
self.scheduler.set_timesteps(config["n_timesteps"], device=self.device)
print('SD model loaded')
# data
self.inversion = config['inversion']
if self.inversion == 'ddpm':
self.skip_steps = config['skip_steps']
self.eta = 1.0
else:
self.eta = 0.0
self.extra_step_kwargs = self.prepare_extra_step_kwargs(self.eta)
# data
self.frames, self.inverted_latents, self.zs = frames, inverted_latents, zs
self.latents_path = self.get_latents_path()
# load frames
self.paths, self.frames, self.latents, self.eps = self.get_data()
if self.sd_version == 'depth':
self.depth_maps = self.prepare_depth_maps()
self.text_embeds = self.get_text_embeds(config["prompt"], config["negative_prompt"])
# pnp_inversion_prompt = self.get_pnp_inversion_prompt()
self.pnp_guidance_embeds = self.get_text_embeds(config["pnp_inversion_prompt"], config["pnp_inversion_prompt"]).chunk(2)[0]
@torch.no_grad()
def prepare_depth_maps(self, model_type='DPT_Large', device='cuda'):
depth_maps = []
midas = torch.hub.load("intel-isl/MiDaS", model_type)
midas.to(device)
midas.eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
if model_type == "DPT_Large" or model_type == "DPT_Hybrid":
transform = midas_transforms.dpt_transform
else:
transform = midas_transforms.small_transform
for i in range(len(self.paths)):
img = cv2.imread(self.paths[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
latent_h = img.shape[0] // 8
latent_w = img.shape[1] // 8
input_batch = transform(img).to(device)
prediction = midas(input_batch)
depth_map = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=(latent_h, latent_w),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
depth_maps.append(depth_map)
return torch.cat(depth_maps).to(torch.float16).to(self.device)
def get_pnp_inversion_prompt(self):
inv_prompts_path = os.path.join(str(Path(self.latents_path).parent), 'inversion_prompt.txt')
# read inversion prompt
with open(inv_prompts_path, 'r') as f:
inv_prompt = f.read()
return inv_prompt
def get_latents_path(self):
read_from_files = self.frames is None
if read_from_files:
latents_path = os.path.join(self.config["latents_path"], f'sd_{self.config["sd_version"]}',
Path(self.config["data_path"]).stem, f'steps_{self.config["n_inversion_steps"]}')
latents_path = [x for x in glob.glob(f'{latents_path}/*') if '.' not in Path(x).name]
n_frames = [int([x for x in latents_path[i].split('/') if 'nframes' in x][0].split('_')[1]) for i in range(len(latents_path))]
latents_path = latents_path[np.argmax(n_frames)]
self.config["n_frames"] = min(max(n_frames), self.config["n_frames"])
else:
n_frames = self.frames.shape[0]
self.config["n_frames"] = min(n_frames, self.config["n_frames"])
if self.config["n_frames"] % self.config["batch_size"] != 0:
# make n_frames divisible by batch_size
self.config["n_frames"] = self.config["n_frames"] - (self.config["n_frames"] % self.config["batch_size"])
if read_from_files:
return os.path.join(latents_path, 'latents')
else:
return None
@torch.no_grad()
def get_text_embeds(self, prompt, negative_prompt, batch_size=1):
# Tokenize text and get embeddings
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
truncation=True, return_tensors='pt')
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# Do the same for unconditional embeddings
uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
return_tensors='pt')
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# Cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings] * batch_size + [text_embeddings] * batch_size)
return text_embeddings
@torch.no_grad()
def encode_imgs(self, imgs, batch_size=VAE_BATCH_SIZE, deterministic=False):
imgs = 2 * imgs - 1
latents = []
for i in range(0, len(imgs), batch_size):
posterior = self.vae.encode(imgs[i:i + batch_size]).latent_dist
latent = posterior.mean if deterministic else posterior.sample()
latents.append(latent * 0.18215)
latents = torch.cat(latents)
return latents
@torch.no_grad()
def decode_latents(self, latents, batch_size=VAE_BATCH_SIZE):
latents = 1 / 0.18215 * latents
imgs = []
for i in range(0, len(latents), batch_size):
imgs.append(self.vae.decode(latents[i:i + batch_size]).sample)
imgs = torch.cat(imgs)
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
def get_data(self):
read_from_files = self.frames is None
