Instructions to use lightx2v/Autoencoders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Autoencoders with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Autoencoders", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Autoencoders with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Terrible Quality
#13 opened 5 months ago
by
qpqpqpqpqpqp
Differences among lighttae, lightvae, and tae VAEs
#12 opened 6 months ago
by
makisekurisu-jp
Will these VAEs be added to diffusers library?
#11 opened 6 months ago
by
Zarxrax
Is a decoder coming to WAN 2.2 14B?
#10 opened 6 months ago
by deleted
Wan2.1_VAE.safetensors is it not 2-3 times faster than the native vae and kijai
#9 opened 6 months ago
by
hydraofm0
Remove the LoRA Tag
#8 opened 6 months ago
by
qpqpqpqpqpqp
Can this model be used to compress videos?
1
#7 opened 7 months ago
by
lanesun
asking for model used
#5 opened 7 months ago
by
eatandpeace
Your sample videos have loud autoplaying audio
1
#4 opened 7 months ago
by
noisefloordev