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
metadata
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