File size: 5,542 Bytes
39ad784 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import os
import argparse
import torch
import torchvision
import json
from einops import rearrange
from diffusers import DDIMScheduler, AutoencoderKL, DDIMInverseScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from models.pipeline_flatten import FlattenPipeline
from models.util import save_videos_grid, read_video, sample_trajectories
from models.unet import UNet3DConditionModel
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--jsonl_path", type=str, help="Path to the JSONL file containing video paths and prompts")
parser.add_argument("--prompt", type=str, help="Textual prompt for video editing")
parser.add_argument("--neg_prompt", type=str, default="none", help="Negative prompt for guidance")
parser.add_argument("--guidance_scale", default=20.0, type=float, help="Guidance scale")
parser.add_argument("--video_path", type=str, help="Path to a source video")
parser.add_argument("--sd_path", type=str, default="checkpoints/stable-diffusion-2-1-base", help="Path of Stable Diffusion")
parser.add_argument("--output_path", type=str, default="./outputs", help="Directory of output")
parser.add_argument("--video_length", type=int, default=32, help="Length of output video")
parser.add_argument("--old_qk", type=int, default=0, help="Whether to use old queries and keys for flow-guided attention")
parser.add_argument("--height", type=int, default=512, help="Height of synthesized video, and should be a multiple of 32")
parser.add_argument("--width", type=int, default=512, help="Width of synthesized video, and should be a multiple of 32")
parser.add_argument("--sample_steps", type=int, default=50, help="Steps for feature injection")
parser.add_argument("--inject_step", type=int, default=40, help="Steps for feature injection")
parser.add_argument("--seed", type=int, default=66, help="Random seed of generator")
parser.add_argument("--frame_rate", type=int, default=2, help="The frame rate of loading input video")
parser.add_argument("--fps", type=int, default=15, help="FPS of the output video")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
os.makedirs(args.output_path, exist_ok=True)
device = "cuda"
args.height = (args.height // 32) * 32
args.width = (args.width // 32) * 32
tokenizer = CLIPTokenizer.from_pretrained(args.sd_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.sd_path, subfolder="text_encoder").to(dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(args.sd_path, subfolder="vae").to(dtype=torch.float16)
unet = UNet3DConditionModel.from_pretrained_2d(args.sd_path, subfolder="unet").to(dtype=torch.float16)
scheduler = DDIMScheduler.from_pretrained(args.sd_path, subfolder="scheduler")
inverse = DDIMInverseScheduler.from_pretrained(args.sd_path, subfolder="scheduler")
pipe = FlattenPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
scheduler=scheduler, inverse_scheduler=inverse)
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
pipe.to(device)
generator = torch.Generator(device=device)
generator.manual_seed(args.seed)
if args.jsonl_path:
with open(args.jsonl_path, 'r') as f:
lines = f.readlines()
video_tasks = [json.loads(line) for line in lines]
else:
if not args.video_path or not args.prompt:
raise ValueError("For single video editing, --video_path and --prompt are required")
video_tasks = [{"video": args.video_path, "edit_prompt": args.prompt}]
base_dir = "/home/wangjuntong/video_editing_dataset/all_sourse/"
os.makedirs("tmp", exist_ok=True)
for task in video_tasks:
video_path = os.path.join(base_dir, task["video"])
prompt = task["edit_prompt"]
video = read_video(video_path=video_path, video_length=args.video_length,
width=args.width, height=args.height, frame_rate=args.frame_rate)
original_pixels = rearrange(video, "(b f) c h w -> b c f h w", b=1)
video_name = os.path.splitext(os.path.basename(video_path))[0]
source_video_path = os.path.join(args.output_path, f"{video_name}_source.mp4")
save_videos_grid(original_pixels, source_video_path, rescale=True)
t2i_transform = torchvision.transforms.ToPILImage()
real_frames = []
for i, frame in enumerate(video):
real_frames.append(t2i_transform(((frame+1)/2*255).to(torch.uint8)))
temp_dir = os.path.join("tmp", video_name)
os.makedirs(temp_dir, exist_ok=True)
trajectories = sample_trajectories(source_video_path, device)
torch.cuda.empty_cache()
for k in trajectories.keys():
trajectories[k] = trajectories[k].to(device)
sample = pipe(prompt, video_length=args.video_length, frames=real_frames,
num_inference_steps=args.sample_steps, generator=generator, guidance_scale=args.guidance_scale,
negative_prompt=args.neg_prompt, width=args.width, height=args.height,
trajs=trajectories, output_dir=temp_dir, inject_step=args.inject_step, old_qk=args.old_qk).videos
output_video_path = os.path.join(args.output_path, f"{video_name}_edited.mp4")
save_videos_grid(sample, output_video_path, fps=args.fps) |