Instructions to use MLbackup/T2V_Video_Loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MLbackup/T2V_Video_Loras with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MLbackup/T2V_Video_Loras", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: creativeml-openrail-m | |
| base_model: | |
| - Wan-AI/Wan2.1-FLF2V-14B-720P | |
| - tencent/HunyuanVideo | |
| pipeline_tag: text-to-video | |
| tags: | |
| - art | |
| library_name: diffusers | |
| Scraped T2V loras from deleted creators |