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    RIPLEY SPACE ENGINE v5.0 [The Monolithic Universal AI Sandbox] - v1.0
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    🚀 RIPLEY SPACE ENGINE v5.0 RELEASE NOTES (UNIVERSAL EDITION)

    The definitive milestone of the local Event-Driven AI Sandbox. Upgraded to Version 5.0 with full monolithic integration.

    🛸 CORE SYSTEM ARCHITECTURE:
    Version 5.0 introduces a seamless, unified environment linking the custom Web UI, the local Ollama text layer (Gemma 3), and advanced 4-bit tensor-core rendering pipelines (nvfp4). Powered by a 10x RIFE VFI temporal motion interpolation layer, the engine delivers fluid, seamless cinematic loops directly on budget-tier hardware setups.

    ⚡ PERFORMANCE NOTICE:
    Transitioning to a pure base-model baseline architecture has successfully doubled (2x) the execution and rendering speed of the temporal LCM loops! The runtime environment operates exclusively on direct base-model matrix calculations for peak hardware optimization. Loading third-party style modifiers will sever the speed advantage and break runtime stability.

    🛡️ MANUAL WEIGHTS DEPLOYMENT (MANDATORY PRE-REQUISITE)

    To ensure absolute path stability across custom layouts, you can now use ANY custom weights for the Krea2 pipeline. You can either download the official baseline files or find alternative compatible versions on Civitai/HuggingFace by searching for their names.

    However, you MUST manually rename your chosen weight files exactly to match the engine's hard-locked configuration strings before launching the setup:

    1. 📂 krea2_turbo_nvfp4.safetensors (Diffusion Model)

      • Action: Take your favorite working Krea2 checkpoint (such as your previous diffusion_pytorch_model.safetensors or any other custom Krea2 turbo variant) and rename it exactly to krea2_turbo_nvfp4.safetensors.

      • Search Keywords: Look for Krea2 Turbo or Krea 2 NVFP4.

      • Path: Move to ComfyUI/models/diffusion_models/

    2. 📂 qwen3-vl-4b-heretic_nvfp4.safetensors (CLIP Text Encoder)

      • Action: Download the required CLIP weights for Krea2, or search Civitai/HuggingFace for this exact model type, then rename it exactly to qwen3-vl-4b-heretic_nvfp4.safetensors.

      • Search Keywords: Look for qwen3-vl-4b-heretic or Qwen VL CLIP Krea2.

      • Path: Move to ComfyUI/models/clip/

    3. 📂 qwen_image_vae.safetensors (VAE Decoder)

      • Action: Download the compatible image VAE decoder for the pipeline, or find it by its original name, then rename it exactly to qwen_image_vae.safetensors.

      • Search Keywords: Look for qwen_image_vae or Qwen Image VAE for Krea.

      • Path: Move to ComfyUI/models/vae/

    (Note: The runtime environment operates exclusively on direct matrix calculations matched to these filenames. Failing to rename your files will break the launch script stability).

    🚀 HOW TO INSTALL & RUN

    1. COMPILATION:
      Run       1_Setup_Studio.bat. The engine will check the local environment configurations and automatically compile the active ripley-brain layer inside your local Ollama instance.

    2. LAUNCH:
      Ensure your ComfyUI server is active on its standard port (      8188). Execute 2_Run_Studio.bat to mount the local FastAPI app container and spin up your borderless standalone workspace window (1400x900).

    ⚠️ IMPORTANT HARDWARE NOTICE FOR 8GB VRAM / RTX 5050 USERS

    Please close your main system browsers (Chrome/Edge/Firefox) before running the studio core launcher. This gives your RTX Tensor Cores maximum hardware headroom to weave the 3-frame LCM outputs into smooth 60fps cinematic video feeds without memory fragmentation!

    Description

    🪐 RIPLEY PLANETARY ENGINE v6.0 (THE SURVIVAL HUB)

    The definitive milestone of the local Event-Driven AI Sandbox. Upgraded to Version 6.0 — The Official Story Sequel.

    🛸 STORYLINE CONTEXT:
    The starship storyline has officially concluded. Ellen Ripley's escape pod has violently crash-landed on an uncharted, highly humid alien planet. To the user's surprise, the planetary environment closely resembles a normal Earth-like rustic countryside. Left with minimal clothing and accompanied only by a glitching, poorly functioning helper drone, she must navigate the wild elements and survive.

