Video-to-Video
Diffusers
Safetensors
g-buffer
neural-rendering
real-time
autoregressive
video-diffusion
supertuxkart
Instructions to use AlayaLab/AlayaRenderer-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlayaLab/AlayaRenderer-Flash with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AlayaLab/AlayaRenderer-Flash", 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
Download samples/window-01.npy from AlayaLab/AlayaRenderer-Flash: direct link, hf CLI and curl.
- Browser
- Download file 89.5 MB
-
https://huggingface.co/AlayaLab/AlayaRenderer-Flash/resolve/main/samples/window-01.npy
- Command line
-
hf download hf://AlayaLab/AlayaRenderer-Flash/samples/window-01.npy
-
curl -L -o window-01.npy https://huggingface.co/AlayaLab/AlayaRenderer-Flash/resolve/main/samples/window-01.npy
89.5 MB
- Xet hash:
- fba0c2acb5a1a745fc52e4e3c14b7c183bd082b6a60817b42a3526b56bc450d4
- Size of remote file:
- 89.5 MB
- SHA256:
- 30d2922e868d79aba502c1161249849762e91207f196ebdd8745232fcf04e845
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.