Instructions to use stepfun-ai/stepvideo-ti2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stepfun-ai/stepvideo-ti2v with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stepfun-ai/stepvideo-ti2v", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -70,7 +70,7 @@ The following table shows the requirements for running Step-Video-TI2V model (ba
|
|
| 70 |
|
| 71 |
## Citation
|
| 72 |
```
|
| 73 |
-
@misc{
|
| 74 |
title={Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model},
|
| 75 |
author={Haoyang Huang, Guoqing Ma, Nan Duan, Xing Chen, Changyi Wan, Ranchen
|
| 76 |
Ming, Tianyu Wang, Bo Wang, Zhiying Lu, Aojie Li, Xianfang Zeng, Xinhao
|
|
|
|
| 70 |
|
| 71 |
## Citation
|
| 72 |
```
|
| 73 |
+
@misc{huang2025step,
|
| 74 |
title={Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model},
|
| 75 |
author={Haoyang Huang, Guoqing Ma, Nan Duan, Xing Chen, Changyi Wan, Ranchen
|
| 76 |
Ming, Tianyu Wang, Bo Wang, Zhiying Lu, Aojie Li, Xianfang Zeng, Xinhao
|