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
- Draw Things
- DiffusionBee
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Aniimage-1 is the first latent diffusion model developed by 8BitStudio.
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The model is a 256x256 anime image generation model trained from scratch using a UNet + VAE + CLIP architecture.
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Aniimage-1 has been trained on 830,001 anime images from [Danbooru](https://danbooru.donmai.us/). It is not based off of any models, the unet is trained from scratch.
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## Model Details
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Aniimage-1 is the first latent diffusion model developed by 8BitStudio.
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The model is a 256x256 anime image generation model trained from scratch using a UNet + VAE + CLIP architecture.
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Aniimage-1 has been trained on 830,001 anime images from [Danbooru](https://danbooru.donmai.us/). It is not based off of any existing models, the unet is trained from scratch.
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## Model Details
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