HiDream-O1 LoRA Trained on AI Toolkit – Fast Pixel‑Space Model
Test the HiDream‑O1 LoRA on AI Toolkit and observe its fast training and absence of VAE and Text Encoder.
Test the HiDream‑O1 LoRA on AI Toolkit and document its training speed and behavior.
Summary
HiDream‑O1 LoRA was trained on the AI Toolkit in a first test, with the author noting its remarkable speed and simplicity. The model operates entirely in pixel space, eliminating the need for a VAE or a text encoder, which contributes to its rapid training times. The author describes it as an industry‑changing innovation, highlighting its efficiency and the lack of traditional components. No version number is provided, but the LoRA demonstrates a clean, lightweight architecture. The training was performed on the AI Toolkit platform, and the results were shared via a Twitter link. The post encourages cautious optimism while acknowledging the potential of the new approach.
The absence of VAE and text encoder simplifies the pipeline, making the LoRA easier to integrate into existing workflows. The author emphasizes that the model trains super fast, suggesting significant performance gains over conventional LoRAs. The community is invited to experiment with the model and report on its behavior. The post serves as an early showcase of HiDream‑O1's capabilities within the AI Toolkit ecosystem.
Key changes
- HiDream‑O1 LoRA uses pixel space only
- No VAE component
- No Text Encoder
- Trains very quickly
- Considered industry‑changing by user
- AI Toolkit used for training
- LoRA trained on first test
- No version numbers