# read_from_files = True
if read_from_files:
# load frames
paths = [os.path.join(self.config["data_path"], "%05d.jpg" % idx) for idx in
range(self.config["n_frames"])]
if not os.path.exists(paths[0]):
paths = [os.path.join(self.config["data_path"], "%05d.png" % idx) for idx in
range(self.config["n_frames"])]
frames = [Image.open(paths[idx]).convert('RGB') for idx in range(self.config["n_frames"])]
if frames[0].size[0] == frames[0].size[1]:
frames = [frame.resize((512, 512), resample=Image.Resampling.LANCZOS) for frame in frames]
frames = torch.stack([T.ToTensor()(frame) for frame in frames]).to(torch.float16).to(self.device)
save_video(frames, f'{self.config["output_path"]}/input_fps10.mp4', fps=10)
save_video(frames, f'{self.config["output_path"]}/input_fps20.mp4', fps=20)
save_video(frames, f'{self.config["output_path"]}/input_fps30.mp4', fps=30)
else:
frames = self.frames
# encode to latents
latents = self.encode_imgs(frames, deterministic=True).to(torch.float16).to(self.device)
# get noise
if self.inversion == 'ddim':
eps = self.get_ddim_eps(latents, range(self.config["n_frames"])).to(torch.float16).to(self.device)
elif self.inversion == 'ddpm':
eps = self.get_ddpm_noise()
else:
raise NotImplementedError()
if not read_from_files:
return None, frames, latents, eps
return paths, frames, latents, eps
def get_ddim_eps(self, latent, indices):
read_from_files = self.inverted_latents is None
if read_from_files:
noisest = max([int(x.split('_')[-1].split('.')[0]) for x in glob.glob(os.path.join(self.latents_path, f'noisy_latents_*.pt'))])
latents_path = os.path.join(self.latents_path, f'noisy_latents_{noisest}.pt')
noisy_latent = torch.load(latents_path)[indices].to(self.device)
else:
noisest = max([int(key.split("_")[-1]) for key in self.inverted_latents.keys()])
noisy_latent = self.inverted_latents[f'noisy_latents_{noisest}'][indices]
alpha_prod_T = self.scheduler.alphas_cumprod[noisest]
mu_T, sigma_T = alpha_prod_T ** 0.5, (1 - alpha_prod_T) ** 0.5
eps = (noisy_latent - mu_T * latent) / sigma_T
return eps
def get_ddpm_noise(self):
read_from_files = self.inverted_latents is None
idx_to_t = {int(k): int(v) for k, v in enumerate(self.scheduler.timesteps)}
t = idx_to_t[self.skip_steps]
if read_from_files:
x0_path = os.path.join(self.latents_path, f'noisy_latents_{t}.pt')
zs_path = os.path.join(self.latents_path, f'noise_total.pt')
x0 = torch.load(x0_path)[:self.config["n_frames"]].to(self.device)
zs = torch.load(zs_path)[self.skip_steps:, :self.config["n_frames"]].to(self.device)
else:
x0 = self.inverted_latents[f'noisy_latents_{t}'][:self.config["n_frames"]].to(self.device)
zs = self.zs[self.skip_steps:, :self.config["n_frames"]].to(self.device)
return x0, zs
def prepare_extra_step_kwargs(self, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
return extra_step_kwargs
@torch.no_grad()
def denoise_step(self, x, t, indices, zs=None):
# register the time step and features in pnp injection modules
read_files = self.inverted_latents is None
if read_files:
source_latents = load_source_latents_t(t, self.latents_path)[indices]
else:
source_latents = self.inverted_latents[f'noisy_latents_{t}'][indices]
latent_model_input = torch.cat([source_latents] + ([x] * 2))
if self.sd_version == 'depth':
latent_model_input = torch.cat([latent_model_input, torch.cat([self.depth_maps[indices]] * 3)], dim=1)
register_time(self, t.item())
# compute text embeddings
text_embed_input = torch.cat([self.pnp_guidance_embeds.repeat(len(indices), 1, 1),
torch.repeat_interleave(self.text_embeds, len(indices), dim=0)])
# apply the denoising network
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input)['sample']
# perform guidance
_, noise_pred_uncond, noise_pred_cond = noise_pred.chunk(3)
noise_pred = noise_pred_uncond + self.config["guidance_scale"] * (noise_pred_cond - noise_pred_uncond)
# compute the denoising step with the reference model
denoised_latent = self.scheduler.step(noise_pred, t, x, variance_noise=zs, **self.extra_step_kwargs)[
'prev_sample']
return denoised_latent
@torch.autocast(dtype=torch.float16, device_type='cuda')
def batched_denoise_step(self, x, t, indices, zs=None):
batch_size = self.config["batch_size"]
denoised_latents = []
pivotal_idx = torch.randint(batch_size, (len(x) // batch_size,)) + torch.arange(0, len(x), batch_size)
