Stability-AI/StableCascade

Features

• Latent Diffusion Models - Provides a multi-stage architecture that performs iterative denoising within compressed latent spaces for high-fidelity synthesis.
• Cascaded Pipelines - Ships a cascaded pipeline that chains base models with upsamplers for structured resolution progression.
• Cascading Decoders - Uses cascading decoders to progressively increase image resolution through sequential model passes.
• Latent Reconstruction - Encodes high-dimensional images into a compact latent space and decodes them back to original dimensions.
• Text-to-Image Generators - Transforms textual descriptions into high-fidelity images using a multi-stage latent diffusion pipeline.
• Image-to-Image Diffusion Toolkits - Provides a toolkit for image-to-image diffusion tasks such as inpainting and creating image variations.
• Diffusion Model LoRA Fine-Tuning - Supports model optimization through low-rank adaptation to learn new visual concepts.
• Latent Conditioning Mechanisms - Injects textual embeddings into the latent denoising mechanism to guide the image generation process.
• Low-Rank Adaptation - Supports parameter-efficient fine-tuning using low-rank adaptation matrices to learn new concepts.
• Variational Autoencoders - Utilizes variational autoencoders to map high-dimensional images into a continuous latent distribution.
• Text-to-Image Model Training - Implements training processes to associate specific text prompts with high-fidelity visual patterns using custom datasets.
• Diffusion Model Trainings From Scratch - Provides specialized scripts to build a cascade of diffusion models and autoencoders from the ground up.
• Resolution Upscalers - Employs super-resolution and decoding techniques to increase the quality and dimensions of generated imagery.
• Diffusion Model Training - Includes scripts for training diffusion models and autoencoders from scratch.
• Image-to-Image Translation - Maps existing images to new versions using text guidance and a diffusion-based denoising process.
• Image-to-Image Denoising - Implements image-to-image transformation by adding and then removing noise to refine existing visual content.
• Image Editing - Provides tools for modifying visual content through generative AI instructions including inpainting and outpainting.
• Image Variation and Mixing - Creates new versions of existing images by utilizing image embeddings without requiring text prompts.
• Diffusion Model Adaptations - Includes scripts for injecting low-rank adaptation matrices into diffusion models for task-specific changes.
• Structural Image Generation - Guides generation through structural constraints such as inpainting, outpainting, and super-resolution.
• Vision Model Fine-Tuning - Enables adapting pretrained vision models to new datasets using specialized LoRA fine-tuning scripts.

Star history