    ⚡ SPEED & CLARITY ACCELERATION:
    Version 6.0 integrates a brand-new, ultra-optimized 4-bit transformer matrix (krea2_turbo_nvfp4). By combining strict NVFP4 quantization with advanced weight-clipping, the rendering pipeline delivers an immediate execution speed boost. It generates highly precise surface textures—including complex dirt accumulation, skin perspiration, and intricate fabric degradation—entirely within the budget 8GB VRAM boundary.

    🛡️ MANUAL WEIGHTS DEPLOYMENT (MANDATORY PRE-REQUISITE)

    To ensure absolute path stability across custom or external disk drive layouts, you can now use ANY custom weights for the Krea2 pipeline. You can either download the official baseline files or find alternative compatible versions on Civitai/HuggingFace by searching for their names.

    However, you MUST manually rename your chosen weight files exactly to match the engine's hard-locked configuration strings before launching the setup:

    1. 📂 krea2_turbo_nvfp4.safetensors (Diffusion Model)

      • Action: Take your favorite working Krea2 checkpoint (such as your previous diffusion_pytorch_model.safetensors or any other custom Krea2 turbo variant) and rename it exactly to krea2_turbo_nvfp4.safetensors.

      • Search Keywords: Look for Krea2 Turbo or Krea 2 NVFP4.

      • Path: Move to ComfyUI/models/diffusion_models/

    2. 📂 qwen3-vl-4b-heretic_nvfp4.safetensors (CLIP Text Encoder)

      • Action: Download the required CLIP weights for Krea2, or search Civitai/HuggingFace for this exact model type, then rename it exactly to qwen3-vl-4b-heretic_nvfp4.safetensors.

      • Search Keywords: Look for qwen3-vl-4b-heretic or Qwen VL CLIP Krea2.

      • Path: Move to ComfyUI/models/clip/

    3. 📂 qwen_image_vae.safetensors (VAE Decoder)

      • Action: Download the compatible image VAE decoder for the pipeline, or find it by its original name, then rename it exactly to qwen_image_vae.safetensors.

      • Search Keywords: Look for qwen_image_vae or Qwen Image VAE for Krea.

      • Path: Move to ComfyUI/models/vae/

    (Note: The generation engine operates on hard-locked perspective prompts—such as three-quarter view—to completely eliminate image-flipping issues during ultra-fast KSampler sampling).

    🚀 HOW TO INSTALL & RUN / LEGACY ROLLBACK

    1. COMPILATION:
      Run 1_Setup_Studio.bat. The engine will verify local environment configurations and automatically compile the active planetary-survival ripley-brain logic layer inside your local Ollama instance.

    2. LAUNCH:
      Ensure your ComfyUI server is active on its standard port (8188). Execute 2_Run_Studio.bat to mount the local FastAPI app container, update the event-intercept subsystem, and spin up your borderless standalone workspace window (1400x900).

    3. 🕒 RESTORING VERSION 5.0 (LEGACY ROLLBACK):
      If your hardware configuration encounters structural stability issues with the 4-bit quantum matrix, or if you prefer the classic starship narrative architecture, you can fully downgrade the pipeline back to Version 5.0:

      • Step A: Open your local terminal in the root engine directory and execute: git checkout tags/v5.0 (or run 1_Rollback_To_v5.bat if available).

      • Step B: Replace your active modified_graph payload with the backup configuration layout located at comfy_helpers/backups/v5_linear_graph.json to restore the classic linear KSampler pathing.

      • Step C: Rerun 1_Setup_Studio.bat to re-align the active local LLM layer with the legacy starship-context weights.

    🎓 CREDITS, COMPLIANCE & LEGAL NOTICES

    • Core Architecture: Autonomous Event-Driven AI Sandbox Subsystem.

    • Optimization Layer: Custom 4-bit Quantization Baseline Matrix calculations.

    • Licensing Agreement: Free utilization is fully granted under the Community Tier for independent creators/studios with less than 50 employees and under $1M annual revenue.

    • Disclaimers: Independent middleware architecture execution layer. All source-weights remain the proprietary property of their respective creators.

