Instructions to use bytedance-research/ATI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/ATI 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("bytedance-research/ATI", 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
Add link to the Github repository
#2
by nielsr HF Staff - opened
README.md
CHANGED
|
@@ -3,11 +3,11 @@ base_model:
|
|
| 3 |
- Wan-AI/Wan2.1-I2V-14B-480P
|
| 4 |
language:
|
| 5 |
- en
|
|
|
|
| 6 |
license: apache-2.0
|
| 7 |
pipeline_tag: image-to-video
|
| 8 |
tags:
|
| 9 |
- videogen
|
| 10 |
-
library_name: diffusers
|
| 11 |
---
|
| 12 |
|
| 13 |
# ATI: Any Trajectory Instruction for Controllable Video Generation
|
|
|
|
| 3 |
- Wan-AI/Wan2.1-I2V-14B-480P
|
| 4 |
language:
|
| 5 |
- en
|
| 6 |
+
library_name: diffusers
|
| 7 |
license: apache-2.0
|
| 8 |
pipeline_tag: image-to-video
|
| 9 |
tags:
|
| 10 |
- videogen
|
|
|
|
| 11 |
---
|
| 12 |
|
| 13 |
# ATI: Any Trajectory Instruction for Controllable Video Generation
|