
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/reason-machines/trending-skills --skill alayarenderer-generative-world- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads alayarenderer-generative-world automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
AlayaRenderer — Generative World Renderer
Skill by ara.so — Daily 2026 Skills collection.
AlayaRenderer is a two-stage framework for high-quality video rendering:
- 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.
- 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
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:
# 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
| Model | Base Model | Size | HuggingFace Link |
|---|---|---|---|
| Inverse Renderer | Cosmos-Transfer1-DiffusionRenderer 7B | ~7B params | Brian9999/world_inverse_renderer |
| Game Editing | Wan2.1 1.3B | ~1.3B params | Brian9999/stylerenderer |
Download and Place Weights
# 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
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:
# 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
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
| Parameter | Description | Example |
|---|---|---|
--checkpoint | Path to fine-tuned .safetensors weights | models/train/Wan2.1-T2V-1.3B_gbuffer/model.safetensors |
--gpu | GPU device index | 0 |
--style | Named style preset | snowy_winter, rainy, night, sunset |
--prompt | Text description of target lighting/atmosphere | See examples below |
--gbuffer_dir | Directory containing G-buffer input frames/video | test_dataset |
--save_dir | Output directory for rendered video | outputs/ |
--num_frames | Number of frames to generate (must be 8n+1) | 81 |
--height | Output height in pixels | 480 |
--width | Output width in pixels | 832 |
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
# 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
# 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
# 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
| Demo | URL |
|---|---|
| Game Editing Demo | https://huggingface.co/spaces/Brian9999/game-editing |
| Project Page | https://alaya-studio.github.io/renderer/ |
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
# If cloned without --recurse-submodules:
git submodule update --init --recursive
CUDA Out of Memory
- Reduce
--num_frames(try41instead of81) - 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
# Valid
--num_frames 81 # 8*10 + 1 ✓
--num_frames 41 # 8*5 + 1 ✓
# Invalid
--num_frames 80 # ✗
--num_frames 60 # ✗
Checkpoint not found
# 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
@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}
}
FAQ
What does the alayarenderer-generative-world 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.
How do I install alayarenderer-generative-world?
Run npx skills add https://github.com/reason-machines/trending-skills --skill alayarenderer-generative-world in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/alayarenderer-generative-world/ (personal) or .claude/skills/alayarenderer-generative-world/ (project).
Which AI agents can use alayarenderer-generative-world?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is alayarenderer-generative-world safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains alayarenderer-generative-world?
It is published by reason-machines in reason-machines/trending-skills. It has 565 recorded installs.
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