Alayarenderer Generative World logo

Alayarenderer Generative World

Organization
reason-machines
alayarenderer-generative-world

AI coding agent skill for AlayaRenderer — a generative world rendering framework with inverse rendering (RGB→G-buffers) and game editing (G-buffers+text→stylized video) using fine-tuned video diffusion models.

Overview

Publisherreason-machines
Repositorytrending-skills
Skill namealayarenderer-generative-world
Stars
80
Forks
15
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by reason-machines on GitHub. Read the source before you install it.

Installation

Install the Alayarenderer Generative World AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/reason-machines/trending-skills.git /tmp/trending-skills
mkdir -p .claude/skills
cp -r /tmp/trending-skills/skills/alayarenderer-generative-world .claude/skills/alayarenderer-generative-world
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alayarenderer Generative World in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alayarenderer Generative World on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alayarenderer Generative World is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AlayaRenderer — Generative World Renderer

Skill by ara.so — Daily 2026 Skills collection.

AlayaRenderer is a two-stage framework for high-quality video rendering:

  1. Inverse Renderer (RGB → G-buffers): Extracts albedo, normal, depth, roughness, and metallic maps from RGB video using a fine-tuned Cosmos-Transfer1-DiffusionRenderer 7B model.
  2. Game Editing (G-buffers + Text → Stylized RGB): Synthesizes photorealistic, stylized RGB video from G-buffer inputs using a fine-tuned Wan2.1 1.3B model via DiffSynth-Studio.

Installation

Clone the Repository

bash
git clone --recurse-submodules https://github.com/ShandaAI/AlayaRenderer.git
cd AlayaRenderer

Important: Use --recurse-submodules — DiffSynth-Studio is a git submodule required for Game Editing.

Two Separate Conda Environments (Recommended)

The two models have conflicting dependencies. Use separate environments:

bash
# Environment 1: Inverse Renderer
conda create -n inverse_renderer python=3.10 -y
conda activate inverse_renderer
cd inverse_renderer
# Follow inverse_renderer/ instructions for Cosmos-Transfer1 setup

# Environment 2: Game Editing
conda create -n game_editing python=3.10 -y
conda activate game_editing
cd game_editing
# Follow DiffSynth-Studio setup instructions

Model Weights

ModelBase ModelSizeHuggingFace Link
Inverse RendererCosmos-Transfer1-DiffusionRenderer 7B~7B paramsBrian9999/world_inverse_renderer
Game EditingWan2.1 1.3B~1.3B paramsBrian9999/stylerenderer

Download and Place Weights

bash
# Inverse Renderer — replace the base checkpoint
huggingface-cli download Brian9999/world_inverse_renderer \
  --local-dir inverse_renderer/checkpoints/Diffusion_Renderer_Inverse_Cosmos_7B

# Game Editing — place in game_editing models directory
mkdir -p game_editing/models/train/Wan2.1-T2V-1.3B_gbuffer
huggingface-cli download Brian9999/stylerenderer \
  --local-dir game_editing/models/train/Wan2.1-T2V-1.3B_gbuffer

Inverse Renderer Usage

The inverse renderer decomposes an RGB video into 5 G-buffer channels: albedo, normal, depth, roughness, metallic.

Setup

bash
cd inverse_renderer
# Follow Cosmos-Transfer1-DiffusionRenderer environment setup
# Ensure checkpoint is at:
# inverse_renderer/checkpoints/Diffusion_Renderer_Inverse_Cosmos_7B/

Inference

Refer to the inverse_renderer/ subdirectory for the full inference script. The general pattern follows Cosmos-Transfer1-DiffusionRenderer conventions:

python
# inverse_renderer/run_inverse.py (typical pattern)
import torch
from pathlib import Path

# Input: path to RGB video
input_video = "path/to/rgb_video.mp4"
output_dir = "outputs/gbuffers/"

# The model outputs 5 synchronized channels:
# - albedo (diffuse color)
# - normal (surface orientation)
# - depth (scene geometry)
# - roughness (surface roughness)
# - metallic (metallic property)

Game Editing Usage

Quick Start — CLI Inference

bash
cd game_editing

CUDA_VISIBLE_DEVICES=0 python \
    examples/wanvideo/model_inference/inference_gbuffer_caption.py \
    --checkpoint models/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors \
    --gpu 0 \
    --style snowy_winter \
    --prompt "the scene is set in a frozen, snow-covered environment under cold, pale winter light with falling snowflakes, creating a silent and ethereal winter wonderland atmosphere." \
    --gbuffer_dir test_dataset \
    --save_dir outputs/ \
    --num_frames 81 \
    --height 480 \
    --width 832

CLI Parameters

ParameterDescriptionExample
--checkpointPath to fine-tuned .safetensors weightsmodels/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors
--gpuGPU device index0
--styleNamed style presetsnowy_winter, rainy, night, sunset
--promptText description of target lighting/atmosphereSee examples below
--gbuffer_dirDirectory containing G-buffer input frames/videotest_dataset
--save_dirOutput directory for rendered videooutputs/
--num_framesNumber of frames to generate (must be 8n+1)81
--heightOutput height in pixels480
--widthOutput width in pixels832

G-buffer Directory Structure

test_dataset/
├── albedo/
│   ├── frame_0000.png
│   ├── frame_0001.png
│   └── ...
├── normal/
│   ├── frame_0000.png
│   └── ...
├── depth/
│   ├── frame_0000.png
│   └── ...
├── roughness/
│   ├── frame_0000.png
│   └── ...
└── metallic/
    ├── frame_0000.png
    └── ...

