benchmarks/edit/code/FiVE-Bench/models/README.md

FiVE-Bench Video Editing Models

This directory contains two state-of-the-art video editing model implementations designed for the FiVE-Bench evaluation framework:

• Pyramid-Edit: A diffusion-based video editing method using the Pyramid-Flow architecture
• Wan-Edit: A rectified flow-based video editing approach leveraging the Wan2.1-T2V model

Both models support fine-grained video editing tasks including object transformations, style changes, background modifications, and temporal consistency preservation across 41-frame sequences.

Environment Setup

Create and activate the conda environment:

conda create -n five-bench python=3.11.10 -y
conda activate five-bench
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install transformers==4.45.2
pip install -r models/requirements.txt
# Verify flash attention installation
pip install flash-attn==2.7.2.post1 --no-build-isolation

---

Pyramid-Edit

Overview

Pyramid-Edit is a diffusion-based video editing method that leverages the Pyramid-Flow architecture for high-quality, temporally consistent video transformations.

Setup: Model Download

cd models/pyramid-edit
mkdir -p hf
cd hf

# Download [Pyramid-Flow](https://huggingface.co/rain1011/pyramid-flow-miniflux) model checkpoint
git clone https://huggingface.co/rain1011/pyramid-flow-miniflux

Configuration

Before running Pyramid-Edit, update the configuration file models/pyramid-edit/config.yaml:

device: 'cuda'
dtype: 'bf16'
model_name: 'pyramid_flux'  # or 'pyramid_mmdit'
model_path: 'models/pyramid-edit/hf/pyramid-flow-miniflux'
resolution: '384p'  # or '768p'
max_frames: 41

Running Pyramid-Edit

Single Video Editing

Edit a single video with custom prompts. This processes the bear example video, changing it from brown to purple.

# Run single video editing example
bash models/pyramid-edit/scripts/run_single.sh

Running on FiVE Dataset

bash models/pyramid-edit/scripts/run_FiVE.sh

---

Wan-Edit

Overview

Wan-Edit is a rectified flow-based video editing method built upon the Wan2.1-T2V-1.3B model architecture. This approach provides efficient and high-quality video transformations through:

• Rectified flow modeling: Advanced flow-based generative approach for smoother video transitions
• Text-to-video capabilities: Strong text conditioning for precise edit control
• 1.3B parameter efficiency: Optimized model size balancing performance and resource usage
• 832x480 resolution: High-definition output suitable for detailed editing tasks

Setup: Model Download

cd models/wan-edit
mkdir hf
# Download [Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) model checkpoint to `models/wan-edit/hf/` directory
cd hf
git clone https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B

Running Wan-Edit on FiVE Dataset

To run Wan-Edit on the FiVE-Bench dataset:

# Run the complete FiVE evaluation script
bash models/wan-edit/scripts/run_FiVE.sh

This script will:

• Process all editing tasks (edit1 through edit6) in the FiVE-Bench dataset
• Use the Wan2.1-T2V-1.3B model with 832x480 resolution and 41 frames
• Require the model checkpoint in models/wan-edit/hf/wan13/
• Save results to outputs/wan_edit_results/

Manual Execution

You can also run individual editing tasks manually:

sh models/wan-edit/scripts/run_single.sh

Custom Parameters

For advanced usage, you can specify custom parameters:

python models/wan-edit/edit.py \
    --task t2v-1.3B \
    --size 832*480 \
    --frame_num 41 \
    --ckpt_dir models/wan-edit/hf/Wan2.1-T2V-1.3B/ \
    --data_dir data \
    --save_dir outputs \
    --FiVE_dataset_json data/edit_prompt/edit5_FiVE.json

Parameter Options:

• --task: Model variant (t2v-1.3B)
• --size: Output resolution (832*480 recommended)
• --frame_num: Number of frames to generate (41 for FiVE-Bench)
• --ckpt_dir: Path to model checkpoint directory
• --data_dir: Input data directory
• --save_dir: Output directory for edited videos
• --FiVE_dataset_json: Specific editing task file

Note: To specify a particular video, use the following arguments:

--video_dir data/examples \
--video_name blackswan \

---

Model Comparison & Selection

When to Use Pyramid-Edit

• High-quality requirements: Better for applications requiring maximum visual fidelity
• Flexible resolutions: When you need both 384p and 768p output options

When to Use Wan-Edit

• Efficiency focused: Faster inference with 1.3B parameter model
• Flow-based benefits: Smoother temporal transitions and more stable generation
• Text conditioning: Superior text understanding for complex editing instructions
• Resource constraints: Better performance on limited computational resources

Performance Characteristics (Wan-Edit > Pyramid-Edit)

Aspect	Pyramid-Edit	Wan-Edit
Model Size	Larger (varies by variant)	1.3B parameters
Resolution	384p/768p	832x480
Architecture	Rectified flow	Rectified flow
Inference Speed	Slower	Faster
Text Understanding	Good	Excellent
Memory Usage	Higher	Lower

Performance Optimization

1. GPU Memory Optimization

• Use bf16 precision instead of fp32
• Reduce max_frames if memory limited

2. Inference Speed

• Use single GPU with CUDA_VISIBLE_DEVICES=0
• Consider lower resolution for rapid prototyping