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Stability AI: A New Era of Generative AI

Stability AI stands at the forefront of the generative AI revolution. Founded by visionary Emad Mostaque, this company is reshaping the landscape of artificial intelligence by offering open-source, cutting-edge AI models. Their work spans across imaging, language, code, and audio, aiming to unlock global creativity, innovation, and productivity.

General
Relese date2019
WebsiteLink
TypeOpen Source Generative AI Model

Stability AI has made significant strides in the field of generative artificial intelligence, providing a range of models that cater to diverse aspects of AI application. Under the leadership of founder Emad Mostaque, the company has been dedicated to democratizing AI technology, making it accessible to a broader audience. Their suite of models demonstrates this commitment, each serving a unique purpose in the AI ecosystem.

Key Models Developed by Stability AI:

1. Stable Diffusion XL

2. Stable Audio

  • Focus: Audio generation and manipulation.
  • Details: Stable Audio.

3. Stable LM (Language Model)

4. Stable Video

5. Stable Code

6. Stable Beluga

7. Japanese Stable LM

Overview of Stability AI's Approach

Stability AI's range of models showcases their expertise in various AI domains, from visual arts and audio to language processing and coding. The company's open-source approach not only fosters community involvement but also ensures widespread dissemination of AI benefits. Each model serves as a testament to Stability AI's commitment to innovation and their goal of activating humanity's potential through AI technology.

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Stability AI AI Technologies Hackathon projects

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

ReelBudget

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.

ReelBudget

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.

Gemma Captioner: Four Voices, One Truth

Gemma Captioner: Four Voices, One Truth

Most video captioning agents write all four styles in a single pass, so one visual mistake poisons every caption. And the funnier a caption tries to be, the more it tends to invent: a tech joke reaches for packets and uptime that were never there, a sarcastic line flips what actually happened. Track 2 scores both accuracy and tone, so a caption that reads well can still be wrong. We built it the other way around. Instead of adding style at the end, the agent uses Google Gemma-4-31B to look at the video once. It turns sampled frames into a grounded record of what is actually there: it reads clearly legible on-screen text like signs and logos, names concrete colors and counts, and flags whatever it cannot be sure of. Any detail a caption asserts that the frozen facts do not support is rejected and rewritten, so unsupported claims never survive. Only then do four parallel Gemma-4 text passes turn the same verified facts into formal, sarcastic, humorous-tech, and humorous-non-tech voices. A joke can change the tone but never the events, because the humor voice has to map its metaphor onto a real visible detail. The other hard part is speed. Long UHD clips are the trap, because a clip that finishes too late is graded as a placeholder. So the agent decodes only about ten frames per clip, aligned to the exact timestamps the judge samples for accuracy, rather than the whole video, and it captions multiple clips concurrently. A complete results.json sits on disk from the first moment and each clip upgrades its own entry as it finishes, so a slow or failed call degrades to a grounded fallback instead of a zero. The full set of clips finishes well inside the 10-minute budget. Gemma does the real work. Every scene fact and every caption comes from Gemma-4-31B: take it out and there is no product. It ships as a public linux/amd64 Docker image that runs headless, reading /input/tasks.json and writing /output/results.json.

Gemma Captioner: Four Voices, One Truth

Gemma Captioner: Four Voices, One Truth

Most video captioning agents write all four styles in a single pass, so one visual mistake poisons every caption. And the funnier a caption tries to be, the more it tends to invent: a tech joke reaches for packets and uptime that were never there, a sarcastic line flips what actually happened. Track 2 scores both accuracy and tone, so a caption that reads well can still be wrong. We built it the other way around. Instead of adding style at the end, the agent uses Google Gemma-4-31B to look at the video once. It turns sampled frames into a grounded record of what is actually there: it reads clearly legible on-screen text like signs and logos, names concrete colors and counts, and flags whatever it cannot be sure of. Any detail a caption asserts that the frozen facts do not support is rejected and rewritten, so unsupported claims never survive. Only then do four parallel Gemma-4 text passes turn the same verified facts into formal, sarcastic, humorous-tech, and humorous-non-tech voices. A joke can change the tone but never the events, because the humor voice has to map its metaphor onto a real visible detail. The other hard part is speed. Long UHD clips are the trap, because a clip that finishes too late is graded as a placeholder. So the agent decodes only about ten frames per clip, aligned to the exact timestamps the judge samples for accuracy, rather than the whole video, and it captions multiple clips concurrently. A complete results.json sits on disk from the first moment and each clip upgrades its own entry as it finishes, so a slow or failed call degrades to a grounded fallback instead of a zero. The full set of clips finishes well inside the 10-minute budget. Gemma does the real work. Every scene fact and every caption comes from Gemma-4-31B: take it out and there is no product. It ships as a public linux/amd64 Docker image that runs headless, reading /input/tasks.json and writing /output/results.json.

ForgeAi

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.

Framure Forge: Text-to-PBR Materials on AMD GPU

Framure Forge: Text-to-PBR Materials on AMD GPU

Framure Forge turns a text prompt into a game-ready PBR material. You describe a surface — "weathered copper, oxidized patina" — and SDXL, running on an AMD Radeon PRO W7900 (gfx1100) through ROCm, renders the albedo. The normal and roughness maps are then derived on the CPU, and the full set is applied live to hero objects in a Three.js showroom, where you can inspect each map and download a zip ready for any engine. It is built for indie game developers, 3D and environment artists, archviz, and students learning PBR — anyone who needs surfaces fast without the asset-store rabbit hole or per-texture licensing. The output is standard PBR maps: nothing proprietary, nothing locked in. It works with Unreal, Unity, Blender, and Godot. The AMD GPU does the real work: a warm 1024px generation takes about ten seconds, verified with rocm-smi and a live /health endpoint reporting gfx1100 and model_loaded:true. The seven materials were proven end-to-end by importing them into Unreal Engine 5. Both frontend and backend are containerized. The public demo runs in a transparent mock mode so judges can try it instantly, with a popover that says so.