Instructions to use Glanty/Capybara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Glanty/Capybara with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Glanty/Capybara", 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
how to run Image to video ?
#2
by TahirC - opened
as mentioned in readme
We show in-context generation and in-context editing results , including subject-conditioned generation (S2V/S2I), conditional generation(C2V), image-to-video(I2V), reference-driven editing (II2I/IV2V).
Right now this is v0.1, and it supports T2I, T2V, TI2I, and TV2V only. The other features mentioned in the README (like S2V, I2V, IV2V, II2I, etc.) are coming in the next version.
Weβre working on it and will update soon β stay tuned! π
as mentioned in readme
We show in-context generation and in-context editing results , including subject-conditioned generation (S2V/S2I), conditional generation(C2V), image-to-video(I2V), reference-driven editing (II2I/IV2V).
Glanty changed discussion status to closed