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---
base_model:
- Wan-AI/Wan2.2-I2V-A14B
license: apache-2.0
pipeline_tag: image-to-video
---
# FastVideo CausalWan2.2-I2V-A14B-Preview-Diffusers Model
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6532f70333c5982a291ca909/zAB7U0da8_8fP1N0DgUS2.png" width="200"/>
</p>
<div>
<div align="center">
<a href="https://github.com/hao-ai-lab/FastVideo" target="_blank">FastVideo Team</a> 
</div>
<div align="center">
<a href="https://github.com/hao-ai-lab/FastVideo">Github</a> |
<a href="https://hao-ai-lab.github.io/FastVideo">Project Page</a>
</div>
</div>
## Disclaimer
Note that this is a preview model, meaning there are still quality issues. The inference speed is also unoptimized.
## Introduction
We're excited to introduce the **CausalWan2.2 I2V A14B series**—a new line of models.
---
## Model Overview
- 8-step inference is supported.
- Try it out on **FastVideo** — we support a wide range of GPUs from **H100** to **4090**, and also support **Mac** users!
## Inference code
```python
from fastvideo import VideoGenerator, SamplingParam
import json
# from fastvideo.configs.sample import SamplingParam
OUTPUT_PATH = "video_samples_self_forcing_causal_wan2_2_14B_i2v"
def main():
# FastVideo will automatically use the optimal default arguments for the
# model.
# If a local path is provided, FastVideo will make a best effort
# attempt to identify the optimal arguments.
generator = VideoGenerator.from_pretrained(
"FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
# FastVideo will automatically handle distributed setup
num_gpus=1,
use_fsdp_inference=True,
dit_cpu_offload=True, # DiT need to be offloaded for MoE
dit_precision="fp32",
vae_cpu_offload=False,
text_encoder_cpu_offload=True,
dmd_denoising_steps=[1000, 850, 700, 550, 350, 275, 200, 125],
# Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
pin_cpu_memory=True,
# image_encoder_cpu_offload=False,
)
sampling_param = SamplingParam.from_pretrained("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers")
sampling_param.num_frames = 81
sampling_param.width = 832
sampling_param.height = 480
sampling_param.seed = 1000
with open("prompts/mixkit_i2v.jsonl", "r") as f:
prompt_image_pairs = json.load(f)
for prompt_image_pair in prompt_image_pairs:
prompt = prompt_image_pair["prompt"]
image_path = prompt_image_pair["image_path"]
_ = generator.generate_video(prompt, image_path=image_path, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
if __name__ == "__main__":
main()
``` |