asp化妆品网站,wordpress微软雅黑,优化是什么意思?,个人网站虚拟主机FancyVideo是一个由360AI团队和中山大学联合开发并开源的视频生成模型。
FancyVideo的创新之处在于它能够实现帧特定的文本指导#xff0c;使得生成的视频既动态又具有一致性。
FancyVideo模型通过精心设计的跨帧文本引导模块#xff08;Cross-frame Textual Guidance Modu…FancyVideo是一个由360AI团队和中山大学联合开发并开源的视频生成模型。
FancyVideo的创新之处在于它能够实现帧特定的文本指导使得生成的视频既动态又具有一致性。
FancyVideo模型通过精心设计的跨帧文本引导模块Cross-frame Textual Guidance Module, CTGM改进了现有的文本控制机制以解决现有文本到视频T2V模型在生成具有连贯运动视频时面临的挑战。
CTGM包含三个子模块时间信息注入器Temporal Information Injector, TII、时间亲和力细化器Temporal Affinity Refiner, TAR和时间特征增强器Temporal Feature Booster, TFB分别在交叉注意的开始、中间和结束时实现帧特定文本指导。
FancyVideo在EvalCrafter基准测试上取得了最先进的T2V生成结果并能够合成动态和一致的视频。
github项目地址https://github.com/360CVGroup/FancyVideo。
一、环境安装
1、python环境
建议安装python版本在3.10以上。
2、pip库安装
pip install torch2.1.2cu118 torchvision0.16.2cu118 torchaudio2.1.2 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
3、fancyvideo模型下载
git lfs install
git clone https://huggingface.co/qihoo360/FancyVideo
4、stable-diffusion-v1-5模型下载
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
二、功能测试
1、运行测试
1python代码调用测试
import os
import argparse
import torch
import yaml
from skimage import img_as_ubyte
from fancyvideo.pipelines.fancyvideo_infer_pipeline import InferPipelinedef load_config(config_path):with open(config_path, r) as fp:return yaml.safe_load(fp)def load_prompts(prompt_path):with open(prompt_path, r) as fp:return [line.strip() for line in fp.readlines()]def check_and_create_folder(folder_path):if not os.path.exists(folder_path):os.makedirs(folder_path, exist_okTrue)torch.no_grad()
def process_prompt(infer_pipeline, prompt, reference_image_path, seed, video_length, resolution, use_noise_scheduler_snr, cond_fps, cond_motion_score, output_fps, dst_path):print(fProcessing prompt: {prompt})reference_image, video, _ infer_pipeline.t2v_process_one_prompt(promptprompt,reference_image_pathreference_image_path,seedseed,video_lengthvideo_length,resolutionresolution,use_noise_scheduler_snruse_noise_scheduler_snr,fpscond_fps,motion_scorecond_motion_score)frame_list [img_as_ubyte(frame.cpu().permute(1, 2, 0).float().detach().numpy()) for frame in video]infer_pipeline.save_video(frame_listframe_list, fpsoutput_fps, dst_pathdst_path)print(fSaved video to: {dst_path}\n)torch.no_grad()
def main(args):# Load configurationsconfig load_config(args.config)model_config config.get(model, {})infer_config config.get(inference, {})# Initialize inference pipelineinfer_pipeline InferPipeline(text_to_video_mm_pathmodel_config.get(text_to_video_mm_path),base_model_pathmodel_config.get(base_model_path),res_adapter_typemodel_config.get(res_adapter_type),trained_keysmodel_config.get(trained_keys),model_pathmodel_config.get(model_path),vae_typemodel_config.get(vae_type),use_fps_embeddingmodel_config.get(use_fps_embedding),use_motion_embeddingmodel_config.get(use_motion_embedding),common_positive_promptmodel_config.get(common_positive_prompt),common_negative_promptmodel_config.get(common_negative_prompt),)# Prepare inference parametersinfer_mode infer_config.get(infer_mode)resolution infer_config.get(resolution)video_length infer_config.get(video_length)output_fps infer_config.get(output_fps)cond_fps infer_config.get(cond_fps)cond_motion_score infer_config.get(cond_motion_score)use_noise_scheduler_snr infer_config.get(use_noise_scheduler_snr)seed infer_config.get(seed)prompt_path infer_config.get(prompt_path)reference_image_folder infer_config.get(reference_image_folder)output_folder infer_config.get(output_folder)check_and_create_folder(output_folder)# Load promptsprompts load_prompts(prompt_path)# Process each promptfor i, prompt in enumerate(prompts):reference_image_path f{reference_image_folder}/{i}.png if infer_mode i2v else dst_path f{output_folder}/example_{i}.mp4process_prompt(infer_pipeline, prompt, reference_image_path, seed, video_length, resolution, use_noise_scheduler_snr, cond_fps, cond_motion_score, output_fps, dst_path)if __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--config, typestr, defaultconfigs/inference/i2v.yaml, helpPath to the configuration file)args parser.parse_args()main(args)
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