Text-to-Video
Diffusers
WanPipeline
WanPipeline-diffusers
image-to-video
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
standard
Instructions to use bghira/wan2.1-1.3b-anyflow-wip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bghira/wan2.1-1.3b-anyflow-wip with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("bghira/wan2.1-1.3b-anyflow-wip") prompt = "A vibrant green Mustang GT parked in an empty parking lot. The camera slowly pans around the car, showing its sleek design, black hood and black rims in a clean promotional video." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Not-For-All-Audiences
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