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import os
import time
import yaml, math
from tqdm import trange
import torch
import numpy as np
from omegaconf import OmegaConf
import torch.distributed as dist
from pytorch_lightning import seed_everything
from lvdm.samplers.ddim import DDIMSampler
from lvdm.utils.common_utils import str2bool
from lvdm.utils.saving_utils import npz_to_video_grid, npz_to_imgsheet_5d
from scripts.sample_text2video import sample_text2video
from scripts.sample_utils import load_model, get_conditions, make_model_input_shape, torch_to_np
from lvdm.models.modules.lora import change_lora, change_lora_v2
from huggingface_hub import hf_hub_download
def save_results(videos, save_dir,
save_name="results", save_fps=8
):
save_subdir = os.path.join(save_dir, "videos")
os.makedirs(save_subdir, exist_ok=True)
for i in range(videos.shape[0]):
npz_to_video_grid(videos[i:i+1,...],
os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4"),
fps=save_fps)
print(f'Successfully saved videos in {save_subdir}')
video_path_list = [os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4") for i in range(videos.shape[0])]
return video_path_list
class Text2Video():
def __init__(self,result_dir='./tmp/') -> None:
self.download_model()
config_file = 'models/base_t2v/model_config.yaml'
ckpt_path = 'models/base_t2v/model.ckpt'
config = OmegaConf.load(config_file)
self.lora_path_list = ['','models/videolora/lora_001_Loving_Vincent_style.ckpt',
'models/videolora/lora_002_frozenmovie_style.ckpt',
'models/videolora/lora_003_MakotoShinkaiYourName_style.ckpt',
'models/videolora/lora_004_coco_style.ckpt']
self.lora_trigger_word_list = ['','Loving Vincent style', 'frozenmovie style', 'MakotoShinkaiYourName style', 'coco style']
model, _, _ = load_model(config, ckpt_path, gpu_id=0, inject_lora=False)
self.model = model
self.last_time_lora = ''
self.last_time_lora_scale = 1.0
self.result_dir = result_dir
self.save_fps = 8
self.ddim_sampler = DDIMSampler(model)
self.origin_weight = None
def get_prompt(self, input_text, steps=50, model_index=0, eta=1.0, cfg_scale=15.0, lora_scale=1.0):
if model_index > 0:
input_text = input_text + ', ' + self.lora_trigger_word_list[model_index]
inject_lora = model_index > 0
self.origin_weight = change_lora_v2(self.model, inject_lora=inject_lora, lora_scale=lora_scale, lora_path=self.lora_path_list[model_index],
last_time_lora=self.last_time_lora, last_time_lora_scale=self.last_time_lora_scale, origin_weight=self.origin_weight)
all_videos = sample_text2video(self.model, input_text, n_samples=1, batch_size=1,
sample_type='ddim', sampler=self.ddim_sampler,
ddim_steps=steps, eta=eta,
cfg_scale=cfg_scale,
)
prompt = input_text
prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt
prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str
self.last_time_lora=self.lora_path_list[model_index]
self.last_time_lora_scale = lora_scale
video_path_list = save_results(all_videos, self.result_dir, save_name=prompt_str, save_fps=self.save_fps)
return video_path_list[0]
def download_model(self):
REPO_ID = 'VideoCrafter/t2v-version-1-1'
filename_list = ['models/base_t2v/model.ckpt',
'models/videolora/lora_001_Loving_Vincent_style.ckpt',
'models/videolora/lora_002_frozenmovie_style.ckpt',
'models/videolora/lora_003_MakotoShinkaiYourName_style.ckpt',
'models/videolora/lora_004_coco_style.ckpt']
for filename in filename_list:
if not os.path.exists(filename):
hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./', local_dir_use_symlinks=False)
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