Instructions to use plankton-rpl/plankton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use plankton-rpl/plankton with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("plankton-rpl/plankton", 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
Model Card for Model ID
This is my first test to train a diffusion model from scratch. It is composed of a simple UNet that denoises the noisy images. This is trained in a subset of the WHOI-PLankton dataset (only a part of the 2006 images). It was trained in a single Nvidia T4, ~6 minutes per epoch.
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