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# General
import os
from os.path import join as opj
import argparse
import datetime
from pathlib import Path
import torch
import gradio as gr
import tempfile
import yaml
from model.video_ldm import VideoLDM
from typing import List, Optional
from model.callbacks import SaveConfigCallback
from PIL.Image import Image, fromarray
from einops import rearrange, repeat
import sys
from videogen_hub import MODEL_PATH
sys.path.append("thirdparty")
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys
import imageio
import pathlib
import numpy as np
# Utilities
from inference_utils import *
from model_init import *
from model_func import *
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
default="A cat running on the street",
help="The prompt to guide video generation.",
)
parser.add_argument(
"--image", type=str, default="", help="Path to image conditioning."
)
# parser.add_argument('--video', type=str, default="", help="Path to video conditioning.")
parser.add_argument(
"--base_model",
type=str,
default="ModelscopeT2V",
help="Base model to generate first chunk from",
choices=["ModelscopeT2V", "AnimateDiff", "SVD"],
)
parser.add_argument(
"--num_frames",
type=int,
default=24,
help="The number of video frames to generate.",
)
parser.add_argument(
"--negative_prompt",
type=str,
default="",
help="The prompt to guide what to not include in video generation.",
)
parser.add_argument(
"--negative_prompt_enhancer",
type=str,
default=None,
help="The prompt to guide what to not include in video enhancement. "
"By default is the same as --negative_prompt",
)
parser.add_argument(
"--num_steps", type=int, default=50, help="The number of denoising steps."
)
parser.add_argument(
"--image_guidance", type=float, default=9.0, help="The guidance scale."
)
parser.add_argument(
"--output_dir",
type=str,
default="results",
help="Path where to save the generated videos.",
)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--seed", type=int, default=33, help="Random seed")
parser.add_argument(
"--chunk", type=int, default=24, help="chunk_size for randomized blending"
)
parser.add_argument(
"--overlap", type=int, default=8, help="overlap_size for randomized blending"
)
parser.add_argument(
"--offload_models",
action="store_true",
help="Load/Offload models to gpu/cpu before and after inference",
)
args = parser.parse_args()
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
result_fol = Path(args.output_dir).absolute()
device = args.device
# --------------------------
# ----- Configurations -----
# --------------------------
ckpt_file_streaming_t2v = os.path.join(MODEL_PATH, "streaming_t2v.ckpt")
cfg_v2v = {
"downscale": 1,
"upscale_size": (1280, 720),
"model_id": "damo/Video-to-Video",
"pad": True,
}
# --------------------------
# ----- Initialization -----
# --------------------------
if args.base_model == "ModelscopeT2V":
if args.offload_models:
model = init_modelscope("cpu")
else:
model = init_modelscope(device)
elif args.base_model == "AnimateDiff":
if args.offload_models:
model = init_animatediff("cpu")
else:
model = init_animatediff(device)
elif args.base_model == "SVD":
if args.offload_models:
model = init_svd("cpu")
sdxl_model = init_sdxl("cpu")
else:
model = init_svd(device)
sdxl_model = init_sdxl(device)
if args.offload_models:
msxl_model = init_v2v_model(cfg_v2v, "cpu")
else:
msxl_model = init_v2v_model(cfg_v2v, device)
stream_cli, stream_model = init_streamingt2v_model(
ckpt_file_streaming_t2v, result_fol, "cuda"
)
if args.offload_models:
stream_model = st2v_to_device(stream_model, "cpu")
inference_generator = torch.Generator(device="cuda")
# ------------------
# ----- Inputs -----
# ------------------
now = datetime.datetime.now()
name = (
args.prompt[:100].replace(" ", "_")
+ "_"
+ str(now.time()).replace(":", "_").replace(".", "_")
)
inference_generator = torch.Generator(device="cuda")
inference_generator.manual_seed(args.seed)
if args.offload_models:
model = model.to(device)
if args.base_model == "ModelscopeT2V":
short_video = ms_short_gen(args.prompt, model, inference_generator)
elif args.base_model == "AnimateDiff":
short_video = ad_short_gen(args.prompt, model, inference_generator)
elif args.base_model == "SVD":
if args.offload_models:
sdxl_model = sdxl_model.to(device)
short_video = svd_short_gen(
args.image, args.prompt, model, sdxl_model, inference_generator
)
if args.offload_models:
sdxl_model = sdxl_model.to("cpu")
if args.offload_models:
model = model.to("cpu")
n_autoreg_gen = (args.num_frames - 8) // 8
stream_long_gen(
args.prompt,
short_video,
n_autoreg_gen,
args.negative_prompt,
args.seed,
args.num_steps,
args.image_guidance,
name,
stream_cli,
stream_model,
)
if args.offload_models:
stream_model = st2v_to_device(stream_model, "cpu")
args.negative_prompt_enhancer = (
args.negative_prompt_enhancer
if args.negative_prompt_enhancer is not None
else args.negative_prompt
)
if args.offload_models:
msxl_model = v2v_to_device(msxl_model, device)
video2video_randomized(
args.prompt,
opj(result_fol, name + ".mp4"),
result_fol,
cfg_v2v,
msxl_model,
chunk_size=args.chunk,
overlap_size=args.overlap,
negative_prompt=args.negative_prompt_enhancer,
)
if args.offload_models:
msxl_model = v2v_to_device(msxl_model, "cpu")
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