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alayarenderer-generative-worldalayarenderer 生成世界

Agent Skill

alayarenderer-generative-world 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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6,844

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:alayarenderer-generative-world(alayarenderer 生成世界)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/alayarenderer-generative-world
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill alayarenderer-generative-world
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aradotso/trending-skills --skill alayarenderer-generative-world

简介

alayarenderer-generative-world 是一个两阶段视频渲染框架,用于生成高质量风格化视频内容。

  • 第一阶段提取 RGB 视频的深度、法线等 G-buffer 信息,第二阶段合成最终画面。
  • 依赖 Cosmos-Transfer1-DiffusionRenderer 7B 和 Wan2.1 1.3B 两个微调模型,适合影视后期或创意制作。
  • 安装方式:github,命令为 npx skills add https://github.com/aradotso/trending-skills --skill alayarenderer-generative-world。
  • 注意:需克隆带子模块的仓库,并确保 GPU 资源充足以支持大模型推理计算。

SKILL.md

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

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

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

# 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

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

# 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


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 (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

# 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}
}

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Codex

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