TrinArt Characters Stable Diffusion v1
| Property | Value |
|---|---|
| License | CreativeML OpenRAIL-M |
| Training Infrastructure | 8x NVIDIA A100 40GB |
| Model Type | Stable Diffusion v1-based |
| Training Dataset Size | 19.2M images |
What is trinart_characters_19.2m_stable_diffusion_v1?
This is a specialized Stable Diffusion model trained on approximately 19.2M anime and manga style images, with additional fine-tuning on 50,000 curated images. It represents a careful balance between artistic versatility and anatomical accuracy within the constraints of SDv1 architecture.
Implementation Details
The model features a custom KL autoencoder trained separately from the latent diffusion model, following recommendations from the Latent Diffusion paper. This separation has shown improved results in image generation quality. The model includes specific safety filters to exclude NSFW content, making it more suitable for general use.
- Specialized architecture with separate autoencoder training
- Comprehensive dataset filtering for safe content generation
- Service-tested through AI Novelist/TrinArt platform
- Optimized for character-focused image generation
Core Capabilities
- High-quality anime/manga style character generation
- Anatomically stable outputs compared to base SD models
- Support for various artistic styles and composition types
- Enhanced safety features for content generation
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its extensive training on 19.2M images and additional fine-tuning, creating a sweet spot between artistic style versatility and anatomical quality. It also features a custom autoencoder implementation that enhances output quality.
Q: What are the recommended use cases?
This model excels at generating anime/manga-style character illustrations, particularly when using specific style presets like the TrinArt 2022 Artstyle or More Details preset. It's recommended to use appropriate negative prompts to stabilize anatomy and achieve optimal results.
