Instructions to use nebulette/aniportrait-lfm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nebulette/aniportrait-lfm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nebulette/aniportrait-lfm", 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
| license: apache-2.0 | |
| base_model: | |
| - 8BitStudio/Aniimage-2 | |
| tags: | |
| - lfm | |
| - portrait | |
| UNet | |
| Flow matching (time_shift_type is 'linear') and the [LFM2.5 text encoder](https://huggingface.co/nebulette/clip-l-sized-lfm-230m) on the top of Aniimage. | |
| Due to the lack of training data, the only prompt it understands is anime portraits. | |
| What is more interesting is that the model did not forget all the CLIP embeddings with | |
| <br> the new v-prediction, despite it has been trained only on LFM embeddings. See the image below: | |
|  | |
| Source data: | |
| - anime_style_portrait | |
| - gelbooru (landscape) | |
| - portraits_512 | |
| - wikiart_face | |