Instructions to use phanerozoic/sonic-plantain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use phanerozoic/sonic-plantain with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-base-4B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("phanerozoic/sonic-plantain") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| # TODO | |
| Planned work for this repository. | |
| 1. Train the adapter on the prepared LibriSpeech corpus. | |
| 2. Track reconstruction quality at each saved checkpoint via a held-out validation set, retaining only checkpoints that improve. | |
| 3. Report the final audio-reconstruction benchmark on a held-out test split (PESQ, STOI). | |
| 4. Publish the inverse-bijection decoder for recovering audio from generated spectrograms. | |
| 5. Release weights, decoder, and a representative set of demonstration audio samples accompanying the model card. | |