Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Stable Diffusion

Latent diffusion models (LDMs) are a type of image generation technique that work by iteratively "de-noising" data in a latent representation space, and then decoding the representation into a full image. This is in contrast to other popular image synthesis methods such as generative adversarial networks (GANs) and the auto-regressive technique used by DALL-E. The Stable Diffusion model is created by a collaboration between engineers and researchers from CompVis, Stability AI, and LAION and released under a Creative ML OpenRAIL-M license, wich means that it can be used for commercial and non-commercial purposes.

The release of this file is the culmination of many hours of collective effort to compress the visual information of humanity into a few gigabytes. Furthermore, the model also supports image-to-image style transfer, as well as upscaling and generating an images from a simple sketch. Included is also an AI-based Safty Classifier, which understands concepts and other factors in generations to remove outputs that may not be desired by the model user.

General
Relese dateAugust 22, 2022
Research Paperhttps://ommer-lab.com/research/latent-diffusion-models/
TypeDeep learning text to image model

Stable diffusion Tutorials

Knowledge Base

Find out how it is working!

  • Research Paper The Stable Diffusion paper describes the model and its training process in detail.
  • Stable Diffusion Demo You can play around with Stable Diffusion on Hugging Face
  • GitHub Repository Visit the Stable Diffusion v2 repository on GitHub
  • dreamstudio Online stable diffusion interface with a lot of optional configurations

Models

There are plenty of Stable Diffusion models, which are taiolred to deliver various art styles, animation styles and more. We encourage you to experiment with many of them and choose the one which you like the most. Here are some of the finest ones:

Boilerplates

Get started with technology!

Edit on GitHub

Stability AI Stable Diffusion AI technology Hackathon projects

Discover innovative solutions crafted with Stability AI Stable Diffusion AI technology, developed by our community members during our engaging hackathons.

x402 Bazaar Agent

x402 Bazaar Agent

Today's AI agents hit a wall the moment they need data behind a paywall. The x402 Bazaar Agent tears that wall down. The agent takes a plain-English question, searches a catalog of 42 premium endpoints across 17 domains using Gemini semantic embeddings, selects the cheapest APIs that can answer, pays for each call via x402 micropayments on Base Sepolia, and composes a cited final answer — no human in the loop. DISCOVER — Gemini text-embedding-005 indexes the catalog at startup. Queries rank by cosine similarity; entries below a relevance floor return zero results, giving the agent a clean stop signal rather than hallucinated answers. PAY — The x402 HTTP client intercepts the 402 response, signs a USDC micropayment with an EVM wallet, and retries. The LLM never sees the payment handshake. VERIFY — After every paid call, an independent Gemini Flash model judges whether the response served the stated purpose. Off-topic data is flagged before the reasoning model builds on it. COMPOSE — Gemini 2.5 Pro weaves results across endpoints and domains into a cited answer. Demo: "Will container ALPHA-99 arrive at Genoa on time?" triggers three paid calls — container ETA, port congestion, marine weather — producing a synthesized delay forecast. Guardrails prevent runaway behavior: hard budget cap, empty-search streak, no-progress streak, duplicate-call detection, and an iteration backstop. The catalog mirrors the real Coinbase Bazaar's discovery schema exactly — switching to the live Bazaar is one function change. All 42 endpoints have mock fallbacks so the demo works with zero mandatory signups. The browser UI streams every agent action live.

Imagr - AI-powered prompt

Imagr - AI-powered prompt

Imagr is a prompt intelligence layer for AI image generation. Most users write short, vague prompts and hope for the best. Imagr fixes that by acting as a creative compiler between the user and the model. It breaks a rough idea into a structured Blueprint covering subject, scene, mood, lighting, composition, and style. It then assigns emphasis weights to each token based on visual importance, ensuring the diffusion model knows what matters most. The system generates reactive negative prompts, scores prompt quality, and detects contradictions or vague language through a built-in linter. What makes Imagr unique is the Model Router. The same creative intent gets adapted into different prompt dialects — parenthetical weights for Stable Diffusion, natural language emphasis for Flux, and descriptive prose for GPT Image. Users can apply style presets like Aggressive or Conservative to control the weighting strategy, lock specific tokens at fixed weights, and compare multiple prompt variants in the Prompt Arena. No open-source tool currently handles automated weight assignment for image prompts. Imagr fills that gap, making image generation faster, clearer, and more predictable for beginners and professionals alike.

NourAI : Your AI Recovery Companion

NourAI : Your AI Recovery Companion

Eating disorders affect over 70 million people globally, yet most digital recovery tools are either clinically sterile or dangerously naive about the psychological complexity of recovery. Nourish bridges that gap. Nourish is a multi-agent AI system that meets you exactly where you are emotionally — every single morning. It reads your mood, then delivers a personalized Daily Card: a warm hype line written just for you, a mood-matched meal suggestion that respects your safe and trigger food boundaries, a curated color palette and real museum artwork chosen to help you feel grounded and seen, and a heartfelt progress note from your AI recovery pal, Nour. What makes Nourish genuinely different is the combination of art therapy and eating disorder recovery in one system. Research consistently shows that engagement with art and aesthetics supports emotional regulation, identity formation, and body neutrality — all critical pillars of ED recovery. Nourish gives patients something beautiful to look forward to every morning — not a symptom tracker, not a calorie log, but a moment of color, warmth, and encouragement that says: you are more than your disorder. The system never mentions calories, weights, or body appearance. Food is framed around taste, warmth, color, and curiosity. Nour — the AI companion — validates your emotional struggles without judgment, celebrates every small act of courage, and stays with you across a 30, 60, or 90 day recovery arc. Built on AMD GPU using Qwen 2.5-7B and FLUX.1-schnell, Nourish runs entirely on open-source models — making it accessible, private, and scalable.

CLOAK-AI

CLOAK-AI

CLOAK-AI is a high-performance privacy-preserving engine designed to bridge the gap between enterprise data utility and strict PII/PCI compliance. Built specifically for the AMD Hackathon, the project addresses the critical risk of sensitive data leakage in Generative AI workflows. By leveraging the massive 192GB VRAM of the AMD Instinct™ MI300X and optimizing inference via ROCm™ 6.1, CLOAK-AI achieves an industry-leading scrubbing latency of just 0.08 seconds. The system utilizes a multi-layer neural architecture: a Llama-3.3-70B model serves as the intelligent reasoning core to identify complex data clusters, while a specialized NLP masking engine handles high-speed scrubbing. The infrastructure is deployed on the AMD Developer Cloud, ensuring that data never leaves the secure environment. Key features include real-time identification of identity documents (Passport/CNIC), financial credentials (IBAN/Credit Cards), and cybersecurity tokens. CLOAK-AI empowers organizations to adopt LLMs safely without compromising user trust or regulatory standards (GDPR/HIPAA).