A comprehensive cheatsheet covering open-source text-to-image generation models, inference libraries, datasets, use cases, deployment strategies, training resources, evaluation methods, and ethical considerations for developers and organizations.
FLUX.1-dev: Introduced in 2024, FLUX.1-dev is a powerful AI image generation model utilizing an advanced architecture called a latent diffusion model.
Stable Diffusion v1.5: An iteration of the latent text-to-image diffusion model capable of generating photo-realistic images from textual descriptions.
Stable Diffusion v2.1: An enhanced version of the model, offering improved image quality and resolution capabilities.
Stable Diffusion XL Base 1.0: A larger model with 3.5 billion parameters, designed for high-resolution image synthesis with greater detail and fidelity.
Stable Diffusion 3.5 Large: An 8-billion-parameter model delivering high-quality, prompt-adherent images up to 1 megapixel, customizable for professional use on consumer hardware.
Diffusers: A library by Hugging Face that provides pre-trained diffusion models for text-to-image generation, facilitating easy integration and experimentation.
LitServe: An open-source easy-to-use, flexible serving engine designed to deploy vision models at scale.
InvokeAI: An open-source AI image generation toolkit that provides a user-friendly interface and supports various models, enabling efficient image creation and customization.
ComfyUI: A powerful and modular open-source GUI for Stable Diffusion, offering a node-based interface for advanced users to experiment with complex workflows.
LAION-5B: A large-scale dataset containing billions of image-text pairs, widely used for training text-to-image models.
CommonCatalog CC-BY: A dataset comprising a diverse collection of images and associated metadata, useful for training image generation models.
DiffusionDB: A large dataset of images generated by diffusion models, along with their prompts, aiding in understanding and improving text-to-image generation.
On-Premises Deployment: Running models on local servers for full control and data privacy.
Cloud Services: Utilizing cloud providers like AWS, Azure, or Google Cloud for scalable deployment.
Serverless GPU Platforms: Serverless GPU platforms like Inferless provide on-demand, scalable GPU resources for machine learning workloads, eliminating the need for infrastructure management and offering cost efficiency.
Edge Deployment: Deploying models on edge devices for low-latency applications.
Containerization: Using Docker or Kubernetes to manage and scale deployments efficiently.
Stable Diffusion WebUI: An open-source web-based user interface for Stable Diffusion, providing extensive features and customization options for image generation.
Civitai: A platform for sharing and discovering models, presets, and other resources related to AI image generation, fostering community collaboration.
ComfyUI: An open-source, node-based graphical interface that enables users to generate images, videos, and audio using generative AI models like Stable Diffusion, offering a modular and customizable workflow for creative applications.
Intellectual Property Rights: AI models may use copyrighted material without permission, risking infringement; it’s essential to respect creators’ rights.
Bias and Representation: AI can perpetuate training data biases, leading to unfair outputs; developers should detect and mitigate these biases.
Transparency and Accountability: Clearly disclose when images are AI-generated to maintain trust and authenticity.
Privacy Concerns: Obtain permissions and anonymize data if you are using personal data in training which can violate privacy.