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import os
import sys
from pathlib import Path
import torch
import argparse
import logging
from omegaconf import OmegaConf
from PIL import Image
import json
# HF imports
from diffusers import (
DDIMInverseScheduler,
DDIMScheduler,
)
from diffusers.utils import load_image, export_to_video, export_to_gif
# Project imports
from utils import (
seed_everything,
load_video_frames,
convert_video_to_frames,
load_ddim_latents_at_T,
load_ddim_latents_at_t,
)
from pipelines.pipeline_i2vgen_xl import I2VGenXLPipeline
def ddim_inversion(config, first_frame, frame_list, pipe: I2VGenXLPipeline, inverse_scheduler, g):
pipe.scheduler = inverse_scheduler
video_latents_at_0 = pipe.encode_vae_video(
frame_list,
device=pipe._execution_device,
height=config.image_size[1],
width=config.image_size[0],
)
ddim_latents = pipe.invert(
prompt=config.prompt,
image=first_frame,
height=config.image_size[1],
width=config.image_size[0],
num_frames=config.n_frames,
num_inference_steps=config.n_steps,
guidance_scale=config.cfg,
negative_prompt=config.negative_prompt,
target_fps=config.target_fps,
latents=video_latents_at_0,
generator=g, # TODO: this is not correct
return_dict=False,
output_dir=config.output_dir,
) # [b, num_inference_steps, c, num_frames, h, w]
logger = logging.getLogger(__name__)
logger.debug(f"ddim_latents.shape: {ddim_latents.shape}")
ddim_latents = ddim_latents[0] # [num_inference_steps, c, num_frames, h, w]
return ddim_latents
def ddim_sampling(
config, first_frame, ddim_latents_at_T, pipe: I2VGenXLPipeline, ddim_scheduler, ddim_init_latents_t_idx, g
):
pipe.scheduler = ddim_scheduler
reconstructed_video = pipe(
prompt=config.prompt,
image=first_frame,
height=config.image_size[1],
width=config.image_size[0],
num_frames=config.n_frames,
num_inference_steps=config.n_steps,
guidance_scale=config.cfg,
negative_prompt=config.negative_prompt,
target_fps=config.target_fps,
latents=ddim_latents_at_T,
generator=g, # TODO: this is not correct
return_dict=True,
ddim_init_latents_t_idx=ddim_init_latents_t_idx,
).frames[0]
return reconstructed_video
def main(template_config, configs_list):
# Initialize the pipeline
pipe = I2VGenXLPipeline.from_pretrained(
"ali-vilab/i2vgen-xl",
torch_dtype=torch.float16,
variant="fp16",
)
pipe.to(device)
g = torch.Generator(device=device)
g = g.manual_seed(template_config.seed)
# Initialize the DDIM inverse scheduler
inverse_scheduler = DDIMInverseScheduler.from_pretrained(
"ali-vilab/i2vgen-xl",
subfolder="scheduler",
)
# Initialize the DDIM scheduler
ddim_scheduler = DDIMScheduler.from_pretrained(
"ali-vilab/i2vgen-xl",
subfolder="scheduler",
)
video_dir = template_config.video_dir
assert os.path.exists(video_dir), f"video_dir: {video_dir} does not exist"
# loop through the video_dir and process every mp4 file
for config_entry in configs_list:
if config_entry["active"] == False:
logger.info(f"Skipping config_entry: {config_entry}")
continue
logger.info(f"Processing config_entry: {config_entry}")
# Override the config with the data_meta_entry
config = OmegaConf.merge(template_config, OmegaConf.create(config_entry))
config.video_path = os.path.join(config.video_dir, config.video_name + ".mp4")
config.video_frames_path = os.path.join(config.video_dir, config.video_name)
# If already computed the latents, skip
if os.path.exists(config.output_dir) and not config.force_recompute_latents:
logger.info(f"### Skipping !!! {config.output_dir} already exists. ")
continue
logger.info(f"config: {OmegaConf.to_yaml(config)}")
# This is the same as run_ddim_inversion.py
try:
logger.info(f"Loading frames from: {config.video_frames_path}")
_, frame_list = load_video_frames(config.video_frames_path, config.n_frames, config.image_size)
except:
logger.error(f"Failed to load frames from: {config.video_frames_path}")
logger.info(f"Converting mp4 video to frames: {config.video_path}")
frame_list = convert_video_to_frames(config.video_path, config.image_size, save_frames=True)
frame_list = frame_list[: config.n_frames] # 16 frames for img2vid
logger.debug(f"len(frame_list): {len(frame_list)}")
# Save the source frames as GIF
export_to_gif(
frame_list,
os.path.join(config.video_frames_path, config.video_name + ".gif")
)
logger.info(f"Saved source video as gif to {config.video_frames_path}")
first_frame = frame_list[0] # Is a PIL image
# Produce static video
if config.inverse_config.inverse_static_video:
logger.info("### Inverse a static video!")
