Instructions to use neph1/ancient_rome_wan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neph1/ancient_rome_wan 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/Wan2.2-T2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("neph1/ancient_rome_wan") prompt = "-" output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
metadata
tags:
- lora
- diffusers
- template:diffusion-lora
- text-to-video
- t2v
widget:
- output:
url: >-
https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/9a1I5_Hn98sNIHSWdoR-6.mp4
text: '-'
- output:
url: >-
https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/2iG_5iNLZnvw8G-ws8ECB.mp4
text: '-'
- output:
url: >-
https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/2iG_5iNLZnvw8G-ws8ECB.mp4
text: '-'
base_model:
- Wan-AI/Wan2.2-T2V-A14B
instance_prompt: null
Ancient Rome Lora Wan2.2 T2V 14B
Mirror of: https://civitai.com/models/1339539/ancient-rome-lora
Low noise model is trained for 30 epochs.
High noise model is trained for 20 epochs.