Stable Cascade

Stable Cascade: An Efficient Architecture for Text-to-Image Diffusion Models

Stable Cascade is an innovative text-to-image model developed by Stability AI, leveraging the Würstchen architecture to achieve high efficiency and impressive visual results. This open-source codebase provides training and inference scripts, along with various models for diverse applications.

What is Stable Cascade?

Stable Cascade distinguishes itself through its highly compressed latent space, enabling faster inference and cheaper training compared to models like Stable Diffusion. By employing a compression factor of 42, Stable Cascade encodes a 1024x1024 image into a compact 24x24 representation while maintaining crisp reconstructions. This efficiency makes it well-suited for scenarios where computational resources are limited.

How Does Stable Cascade Work?

Stable Cascade comprises three key models: Stage A, Stage B, and Stage C. Stages A and B function as autoencoders, compressing images to a smaller latent space. Stage C, a diffusion model, generates 24x24 latent images from a given text prompt. This cascaded approach allows for efficient and high-quality image generation.

• Stage A: VAE (Variational Autoencoder) for initial compression.
• Stage B: Diffusion model for further compression.
• Stage C: Text-conditional diffusion model for generating latent images.

Key Features and Benefits

• Efficiency: Smaller latent space leads to faster inference and reduced training costs.
• High Compression: Achieves a compression factor of 42, encoding 1024x1024 images to 24x24.
• Extensibility: Supports finetuning, LoRA, ControlNet, and IP-Adapter.
• Impressive Results: Delivers excellent prompt alignment and aesthetic quality.

Model Overview

The release includes multiple checkpoints for each stage:

• Stage C: 1 billion and 3.6 billion parameter versions (3.6 billion recommended).
• Stage B: 700 million and 1.5 billion parameter versions (1.5 billion recommended for finer details).
• Stage A: Fixed 20 million parameter version.

Getting Started with Stable Cascade

Inference:

Use the provided notebooks in the inference section for various use cases:

• Text-to-Image: Basic functionality for text-to-image generation, image variation, and image-to-image tasks.
• ControlNet: Integration with ControlNets for advanced control over image generation (Inpainting, Face Identity, Canny, Super Resolution).
• LoRA: Implementation for training and using LoRAs to finetune Stage C and add new tokens.
• Image Reconstruction: Utilize Stage A & B as (Diffusion) Autoencoders, benefiting from a much higher compression, allowing you to train and run models faster.

Training:

Code and explanations for training Stable Cascade from scratch, finetuning, and training ControlNets and LoRAs are available in the training folder.

Use Cases

• Text-to-Image Generation: Create images from textual descriptions.
• Image Variation: Generate variations of existing images.
• Image-to-Image Translation: Modify images based on text prompts.
• ControlNet Integration: Control image generation using various ControlNets.
• Customization: Finetune the model with LoRAs and custom datasets.
• Efficient AI Research: Use the highly compressed latent space to train your own models faster.

Who is Stable Cascade For?

Stable Cascade is suitable for:

• AI researchers seeking efficient text-to-image models.
• Developers building applications that require fast image generation.
• Artists and designers exploring AI-assisted creativity.
• Anyone interested in the latest advancements in latent diffusion models.

Why Choose Stable Cascade?

• Efficiency: Faster inference and cheaper training due to the highly compressed latent space.
• Extensibility: Supports various extensions and customization options.
• State-of-the-Art Performance: Delivers excellent visual quality and prompt alignment.
• Open Source: Freely available and customizable codebase.

Example Use Cases with Images

• Text-to-Image: Generate a cinematic photo of an anthropomorphic penguin in a cafe reading a book.
• Image Variation: Create variations of a given image without a prompt.
• Image-to-Image: Noise an image and regenerate it based on a text prompt.

Technical Details

Stable Cascade achieves a spatial compression factor of 1024 / 24 = 42.67, enabling efficient encoding and decoding of images with minimal loss of detail.

Community and Contributions

The codebase is under active development, and contributions are welcome. Share your ideas, feedback, and updates to help improve Stable Cascade.

License

The code is licensed under the MIT License, while the model weights are under the STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE.

Get Started Today

Explore the official Stable Cascade codebase and unleash your creativity with efficient text-to-image generation!