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Model Details
Introduction
Key Takeaways: Today we are releasing Stable Cascade in research preview, a new text to image model building upon the Würstchen architecture. This model is being released under a non-commercial license that permits non-commercial use only. Stable Cascade is exceptionally easy to train and finetune on consumer hardware thanks to its three-stage approach. In addition to providing checkpoints and inference scripts, we are releasing scripts for finetuning, ControlNet, and LoRA training to enable users further to experiment with this new architecture that can be found on the Stability GitHub page.
Training
Training Code AccessibilityMIT licence for training and inference code https://github.com/Stability-AI/StableCascade non commercial licemse for weights https://github.com/Stability-AI/StableCascade/blob/master/models/readme.md
Parameters
Parameters5120000000
Notes: "we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to its small size." 3.6B + 1.5B + 20M = 5.12B