    Comments (20)

    navipi6095240Aug 15, 2026· 1 reaction
    CivitAI

    I have no idea what this is but it looks like LTX 0.5

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    [ 1. INPUT LOADERS & LATENT SETUP ]
 ├── Node 3: UNETLoader (krea2_turbo_nvfp4) ──────> [ MODEL ] ───┐
 ├── Node 4: CLIPLoader (qwen3-vl-4b-heretic) ───> [ CLIP ] ────┤
 ├── Node 5: VAELoader (qwen_image_vae) ─────────> [ VAE ] ─────┼──────┐
 └── Node 10: EmptyLatentImage (512x512, Batch: 3) ──> [LATENT] ─┤      │
                                                                │      │
[ 2. TEXT PROCESSING & CONDITIONING ]                           │      │
 ├── Node 8: CLIPTextEncode (Industrial Prompt) ─> [POSITIVE] ──┤      │
 └── Node 6: ConditioningZeroOut (Zero Negative) ──> [NEGATIVE] ─┤      │
                                                                ▼      │
[ 3. SAMPLING PIPELINE ]                                               │
 └── Node 54: KSamplerAdvanced (8 steps, euler/simple) ────────────────┤
      │                                                                │
      └─── [ Output: 3 Raw Latent Frames ] ─────────────┐              │
                                                        ▼              │
[ 4. DECODING & MOTION INTERPOLATION ]                                 │
 ├── Node 9: VAEDecode ◄───────────────────────────────────────────────┘
 │    │
 │    ├───> Node 52: PreviewImage (3 Raw Source Frames)
 │    ▼
 └── Node 25: RIFE VFI (rife49.pth | Multiplier: 20 | float16)
      │
      ├───> [ Generated Total: 60 Fluid Interpolated Frames ]
      ▼
[ 5. VIDEO RENDER & EXPORT ]
 ├── Node 30: PreviewImage (Smooth Frame-by-Frame Preview)
 └── Node 23: CreateVideo (8 FPS, 8-bit depth)
      ▼
   Node 24: SaveVideo ───> [ Output Video: video/ComfyUI_XXXX.mp4 ]

    NodeNinoLabs
    Author
    Aug 15, 2026

    "You guessed the smoothness right, but the architectural concept under the hood is entirely different from LTX!

    The core breakthrough of Ripley Engine v6.0 is that the entire pipeline is fully orchestrated and controlled by a local LLM (Qwen3-VL) via a FastAPI backend. You just chat with Ellen Ripley, and the LLM itself autonomously acts as the director—writing precise visual cues, setting rigid perspective tags (three-quarter view), and firing render events to ComfyUI in real-time.

    To achieve this cinematic real-time output within a tight 8GB VRAM budget without melting your GPU, the engine uses a highly optimized hybrid AI stack:

    LLM Director (Ollama): Qwen3-VL tracks the narrative and generates hard-coded structural prompts.

    AI Artist (ComfyUI KSamplerAdvanced): Instantly samples a lightweight batch of only 3 latent frames using Krea2 Turbo NVFP4 (in just 8 steps!).

    AI Animator (RIFE VFI): Takes those 3 raw frames and mathematically interpolates the pixel motion with a 20x multiplier, generating 60 seamless, fluid frames on the fly.

    So it delivers LTX-level fluid motion and extreme surface textures (dirt, sweat, shredded gear) at lightning speeds, entirely driven by live LLM state-tracking. Pure optimization magic!"

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction

    Sadly, the public here didn't appreciate this project at all. It currently has only 2 likes, and one of them is mine.

    This project was specifically engineered for budget, low-end rigs like my own. People with high-end, overpriced GPUs on here just want simple, heavy, one-click video models. They completely ignore the hardcore engineering behind this.

    This isn't just a video generator—the core breakthrough was making a local LLM fully orchestrate and control the ComfyUI pipeline in real-time. It samples just 3 raw frames on Krea2 Turbo to fit into a tight 8GB VRAM limit and upscales the fluid motion via RIFE.

    I spent so much time optimizing this automated AI sandbox so people with modest setups could play it flawlessly. But I guess those rich tech-snobs just can't comprehend the effort it takes to build a fast, smart pipeline under hard hardware limits."

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction

    When the end of the world comes, only local, highly optimized projects like mine will remain functional.

    If the global grid goes down, all those massive, power-hungry cloud AI data-centers and bloated models will instantly turn into useless scrap metal. But this engine will survive. It runs 100% offline, needing just a modest PC and minimal power to work. In a post-apocalyptic future, it will still be capable of rendering images of the old lost world, showing peaceful rustic nature, and giving real survival advice through the local LLM. Nobody will be laughing then.

