Sygil-Diffusion
| Property | Value |
|---|---|
| License | OpenRAIL++ |
| Languages Supported | English, Japanese, Spanish, Chinese |
| Training Duration | 857 hours |
| Training Steps | 2,370,200 |
What is Sygil-Diffusion?
Sygil-Diffusion is an advanced fine-tuned version of Stable Diffusion, specifically trained on the Imaginary Network Expanded Dataset. Its standout feature is the implementation of multiple namespaces for precise control over image generation, effectively addressing common context confusion issues in AI image generation.
Implementation Details
The model utilizes the StableDiffusionPipeline architecture with DPMSolverMultistepScheduler for optimal performance. Trained using AdamW optimizer with carefully tuned parameters (learning rate: 1e-7, batch size: 1) and implements gradient checkpointing for efficient resource usage.
- Trained on a single NVIDIA RTX 3050 8GB GPU
- Implements cosine learning rate scheduling with restarts
- Uses gradient accumulation (400 steps)
- Supports 512px resolution output
Core Capabilities
- Multilingual prompt understanding (EN, JA, ES, ZH)
- Namespace-controlled generation for precise outputs
- Superior performance in portraits, architecture, landscapes
- Memory-efficient with xformers compatibility
- Multiple checkpoint versions available for different use cases
Frequently Asked Questions
Q: What makes this model unique?
The model's namespace system allows for precise control over generation, preventing common mix-ups like confusing "seal" (animal) with "Seal" (singer). This, combined with multilingual support and extensive training on diverse content, makes it particularly versatile.
Q: What are the recommended use cases?
The model excels at generating portraits, architecture, reflections, fantasy scenes, concept art, anime, and landscapes. It's particularly useful for projects requiring precise control over generated elements through namespace tags.