register_pivotal(self, True)
if zs is None:
zs_input = None
else:
zs_input = zs[pivotal_idx]
self.denoise_step(x[pivotal_idx], t, indices[pivotal_idx], zs_input)
register_pivotal(self, False)
for i, b in enumerate(range(0, len(x), batch_size)):
register_batch_idx(self, i)
if zs is None:
zs_input = None
else:
zs_input = zs[b:b + batch_size]
denoised_latents.append(self.denoise_step(x[b:b + batch_size], t, indices[b:b + batch_size]
, zs_input))
denoised_latents = torch.cat(denoised_latents)
return denoised_latents
def init_method(self, conv_injection_t, qk_injection_t):
self.qk_injection_timesteps = self.scheduler.timesteps[:qk_injection_t] if qk_injection_t >= 0 else []
self.conv_injection_timesteps = self.scheduler.timesteps[:conv_injection_t] if conv_injection_t >= 0 else []
register_extended_attention_pnp(self, self.qk_injection_timesteps)
register_conv_injection(self, self.conv_injection_timesteps)
set_tokenflow(self.unet)
def save_vae_recon(self):
os.makedirs(f'{self.config["output_path"]}/vae_recon', exist_ok=True)
decoded = self.decode_latents(self.latents)
for i in range(len(decoded)):
T.ToPILImage()(decoded[i]).save(f'{self.config["output_path"]}/vae_recon/%05d.png' % i)
save_video(decoded, f'{self.config["output_path"]}/vae_recon_10.mp4', fps=10)
save_video(decoded, f'{self.config["output_path"]}/vae_recon_20.mp4', fps=20)
save_video(decoded, f'{self.config["output_path"]}/vae_recon_30.mp4', fps=30)
def edit_video(self):
save_files = self.inverted_latents is None # if we're in the original non-demo setting
if save_files:
os.makedirs(f'{self.config["output_path"]}/img_ode', exist_ok=True)
self.save_vae_recon()
# self.save_vae_recon()
pnp_f_t = int(self.config["n_timesteps"] * self.config["pnp_f_t"])
pnp_attn_t = int(self.config["n_timesteps"] * self.config["pnp_attn_t"])
self.init_method(conv_injection_t=pnp_f_t, qk_injection_t=pnp_attn_t)
if self.inversion == 'ddim':
noisy_latents = self.scheduler.add_noise(self.latents, self.eps, self.scheduler.timesteps[0])
elif self.inversion == 'ddpm':
noisy_latents = self.eps[0]
else:
raise NotImplementedError()
edited_frames = self.sample_loop(noisy_latents, torch.arange(self.config["n_frames"]))
if save_files:
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_PnP_fps_10.mp4')
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_PnP_fps_20.mp4', fps=20)
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_PnP_fps_30.mp4', fps=30)
print('Done!')
else:
return edited_frames
def sample_loop(self, x, indices):
save_files = self.inverted_latents is None # if we're in the original non-demo settinge
if save_files:
os.makedirs(f'{self.config["output_path"]}/img_ode', exist_ok=True)
timesteps = self.scheduler.timesteps
if self.inversion == 'ddpm':
zs_total = self.eps[1]
t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs_total.shape[0]:])}
timesteps = timesteps[-zs_total.shape[0]:]
for i, t in enumerate(tqdm(timesteps, desc="Sampling")):
if self.inversion == 'ddpm':
idx = t_to_idx[int(t)]
zs = zs_total[idx]
else:
zs = None
x = self.batched_denoise_step(x, t, indices, zs)
decoded_latents = self.decode_latents(x)
if save_files:
for i in range(len(decoded_latents)):
T.ToPILImage()(decoded_latents[i]).save(f'{self.config["output_path"]}/img_ode/%05d.png' % i)
return decoded_latents
# def run(config):
# seed_everything(config["seed"])
# print(config)
# editor = TokenFlow(config)
# editor.edit_video()
# if __name__ == '__main__':
# parser = argparse.ArgumentParser()
# parser.add_argument('--config_path', type=str, default='configs/config_pnp.yaml')
# opt = parser.parse_args()
# with open(opt.config_path, "r") as f:
# config = yaml.safe_load(f)
# config["output_path"] = os.path.join(config["output_path"] + f'_pnp_SD_{config["sd_version"]}',
# Path(config["data_path"]).stem,
# config["prompt"][:240],
# f'attn_{config["pnp_attn_t"]}_f_{config["pnp_f_t"]}',
# f'batch_size_{str(config["batch_size"])}',
# str(config["n_timesteps"]),
# )
# os.makedirs(config["output_path"], exist_ok=True)
# print(config["data_path"])
# assert os.path.exists(config["data_path"]), "Data path does not exist"
# with open(os.path.join(config["output_path"], "config.yaml"), "w") as f:
# yaml.dump(config, f)
# run(config)