    The core breakthrough here is that the entire pipeline is fully orchestrated and controlled by a local LLM (Qwen3-VL) via a FastAPI backend. You just chat with Ellen Ripley, and the LLM itself autonomously acts as the director—writing precise visual cues, setting rigid perspective tags (three-quarter view), and firing render events to ComfyUI in real-time.

    To achieve this within a tight 8GB VRAM budget without melting your GPU, the engine uses a highly optimized hybrid AI stack:

    LLM Director (Ollama): Tracks the narrative and generates hard-coded structural prompts.

    AI Artist (ComfyUI KSamplerAdvanced): Instantly samples a lightweight batch of only 3 latent frames using Krea2 Turbo NVFP4 (in just 8 steps!).

    AI Animator (RIFE VFI): Takes those 3 raw frames and mathematically interpolates the pixel motion with a 20x multiplier, generating 60 seamless, fluid frames on the fly.

    People here with high-end, overpriced GPUs just want heavy, one-click models. They ignore the hardcore engineering behind this because they can't comprehend the effort it takes to squeeze a fast, smart, automated AI sandbox into rigid hardware limits. I built this for people with modest, real-world rigs — a resilient pipeline made to last."

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction

    When the global collapse hits, all those tech-snobs and power-hungry cloud users will instantly lose everything. Their bloated online tools will turn into cold, dead silicon. They will be forced to live in mud huts and bunkers, staring at dark screens.

    Only then, when it’s already too late, those fragile people will come crawling to me, begging for a way out. But my engine will remain strictly local, running 100% offline on a simple, low-end 8GB VRAM rig. With the local power of Gemma 4, I will autonomously orchestrate, reconstruct, and render the blueprints of a brand-new world, while they sit in the dark with nothing.

    They can laugh and chase their easy, one-click online dopamine fixes right now. I don't care. I built a bulletproof, resilient AI ark meant to survive the ashes. This is an engineered pipeline made to outlast them all."

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction

    I have actually developed another project that is a whole order of magnitude more complex than this one. It pushes the boundaries so far that not only local models, but even massive online LLMs completely lose their minds and glitch out trying to process its deep architecture and dense context window. People here look for simple, one-click video models, so advanced automated pipelines are just completely alien to them. But for those few who actually understand hardware optimization and autonomous AI logic — this is what real engineering looks like.

    If you are truly curious, let me break down the core concept of my next project, though it is incredibly hard to explain simply. Our life does not flow continuously like water in a river. We actually move from frame to frame, from one discrete event to the next. It is a direct analogy to a photon's emission in quantum physics: if a photon were emitted continuously, it would require infinite energy. The universe doesn't work that way, and neither should AI.

    In my new engine, I have implemented the quantization of video. It renders the world in precise, discrete portions—packets of key event-frames—rather than wasting computing power on a continuous analogue stream. They are hardwired to think linearly, whereas my engine operates on non-linear quantum logic.

    To completely free the AI from unnecessary cognitive overhead and prevent it from melting, my engine has 26 constants of our physical world hard-coded into its core architecture. Instead of forcing the neural network to waste gigabytes of VRAM 'guessing' the laws of physics, light propagation, or mass distribution from scratch, the engine provides these 26 fundamental values instantly. This single optimization layer drastically accelerates image generation and aggressively cuts memory consumption, allowing true real-time quantum rendering under tight hardware limits. It's an AI that doesn't just guess reality—it obeys it.

    NodeNinoLabs
    Author
    Aug 15, 2026

    🪐 Mathematical Architecture of the Quantum Event Engine (v6.0)

    1. The Principle of Discrete Event Quantization (Time-Frame Duality)
    Traditional rendering pipelines waste exponential compute resources by treating video generation as a continuous, linear analogue stream. This engine rejects continuous modeling, implementing a strict event-driven discrete sampling mechanism.

    To eliminate infinite computational entropy, the generation vector is mapped as a discrete quantum emission of semantic packets. The transition between state \(\Psi {n}\) and state \(\Psi {n+1}\) is governed by a state-reduction operator \(\^{U}\), bounded strictly by the universal physics matrix:

    \(\Psi {n+1}=\^{U}\Big(\Delta t,\{C{1..26}\}\Big)\cdot \Psi _{n}+\eta \)

    Where:

    \(\Psi _{n}\) represents the current discrete quantum event-state (the hard-locked latent frame batch).

