Stable Dreamfusion

Stable-Dreamfusion is an avant-garde PyTorch implementation that seamlessly integrates text-to-3D modeling by synergizing the Stable Diffusion text-to-2D model with Dreamfusion technology.

Enabling the translation of textual content into rich 3D visuals, this cutting-edge model leverages a latent diffusion approach and a multi-resolution grid encoder to offer rapid rendering. While it offers groundbreaking potential in fields like 3D gaming, virtual reality, and film production, it's important to note that the current version contains differences from the original paper, resulting in varying generation quality.

With ongoing development and exciting recent support for Perp-Neg, Stable-Dreamfusion stands as a promising frontier in 3D AI technologies.

General
Repository	https://github.com/ashawkey/stable-dreamfusion
Type	3D AI Models

Stable-Dreamfusion Resources:

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

ReelBudget

The Vision Pre-production is one of the most expensive and time-consuming phases of video creation. Whether you are an independent filmmaker pitching a short film or an enterprise marketing team designing a high-budget commercial, the process requires weeks of sketching, planning, and budgeting before a single frame is ever shot. Reelbudget was built to completely automate this process, acting as your autonomous AI creative director and line-producer. How it Works Users simply input a raw text creative brief. Our multi-agent backend instantly parses the creative intent and breaks the script down into structured scenes, characters, and semantic camera directions. The AMD MI300X Advantage To bring these storyboards to life, we needed massive, uncompromised compute power. We proudly deployed our True AI Video Engine on the AMD MI300X Cloud Instance. Rather than relying on simple static images, our pipeline leverages the MI300X's incredible speed to run two state-of-the-art models on the fly: Stable Diffusion XL generates hyper-realistic, anatomically correct base frames. Stable Video Diffusion instantly animates those frames into true moving video clips, physically matching the AI's camera directions. The system then automatically stitches these clips together, burns in dialogue subtitles, and overlays a background soundtrack, outputting a highly polished, ready-to-pitch movie animatic. Feasibility & Cost Estimation Finally, our AI Feasibility Agent analyzes the generated shot list to provide accurate, real-world cost estimations and suggests cheaper production alternatives, saving businesses thousands of dollars in pre-production overhead. Reelbudget proves that with AMD hardware, AI can handle the heavy lifting of video production, allowing creators to focus entirely on the story.

ForgeAi

ForgeAI is a hardware-aware AI model optimization platform that automatically finds the fastest, most efficient version of a model for a specific GPU — starting with AMD MI300X. Instead of manually tuning models for each accelerator, ForgeAI runs a 7-phase optimization pipeline: architecture search finds the best candidate structures, knowledge distillation transfers accuracy from a teacher model, pruning removes redundant weights, quantization compresses from FP32 to INT8, benchmarking measures real performance on target hardware, Pareto analysis identifies optimal latency-accuracy tradeoffs, and Optuna hyperparameter tuning auto-optimizes across 6 parameters with 50 trials and early stopping. The platform consists of a FastAPI backend with 9 optimization modules, a Next.js 14 frontend, and WebSocket-based live progress streaming. Users upload a PyTorch checkpoint, select target hardware, set constraints (max latency, max memory, min accuracy), and watch the pipeline execute in real time. Results include a Pareto frontier chart, before/after performance comparison, and export to ONNX or TorchScript. ForgeAI targets the $100B+ AI inference market where hardware-specific optimization is still done manually. Unlike Neural Magic and OpenVINO (CPU-focused, tool-by-tool), ForgeAI is AMD-native, full-pipeline, and open source under Apache 2.0.

SupplyGenius Pro

Core Features 1. Document Processing & Analysis - Automated analysis of supply chain documents - Extraction of key information (parties, dates, terms) - Compliance status verification - Confidence scoring for extracted data 2. Demand Forecasting & Planning - AI-powered demand prediction - Time series analysis with confidence intervals - Seasonal pattern recognition - Multi-model ensemble forecasting (LSTM, Random Forest) 3.Inventory Optimization - Real-time inventory level monitoring - Dynamic reorder point calculation - Holding cost optimization - Stockout risk prevention 4. Risk Management - Supply chain disruption simulation - Real-time risk monitoring - Automated mitigation strategy generation - Risk score calculation 5. Supplier Management - Supplier performance tracking - Lead time optimization - Pricing analysis - Automated purchase order generation 6. Financial Analytics - ROI calculation - Cost optimization analysis - Financial impact assessment - Budget forecasting 7. Real-time Monitoring - Live metrics dashboard - WebSocket-based alerts - Performance monitoring - System health tracking 8. Security Features - JWT-based authentication - Role-based access control - Rate limiting - Secure API endpoints -- Technical Capabilities 1. AI Integration - IBM Granite 13B model integration - RAG (Retrieval Augmented Generation) - Custom AI toolchains - Machine learning pipelines 2. Data Processing - Real-time data processing - Time series analysis - Statistical modeling - Data visualization 3. Performance Optimization - Redis caching - Async operations - Rate limiting - Load balancing 4. Monitoring & Logging - Prometheus metrics - Detailed logging - Performance tracking - Error handling