frame_list = [frame_list[0]] * config.n_frames
# Null image inversion
if config.inverse_config.null_image_inversion:
logger.info("### Inverse a null image!")
first_frame = Image.new("RGB", (config.image_size[0], config.image_size[1]), (0, 0, 0))
# Main pipeline
# Inversion
logger.info(f"config: {OmegaConf.to_yaml(config)}")
_ddim_latents = ddim_inversion(config.inverse_config, first_frame, frame_list, pipe, inverse_scheduler, g)
# Reconstruction
recon_config = config.recon_config
if recon_config.enable_recon:
ddim_init_latents_t_idx = recon_config.ddim_init_latents_t_idx
ddim_scheduler.set_timesteps(recon_config.n_steps)
logger.info(f"ddim_scheduler.timesteps: {ddim_scheduler.timesteps}")
ddim_latents_path = recon_config.ddim_latents_path
ddim_latents_at_t = load_ddim_latents_at_t(
ddim_scheduler.timesteps[ddim_init_latents_t_idx],
ddim_latents_path=ddim_latents_path,
)
logger.debug(f"ddim_scheduler.timesteps[t_idx]: {ddim_scheduler.timesteps[ddim_init_latents_t_idx]}")
reconstructed_video = ddim_sampling(
recon_config,
first_frame,
ddim_latents_at_t,
pipe,
ddim_scheduler,
ddim_init_latents_t_idx,
g,
)
# Save the reconstructed video
os.makedirs(config.output_dir, exist_ok=True)
# Downsampling the video for space saving
reconstructed_video = [frame.resize((512, 512), resample=Image.LANCZOS) for frame in reconstructed_video]
export_to_video(
reconstructed_video,
os.path.join(config.output_dir, "ddim_reconstruction.mp4"),
fps=10,
)
export_to_gif(
reconstructed_video,
os.path.join(config.output_dir, "ddim_reconstruction.gif"),
)
logger.info(f"Saved reconstructed video to {config.output_dir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--template_config", type=str, default="./configs/group_ddim_inversion/template.yaml")
parser.add_argument("--configs_json", type=str, default="./configs/group_config.json") # This is going to override the template_config
args = parser.parse_args()
template_config = OmegaConf.load(args.template_config)
# Set up logging
logging_level = logging.DEBUG if template_config.debug else logging.INFO
logging.basicConfig(level=logging_level, format="%(asctime)s - %(levelname)s - [%(funcName)s] - %(message)s")
logger = logging.getLogger(__name__)
logger.info(f"template_config: {OmegaConf.to_yaml(template_config)}")
# Load data jsonl into list
configs_json = args.configs_json
assert Path(configs_json).exists()
with open(configs_json, 'r') as file:
configs_list = json.load(file)
logger.info(f"Loaded {len(configs_list)} configs from {configs_json}")
# Set up device and seed
device = torch.device(template_config.device)
torch.set_grad_enabled(False)
seed_everything(template_config.seed)
main(template_config, configs_list)
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