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import glob
import os
from pathlib import Path
import torch
import torch.nn as nn
import torchvision.transforms as T
import argparse
from PIL import Image
import yaml
from tqdm import tqdm
from transformers import logging
from diffusers import DDIMScheduler, StableDiffusionPipeline
import numpy as np
from tokenflow_utils import *
from util import save_video, seed_everything
# suppress partial model loading warning
logging.set_verbosity_error()
VAE_BATCH_SIZE = 10
# UNET_BATCH_SIZE = 5
class TokenFlow(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.device = config["device"]
sd_version = config["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"
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)
# start from the config["start"] * len(timesteps) timestep
self.scheduler.timesteps = self.scheduler.timesteps[int(1 - config["start"] * len(self.scheduler.timesteps)):]
print('SD model loaded')
# data
self.latents_path = self.get_latents_path()
self.keyframes_path = [os.path.join(config["data_path"], "%05d.jpg" % idx) for idx in
range(self.config["n_frames"])]
if not os.path.exists(self.keyframes_path[0]):
self.keyframes_path = [os.path.join(config["data_path"], "%05d.png" % idx) for idx in
range(self.config["n_frames"])]
# load frames
self.frames, self.latents, self.eps = self.get_data()
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(pnp_inversion_prompt, pnp_inversion_prompt).chunk(2)[0]
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):
latents_path = os.path.join(config["latents_path"], f'sd_{config["sd_version"]}',
Path(config["data_path"]).stem,)
latents_path = [x for x in glob.glob(f'{latents_path}/*/*') if not x.startswith('.')]
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), 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"])
print(self.config["n_frames"])
return os.path.join(latents_path, 'latents')
@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):
# load frames
frames = [Image.open(self.keyframes_path[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)
# encode to latents
latents = self.encode_imgs(frames, deterministic=True).to(torch.float16).to(self.device)
# get noise
eps = self.get_ddim_eps(latents, range(self.config["n_frames"])).to(torch.float16).to(self.device)
return frames, latents, eps
def get_ddim_eps(self, latent, indices):
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)
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
@torch.no_grad()
def denoise_step(self, x, t, indices):
# register the time step and features in pnp injection modules
source_latents = load_source_latents_t(t, self.latents_path)[indices]
latent_model_input = torch.cat([source_latents] + ([x] * 2))
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)['prev_sample']
return denoised_latent
@torch.autocast(dtype=torch.float16, device_type='cuda')
def batched_denoise_step(self, x, t, indices):
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) # [int(x) for x in torch.tensor((range(batch_size // 2, len(x) + batch_size // 2, batch_size)))]
register_pivotal(self, True)
self.denoise_step(x[pivotal_idx], t, indices[pivotal_idx])
register_pivotal(self, False)
for i, b in enumerate(range(0, len(x), batch_size)):
register_batch_idx(self, i)
denoised_latents.append(self.denoise_step(x[b:b + batch_size], t, indices[b:b + batch_size]))
denoised_latents = torch.cat(denoised_latents)
return denoised_latents
def init_method(self):
register_extended_attention(self)
set_tokenflow(self.unet)
def edit_video(self):
os.makedirs(f'{self.config["output_path"]}/img_ode', exist_ok=True)
self.init_method()
noise = self.eps if config["use_ddim_noise"] else torch.randn_like(self.eps[[0]]).repeat(self.eps.shape[0])
noisy_latents = self.scheduler.add_noise(self.latents, noise, self.scheduler.timesteps[0])
edited_frames = self.sample_loop(noisy_latents, torch.arange(self.config["n_frames"]))
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_SDEdit_fps_10.mp4')
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_SDEdit_fps_20.mp4', fps=20)
save_video(edited_frames, f'{self.config["output_path"]}/tokenflow_SDEdit_fps_30.mp4', fps=30)
print('Done!')
def sample_loop(self, x, indices):
os.makedirs(f'{self.config["output_path"]}/img_ode', exist_ok=True)
for i, t in enumerate(tqdm(self.scheduler.timesteps, desc="Sampling")):
x = self.batched_denoise_step(x, t, indices)
decoded_latents = self.decode_latents(x)
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 per_frame_sde(self):
os.makedirs(f'{self.config["output_path"]}/img_ode', exist_ok=True)
noisy_latents = self.scheduler.add_noise(self.latents, self.eps, self.scheduler.timesteps[0])
edited_frames = self.vanilla_sample_loop(noisy_latents, torch.arange(self.config["n_frames"]))
save_video(edited_frames, f'{self.config["output_path"]}/vanilla_sde.mp4')
save_video(edited_frames, f'{self.config["output_path"]}/vanilla_sde_fps20.mp4', fps=20)
save_video(edited_frames, f'{self.config["output_path"]}/vanilla_sde_fps30.mp4', fps=30)
print('Done!')
def vanilla_denoise(self, batch, t, text_embed_input):
latent_model_input = torch.cat(([batch] * 2))
# 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(2)
noise_pred = noise_pred_uncond + self.config["guidance_scale"] * (noise_pred_cond - noise_pred_uncond)
# compute the denoising step with the reference model
batch = self.scheduler.step(noise_pred, t, batch)['prev_sample']
return batch
def batch_vanilla_denoise_step(self, x, t, text_embed_input):
denoised = []
for b in range(0, len(x), self.config["batch_size"]):
denoised.append(self.vanilla_denoise(x[b:b + self.config["batch_size"]], t, text_embed_input))
x = torch.cat(denoised)
return x
@torch.no_grad()
def vanilla_sample_loop(self, x, indices):
os.makedirs(f'{self.config["output_path"]}/img_ode_vanilla_sde', exist_ok=True)
text_embed_input = torch.cat([torch.repeat_interleave(self.text_embeds, config["batch_size"], dim=0)])
for i, t in enumerate(tqdm(self.scheduler.timesteps, desc="Sampling")):
x = self.batch_vanilla_denoise_step(x, t, text_embed_input)
decoded_latents = self.decode_latents(x)
for i in range(len(decoded_latents)):
T.ToPILImage()(decoded_latents[i]).save(f'{self.config["output_path"]}/img_ode_vanilla_sde/%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_sdedit.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"] + '_sdedit',
Path(config["data_path"]).stem,
config["prompt"][:240],
f'batch_size_{str(config["batch_size"])}',
str(config["n_timesteps"]) + f'start_{config["start"]}')
os.makedirs(config["output_path"], exist_ok=True)
with open(os.path.join(config["output_path"], "config.yaml"), "w") as f:
yaml.dump(config, f)
assert os.path.exists(config["data_path"]), "Data path does not exist"
run(config) |