Aniimage-2

Aniimage-2 is the second public model in the Aniimage series. The model is a 512x512 anime image generation model trained from scratch using a UNet + VAE + CLIP architecture with 441.9 million parameters. Aniimage-2 has been trained on 1.2M anime images from Danbooru and other sources. It is not based on any existing models, the UNet is trained from scratch.

Model Details

Requirements

• GPU: ~6 GB VRAM minimum (recommend 8+ GB)
• CPU: ~7 GB RAM. Image generation is extremely slow on CPU. (recommend 10+ GB)

Prompting

Aniimage uses plain text captions as well as Danbooru tags.

An example of acceptable captions:

"A smiling anime girl with red hair and a school uniform"

"1girl, solo, smile, red_hair, school_uniform, anime_coloring"

Long prompts and enhanced prompts usually result in worse overall results. Try to keep your prompts short.

Although you can use Danbooru tags, you will get the best results with plain text captions

Adding a negative prompt is required for a good output. The one used during inference in the preview image is:

"low quality, ugly, blurry, distorted, deformed, bad anatomy, bad proportions, extra limbs, missing limbs, watermark, text, signature, washed out, flat colors, manga panel, disfigured, poorly drawn, jpeg artifacts, cropped, out of frame"

For the best quality, use DPM++ SDE at 50 steps.

Run

After downloading, install the dependencies.

pip install torch torchvision diffusers transformers safetensors pillow huggingface_hub
python generate_images.py

Capabilities

Aniimage-2 has significantly higher capabilities than Aniimage-1. Especially when it comes to backgrounds.

Limitations

Aniimage-2 is bad at generating hands. It may generate hands but they often melt into surfaces if they are similar colors and usually have more or fewer than five fingers. The model generates male characters less reliably than female characters, it may take many images to find a good image like above.

License

Apache 2.0