## Test

We employed the VBench benchmark to generate prompts for the text-to-video generation scenario. The complete set of prompts used in this evaluation is available in `VBench_full.json`

## Usage

All the scripts code can be found in folder `scripts/`.

Note that before using **Speculative Diffusion and Speculative Verification**, please download the original diffusion models (including the target model and the draft model) and install the corresponding environment, following the instructions in their original repositories.

For text-to-image tasks,  from `https://github.com/mit-han-lab/nunchaku.git`. Because the SVDquant technique is under development. You should switch to tags v0.1.4 version of nunchaku if you meet some problem. Please copy the files under the flux directory to the examples folder in your nunchaku repository. Additionally, replace the `transformer_flux.py` file in your transformer library and the `attention_processor.py` and `scheduling_flow_match_euler_discrete.py` files in your diffusers library. We are currently working on a more convenient usage method to avoid multiple environment copies and preparing the full code.

For text-to-video tasks,  from `https://github.com/Wan-Video/Wan2.1.git`.  The `Wan2.1/wan` directory contains three distinct text-to-video methods, corresponding to the improved draft model baseline, our method, and the original Wan approach. To utilize a particular method, modify the respective file subscript to `text2video.py` and replace the `generate.py` and `fm_solvers_unipc.py` file to your repository.