Instructions to use phanerozoic/echo-plantain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use phanerozoic/echo-plantain with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # 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/echo-plantain") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
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Download TODO.md from phanerozoic/echo-plantain: direct link, hf CLI and curl.
- Browser
- Download file 633 Bytes
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https://huggingface.co/phanerozoic/echo-plantain/resolve/main/TODO.md
- Command line
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hf download hf://phanerozoic/echo-plantain/TODO.md
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curl -L -o TODO.md https://huggingface.co/phanerozoic/echo-plantain/resolve/main/TODO.md
633 Bytes
TODO
Planned work for this repository.
- Train the adapter on the prepared synthetic-room corpus.
- Track reconstruction quality at each saved checkpoint via a held-out validation set of unseen rooms.
- Report the final reverb-quality benchmark on a held-out test split (spectrogram distance and reverberation-time error after decoding).
- Publish the inverse-bijection decoder and the schematic renderer used at training time.
- Release weights, decoder, renderer, and a representative set of demonstration (room schematic, predicted impulse response, dry-signal-with-recovered-reverb) bundles accompanying the model card.