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
UNet
Flow matching (time_shift_type is 'linear') and the LFM2.5 text encoder 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
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
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Model tree for nebulette/aniportrait-lfm
Base model
8BitStudio/Aniimage-2