VBench Evaluate
VBench是当前视频生成领域相对全面的评测框架，支持t2v、i2v、long场景任务评测，支持从多种维度评测生成的视频质量，详细可查看： VBench介绍
环境安装
待评测模型按照README完成基础环境安装后，还需完成以下操作：
使用pip install 安装以下包：
vbench==0.1.5
transformers==4.45.0
scenedetect==0.6.5.2
av==13.1.0
ffmpeg
moviepy==1.0.3
dreamsim==0.2.1
cloudpickle==3.1.1
imageio_ffmpeg==0.5.1
portalocker==2.8.2
timm==1.0.8
detectron2安装：
pip install detectron2@git+https://github.com/facebookresearch/detectron2.git
vbench完成安装后，需要从源码复制文件到安装目录，其中"..."需要改为vbench实际安装的目录，具体操作如下：
git clone https://github.com/Vchitect/VBench.git
cd VBench
cp -r vbench2_beta_i2v/third_party .../envs/test/lib/python3.10/site-packages/vbench2_beta_i2v/third_party
cp -r vbench2_beta_long/configs .../envs/test/lib/python3.10/site-packages/vbench2_beta_long/configs
数据集准备
当前支持vbench视频生成评测，支持t2v/long/i2v等场景。需要下载相关数据用于生成视频。
t2v/long场景需要准备prompts、json文件。
t2v json下载路径下载到$VBench_full_info.json
t2v/long配置说明
```
$vbench_prompts
├── augmented_prompts
│ ├── gpt_enhanced_prompts
│ │ ├── prompts_per_category_longer
│ │ ├── prompts_per_dimension_longer
│ │ │ ├── appearance_style_longer.txt
│ │ │ └── ...
│ │ ├── all_category_longer.txt
│ │ └── ...
│ └── hunyuan_all_dimension.txt
├── prompts_per_category
│ └── ...
├── metadata
│ └── ...
├── prompts_per_dimension_chinese
│ └── ...
├── prompts_per_dimension
│ ├── appearance_style.txt
│ └── ...
├── all_dimension.txt
├── all_dimension_cn.txt
└── ...
```
配置说明：
{
"eval_config": {
"dataset": {
"type": "vbench_eval", // 表示t2v/long场景
"basic_param": {
"data_path": "$VBench_full_info.json",
"data_folder": "$vbench_prompts", // $vbench_prompts路径
"return_type": "list",
"data_storage_mode": "standard"
},
"extra_param": {
"augment": false, // 数据增强开关，开启后将使用强化后的prompt
"prompt_file": "all_dimension.txt", // 在dimension配置为空列表时，使用此文件作为视频文件前缀，默认为英文版本全维度
"augmented_prompt_file": "augmented_prompts/gpt_enhanced_prompts/all_dimension_longer.txt" // 如dimension配置为空列表，在augment=true时，使用此文件作为视频生成提示词，否则使用prompt file，默认为GPT强化的英文版本全维度
}
},
"dataloader_param": { // dataloader参数，功能与训练配置相同
"dataloader_mode": "sampler",
"sampler_type": "SequentialSampler",
"shuffle": true,
"drop_last": false, // 关闭drop last保证所有prompt都生成视频
"pin_memory": true,
"group_frame": false,
"group_resolution": false,
"collate_param": {},
"prefetch_factor": 4
},
"evaluation_model": "cogvideox-1.5", // 被评测模型
"evaluation_impl": "vbench_eval", // 使用vbench评测
"eval_type": "t2v", // t2v或者long，以5秒为界
"load_ckpt_from_local": true,
"long_eval_config": "path_to_long_eval_configs", // eval_type 为long时需要配置，配置为vbench安装完成后vbench2_beta_long的路径
"dimensions": [ // 评测维度配置
"subject_consistency",
"background_consistency",
"aesthetic_quality",
"imaging_quality",
"temporal_style",
"overall_consistency",
"human_action",
"temporal_flickering",
"motion_smoothness",
"dynamic_degree",
"appearance_style"
]
}
}
i2v配置说明
i2v json下载路径保存到$vbench2_i2v_full_info.json
图片数据集下载链接，解压后将目录配置如下
```
$vbench_i2v
├── data
│ ├── [crop]($vbench_i2v_crop)
│ │ ├── 1-1
│ │ ├── 7-4
│ │ │ ├── a bald eagle flying over a tree filled forest.jpg
│ │ │ └── ...
│ │ └── ...
│ └── origin
│ └── ...
└── [vbench2_i2v_full_info.json]($vbench2_i2v_full_info.json)
```
配置说明
{
"eval_config": {
"dataset": {
"type": "vbench_i2v", // 数据集合类型，适用于i2v
"basic_param": {
"data_path": "$vbench2_i2v_full_info.json", // i2v_full_info文件路径
"data_folder": "$vbench_i2v", // 配置根路径
"return_type": "list",
"data_storage_mode": "standard"
},
"extra_param": {
"ratio": "16-9" // 图片比例，支持1-1、8-5、7-4、16-9四种比例
}
},
"dataloader_param": {
"dataloader_mode": "sampler",
"sampler_type": "SequentialSampler",
"shuffle": true,
"drop_last": false, // 关闭drop last保证所有prompt都生成视频
"pin_memory": true,
"group_frame": false,
"group_resolution": false,
"collate_param": {},
"prefetch_factor": 4
},
"evaluation_model": "cogvideox-1.5", // 被评测模型
"evaluation_impl": "vbench_eval", // 使用vbench评测
"eval_type": "i2v", // 评测场景i2v
"load_ckpt_from_local": true,
"dimensions": [ // 评测维度配置
"subject_consistency"
],
"image_path": "$vbench_i2v_crop" // 原始图片路径
}
}
参数配置
-
权重及模型文件配置
请参考"推理-配置参数"章节，配置评测脚本（如
eval_cogvideox_i2v_1.5.sh）、评测配置文件（如eval_model_i2v_1.5.json）中的模型和权重路径。 -
评测维度配置 当前vbench评测支持三种评测类型：i2v、t2v、long（>=5s视频评测），需要在
eval_model_i2v_1.5.json/eval_model_t2v_1.5.json文件中配置dimensions的维度，支持配置多个。t2v支持维度：
["subject_consistency", "background_consistency", "aesthetic_quality", "imaging_quality", "object_class", "multiple_objects", "color", "spatial_relationship", "scene", "temporal_style", "overall_consistency", "human_action", "temporal_flickering", "motion_smoothness", "dynamic_degree", "appearance_style"]i2v支持维度：
["subject_consistency", "background_consistency", "aesthetic_quality", "imaging_quality", "temporal_flickering", "motion_smoothness", "dynamic_degree", "i2v_subject", "i2v_background", "camera_motion"]long支持维度：
["subject_consistency", "background_consistency", "aesthetic_quality", "imaging_quality", "object_class", "multiple_objects", "color", "spatial_relationship", "scene", "temporal_style", "overall_consistency", "human_action", "temporal_flickering", "motion_smoothness", "dynamic_degree", "appearance_style"] -
数据集参数配置
启动评测
i2v启动评测：
bash examples/cogvideox/i2v_1.5/eval_cogvideox_i2v_1.5.sh
t2v启动评测：
bash examples/cogvideox/t2v_1.5/eval_cogvideox_t2v_1.5.sh
long启动评测，修改examples/cogvideox/t2v_1.5/eval_model_t2v_1.5.json中的eval_type为long，执行如下命令即可：
bash examples/cogvideox/t2v_1.5/eval_cogvideox_t2v_1.5.sh