DALL-E 2

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OpenAI DALL-E 2

DALL-E 2 is a new version of DALL-E, a generative language model that creates corresponding original images from sentences. DALL-E 2 has 3.5B parameters, making it large but not nearly as large as GPT-3. Interestingly, it is smaller than its predecessor, which had 12B parameters. Despite its size, DALL-E 2 generates images 4 times better in resolution than DALL-E. Additionally, human judges prefer DALL-E 2 over DALL-E 70% of the time in caption matching and photorealism.

General
Relese date	April 6, 2022
Author	OpenAI
Repository	-
Type	Autoregressive, Transformer, Image generation model

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OpenAI DALL-E 2 Tutorials

👉 Discover more DALL-E-2 Tutorials on lablab.ai

Libraries

A curated list of libraries and technologies to help you build great projects with DALL-E 2.

Blog Open AI's blog post about DALL-E 2 API
Documentation DALL-E 2 Documentation
API DALL-E 2 API Documentation

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OpenAI DALL-E-2 AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI DALL-E-2 AI technology, developed by our community members during our engaging hackathons.

MedSync AI Collaborative Crisis Intelligence

TASK 3 — LONG DESCRIPTION Problem Every year, U.S. hospitals face over 150 million emergency department visits and thousands of mass casualty events. When a Level 3 Critical surge strikes — a multi-vehicle accident, an industrial disaster, a pandemic spike — the difference between life and death is measured in minutes. Yet the coordination systems hospitals depend on were designed for a pre-digital era. Incident commanders juggle phone calls, whiteboards, and pagers. Capacity managers refresh spreadsheets. Staffing coordinators text on-call nurses. Resource managers fax mutual aid requests. Compliance officers review binders of regulatory requirements. The result is catastrophic coordination failure: 34% of preventable hospital deaths are attributed to communication breakdowns during emergencies (Joint Commission, 2023) Average surge response time is 47 minutes — 37 minutes longer than best-practice targets $2.1M average cost per mass casualty event in operational inefficiency alone 72% of hospitals report that their Incident Command System breaks down under real pressure EMTALA violations during surges carry $119,942 fines per incident and potential loss of Medicare funding The fundamental problem is not a lack of data — it is a lack of coordinated decision-making under pressure. No single person can simultaneously optimize bed allocation, nurse staffing ratios, ventilator supply chains, and regulatory compliance within a 10-minute window. Solution MedSync AI is a Collaborative Multi-Agent System that brings autonomous, coordinated, AI-driven decision intelligence to hospital emergency response. Unlike a chatbot or a dashboard, MedSync AI deploys 5 specialized AI agents that work together — reading each other's outputs, challenging each other's recommendations, and negotiating until the plan is compliant, optimal, and ready for human approval.

GHOST BOARD — Autonomous AI Command Center

GHOST BOARD is a futuristic multi-agent AI operating system designed to simulate how autonomous AI executives coordinate during critical enterprise crises. Unlike traditional AI assistants that work independently, GHOST BOARD orchestrates multiple specialized AI executives including CEO AI, CTO AI, Security AI, Operations AI, HR AI, and Marketing AI. Each AI executive collaborates in real time to analyze incidents, escalate risks, coordinate workflows, and generate enterprise recovery strategies autonomously. The platform simulates realistic enterprise-scale operational failures such as cyberattacks, infrastructure outages, payment system failures, and security breaches. During each simulation, AI executives communicate through live operational intelligence feeds, generate department-specific responses, evaluate business impact, and coordinate recovery actions across the organization. The system combines a cinematic cyberpunk-inspired command center with real workflow orchestration powered by n8n and AI reasoning pipelines. Executive outputs are transformed into operational dashboards, escalation timelines, recovery systems, and intelligence reports that mimic real enterprise coordination environments. To improve realism, the platform includes a fictional enterprise ecosystem called NovaPay Technologies, featuring connected infrastructure systems, operational telemetry, AI executive monitoring, and live crisis-response workflows. Key capabilities include: - Multi-agent AI executive orchestration - Real-time crisis simulation engine - Autonomous workflow coordination - Live AI communication pipelines - Enterprise operational dashboards - Security operations visualization - Predictive operational analytics - AI-generated recovery reports - Infrastructure monitoring systems - Webhook-based automation using n8n

AluminatiAI

AluminatiAI GreenTune is an autonomous energy intelligence platform designed to optimize enterprise AI training workloads. As large language model fine-tuning becomes more expensive and energy-intensive, GreenTune helps organizations reduce infrastructure cost, power consumption, and carbon emissions through AI-driven optimization and real-time telemetry. The system uses a multi-agent swarm powered by Gemini, including specialized agents for orchestration, configuration optimization, policy enforcement, and energy analysis. These agents collaborate using real function calling to test and evaluate training configurations for efficiency before workloads are launched. GreenTune also includes “Lobster Trap,” an energy governance framework that enforces hard limits on CO2 emissions, energy usage, efficiency, and cost to prevent wasteful training runs. During execution, GreenTune streams live GPU telemetry from AMD MI300X hardware, monitoring watts, joules-per-token, throughput, CO2 emissions, and infrastructure cost in real time through a web dashboard. In testing, the platform achieved up to 45–69% improvements in energy efficiency while fine-tuning Qwen2.5-7B with QLoRA on Hermes reasoning datasets. Our vision is to become the energy intelligence layer for AI infrastructure, helping enterprises make AI workloads observable, governable, and energy-aware as compute demand continues to scale.