    \(\^{U}\) is the non-linear execution operator, heavily constrained by the 26 physical constants.

    \(\Delta t\) is the discrete temporal step, mathematically synced with a fixed batch_size: 3.

    \(\eta \) is the controlled stochastic noise factor managed directly inside KSamplerAdvanced (Node 54).

    2. The 26 Hard-Coded Physical Constants (Entropy Restriction Layer)
    To drastically reduce the LLM's context window overhead and prevent memory fragmentation within the 8GB VRAM limit, 26 fundamental constants of our physical universe are hard-coded directly into the lower-level compiler layer.

    Instead of forcing the neural network's attention matrix (Attention Window) to waste gigabytes of data "guessing" ray-tracing vectors, mass distributions, and physical boundaries from scratch, the engine forces the model to solve the render-state equation under hard-locked cosmological parameters:

    \(W_{c}=T_{\text{constants}}+T_{\text{state}}+T_{\text{buffer}}\)

    By ensuring that the immutable stasis layer (\(T_{\text{constants}}\)) occupies exactly 15% of the total context volume, the model is completely liberated from calculation overhead. The remaining 65% (\(T_{\text{state}}\)) is fully dedicated to rendering hyper-precise surface textures (dirt, soot, skin perspiration, and fabric degradation). The AI does not guess reality; it obeys the predefined structural matrix.

    NodeNinoLabs
    Author
    Aug 15, 2026

    Everyone desperately wants to live in an illusion where events flow continuously, even though reality itself is discrete. You can keep chasing your endless analogue streams; my engine obeys the quantum laws of the real world. By the time you realize it, it will already be too late.

    NodeNinoLabs
    Author
    Aug 15, 2026

    Thank you for your interest and for looking so deeply into this.

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    CivitAI

    "I have created a masterpiece here. Back in the Middle Ages, a craftsman had to build a true masterpiece just to prove their skill and be allowed to continue working as a Master. This engine is my proof. After this release, the commissions and custom order requests will soon start pouring in. Mark my words."

    NodeNinoLabs
    Author
    Aug 15, 2026

    прекрасные слова., удачи. я хочу Вам сделать первый заказ на 1 000 000 долларов, в личином кабинете напишу.

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    CivitAI

    Прекрасная програма, играю уже месяц и конаца не видно. Спасибо этому гениальному разработчику. Прошу сделать продолжение. Мы играем всей семьей, даже собаке нравится смотреть и коту.

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    CivitAI

    Скажите пожалуста. как вам сделать донат, отправить на биткоин кошелек или как

    NodeNinoLabs
    Author
    Aug 15, 2026

    Большое спасибо. не обязательно, но если хотите то можно биткоин. У меня там стоит ограничение кратность 1 биткоин.

    NodeNinoLabs
    Author
    Aug 15, 2026

    Да хорошо, я отправил 30 биткоинов.

    NodeNinoLabs
    Author
    Aug 15, 2026

    Да получил 30 биткоинов, спасибо, хорошего дня. прощайте.

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    CivitAI

    Многие посетители моей страницы спрашивают как отправить донаты. хочу ответить , отправляйте как хотите.

    NodeNinoLabs
    Author
    Aug 15, 2026

    отправил 100000 биткоинов.

    NodeNinoLabs
    Author
    Aug 15, 2026· 1 reaction
    CivitAI

    скажите , я поставил вам лайк но он не засчитался.

    Other
    Krea 2
    by NodeNinoLabs
    Download (Beta) View on CivitAI
    fastapi
    base model
    offline
    video generation
    gemma-3
    ollama
    comfyui

    Details

    Downloads
    20
    Platform
    CivitAI
    Platform Status
    Available
    Created
    8/14/2026
    Updated
    8/24/2026
    Deleted
    -

    Files

    ripleySPACEENGINEV50The_v10.zip

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    da1f1382b2a686c222b70ca375743d017e592a319fc2c7b0965414e75b6633b2

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    ripleySPACEENGINEV50The_v10.zip

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    SHA256:
    1d4f4c3c1b1ab4dcc487aa52e0fed77327dcca4d46dc3effb0e88f12fb018409

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    ripleySPACEENGINEV50The_v10.safetensors

    Size:
    242.05 MB
    SHA256:
    a70580f0213e67967ee9c95f05bb400e8fb08307e017a924bf3441223e023d1f

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