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
- Draw Things
Expand license + dataset attribution
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README.md
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## License
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Apache 2.0.
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## References
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## License
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The LoRA adapter weights in this repository are released under the Apache License 2.0, matching the license of the base model FLUX.2 Klein 4B.
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### Training data attribution
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The training data is fully synthetic, generated at preparation time from random rectangular room geometries via the [pyroomacoustics](https://github.com/LCAV/pyroomacoustics) Python library (Scheibler, Bezzam, Dokmanić, 2018). Pyroomacoustics is distributed under the MIT License. No external dataset is required to reproduce the training corpus; the dataset-generation script is included in this repository.
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### Base model
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Base model FLUX.2 Klein 4B is distributed by Black Forest Labs under the Apache License 2.0. See https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B for the original model card.
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## References
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