Instructions to use 8BitStudio/Aniimage-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 8BitStudio/Aniimage-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("8BitStudio/Aniimage-1", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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pipeline_tag: text-to-image
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language:
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## What's Next
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**Aniimage-
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Aniimage-2.0 is currently training and will be done soon. 2.0 has many features including quality filters for dataset images, 512x512 native image training, and architecture changes.
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Due to Aniimage-2.0 having a better VAE and being at 512x512 natively, pupils and hands should look significantly better.
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## License
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pipeline_tag: text-to-image
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new_version: 8BitStudio/Aniimage-2
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## What's Next
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**[Aniimage-2](https://huggingface.co/8BitStudio/Aniimage-2) is complete!**
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It features a native resolution of 512×512 and was trained on 1.2 million unique images, compared with the 830,000 images used to train Aniimage-1.
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Aniimage-2 also switches from Aniimage-1’s epsilon-prediction objective to v-prediction and uses a zero-terminal signal-to-noise ratio.
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It also uses uniform timestep sampling, while the upcoming Aniimage-3 will adopt logit-normal noise-level sampling as part of its flow-matching objective.
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## License
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