Style Prompt Examples

bash
# Cyberpunk night scene
--style night \
--prompt "neon-lit urban environment at night with rain-slicked streets reflecting colorful neon signs, creating a cyberpunk noir atmosphere"

# Golden hour / sunset
--style sunset \
--prompt "warm golden hour lighting with long shadows and a glowing amber sky, soft cinematic atmosphere"

# Rainy urban
--style rainy \
--prompt "overcast rainy day with wet surfaces, soft diffuse lighting, and atmospheric fog creating a moody cinematic look"

# Fantasy / stylized
--style fantasy \
--prompt "magical forest environment with bioluminescent plants, ethereal blue-green lighting, and mystical particle effects"

# Foggy morning
--style foggy \
--prompt "early morning dense fog with soft diffused light creating a mysterious and quiet atmosphere"

Multi-GPU Inference

bash
# Run on specific GPU
CUDA_VISIBLE_DEVICES=1 python \
    examples/wanvideo/model_inference/inference_gbuffer_caption.py \
    --checkpoint models/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors \
    --gpu 1 \
    --style rainy \
    --prompt "heavy rainfall with dark storm clouds and dramatic lightning in the distance" \
    --gbuffer_dir my_gbuffers \
    --save_dir outputs/rainy_scene \
    --num_frames 81 --height 480 --width 832

Full Pipeline: RGB Video → Stylized Output

bash
# Step 1: Extract G-buffers from RGB video (Inverse Renderer env)
conda activate inverse_renderer
cd inverse_renderer
python run_inverse.py \
    --input path/to/gameplay_video.mp4 \
    --output_dir ../game_editing/test_dataset/

# Step 2: Apply game editing style (Game Editing env)
conda activate game_editing
cd ../game_editing
CUDA_VISIBLE_DEVICES=0 python \
    examples/wanvideo/model_inference/inference_gbuffer_caption.py \
    --checkpoint models/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors \
    --gpu 0 \
    --style snowy_winter \
    --prompt "frozen tundra with blizzard conditions, pale blue-white lighting and drifting snow" \
    --gbuffer_dir test_dataset \
    --save_dir outputs/final_render \
    --num_frames 81 --height 480 --width 832

Online Demos


Dataset Overview

The AlayaRenderer dataset (release pending) features:

  • 4M+ frames at 720p / 30 FPS
  • 6 synchronized channels: RGB + albedo, normal, depth, metallic, roughness
  • 40 hours from Cyberpunk 2077 and Black Myth: Wukong
  • Average clip length: 8 minutes, up to 53 minutes continuous
  • Weather variants: sunny, rainy, foggy, night, sunset
  • Motion blur variant via sub-frame interpolation

Architecture Summary

RGB Video Input
┌─────────────────────────────────────┐
│  Inverse Renderer                   │
│  (Cosmos-Transfer1 7B fine-tuned)   │
│  RGB → [albedo, normal, depth,      │
│          roughness, metallic]       │
└─────────────────┬───────────────────┘
                  │  G-buffers
┌─────────────────────────────────────┐
│  Game Editing                       │
│  (Wan2.1 1.3B fine-tuned)           │
│  G-buffers + Text Prompt            │
│  → Stylized RGB Video               │
└─────────────────────────────────────┘

Troubleshooting

Submodule not found / DiffSynth-Studio missing

bash
# If cloned without --recurse-submodules:
git submodule update --init --recursive

CUDA Out of Memory

  • Reduce --num_frames (try 41 instead of 81)
  • Reduce resolution: --height 320 --width 576
  • Ensure no other processes are using the GPU: CUDA_VISIBLE_DEVICES=0

num_frames must follow 8n+1 pattern

Valid values: 9, 17, 25, 33, 41, 49, 57, 65, 73, 81

bash
# Valid
--num_frames 81   # 8*10 + 1 ✓
--num_frames 41   # 8*5 + 1  ✓

# Invalid
--num_frames 80   # ✗
--num_frames 60   # ✗

Checkpoint not found

bash
# Verify checkpoint placement
ls game_editing/models/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors
ls inverse_renderer/checkpoints/Diffusion_Renderer_Inverse_Cosmos_7B/

Version conflicts between models

Always use the two separate conda environments (inverse_renderer and game_editing). Do not install both models' dependencies in one environment.


Citation

bibtex
@article{huang2026generativeworldrenderer,
    title={Generative World Renderer},
    author={Zheng-Hui Huang and Zhixiang Wang and Jiaming Tan and Ruihan Yu and Yidan Zhang and Bo Zheng and Yu-Lun Liu and Yung-Yu Chuang and Kaipeng Zhang},
    journal={arXiv preprint arXiv:2604.02329},
    year={2026}
}

Frequently asked questions

What does the Alayarenderer Generative World AI skill do?

AI coding agent skill for AlayaRenderer — a generative world rendering framework with inverse rendering (RGB→G-buffers) and game editing (G-buffers+text→stylized video) using fine-tuned video diffusion models.

Why use Alayarenderer Generative World on TypingMind?

Because you install it once and use it with any model. Alayarenderer Generative World is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alayarenderer Generative World in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reason-machines/trending-skills/tree/main/skills/alayarenderer-generative-world. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alayarenderer Generative World?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alayarenderer Generative World?

As many as you like. As long as a model supports skills, you can use Alayarenderer Generative World with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alayarenderer Generative World AI skill free?

It is published on GitHub by reason-machines. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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