TinyBreaker is a hybrid model that combines the PixArt model for base image generation with Photon model (or any SD1 model) for image refinement. The idea is to leverage both models' strengths in these tasks, enabling them to operate efficiently on mid and low-end hardware due to their minimal parameter count. Moreover, by sequentially executing both models, you can offload them to system RAM reducing the VRAM usage. Additionally, TinyBreaker employs Tiny Autoencoders for latent space conversion, optimizing performance and efficiency.
TinyBreaker is the natural evolution of my two previous developments:
- Photon Model: A fine-tuning of SD1.5 aimed at generating photorealistic and visually appealing images effortlessly.
- The Abominable Workflows: A set of workflows for ComfyUI that emulated, through a spaghetti nightmare, what TinyBreaker currently achieves.
Efficient Parameter Use: TinyBreaker is notable for its low parameter count, featuring just 0.6 billion parameters in the base model. This efficiency means that high-quality image generation requires significantly fewer computational resources compared to heavier models.
Quick Performance: TinyBreaker currently generates images of size 1536×1024 in approximately 10 to 15 seconds using a NVIDIA RTX 3080 GPU. Initially, my goal was always to achieve image generation in under 10 seconds, and I continue striving towards this target. By exploring new optimizations, there is potential to meet this objective while maintaining high quality.
High Prompt Adherence: Thanks to the PixArt model integration, TinyBreaker achieves impressive adherence to prompts despite its minimal parameter count. This ensures that the generated images closely align with user instructions and expectations.
Text Generation Challenges: Currently, TinyBreaker faces significant challenges in producing legible text within images. Given that its underlying PixArt model was not specifically trained on tasks involving detailed text generation, achieving high-quality lettering is problematic. As a result, enhancing this capability may be difficult or nearly impossible without substantial retraining beyond simple fine-tuning.
Hybrid Architecture: TinyBreaker's design leverages the PixArt model for creating a strong base image and uses either a Photon or SD1 model to refine these images. By combining their strengths, the model achieve high-quality results while minimizing computational demands.
Optimized Latent Space Handling: The use of Tiny Autoencoders for latent space conversion further boosts TinyBreaker's performance and efficiency. These autoencoders streamline the process of converting input data into meaningful images, ensuring quality while minimizing resource usage.
My focus is on enhancing speed without compromising image quality, and I'm working hard to make TinyBreaker more accessible, especially for those with mid-range or lower-end hardware.
Thank you for your continued support as we strive to improve. Updates will be shared soon, looking forward to sharing what's new!