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+ ---
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+ license: agpl-3.0
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+ tags:
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+ - anime
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+ - image-generation
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+ - sdxl
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+ - stable-diffusion
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+ - 3d
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+ - deterministic
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+ - dora
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+ - text-to-image
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+ - noobai
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+ language:
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+ - en
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+ pipeline_tag: text-to-image
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+ ---
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+
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+ # 4CGT — 四彫画匠 (Shichō Gashō)
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+
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+ **Master Artisan of Sculpted Art** — quality-engineered anime image generation with cross-platform determinism, DoRA anatomical accuracy, and integrated 2D→3D conversion.
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+
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+ **GitHub**: [teenu/4cgt](https://github.com/teenu/4cgt)
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+ **Website**: [4cgt.com](https://4cgt.com)
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+
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+ ## Assets
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+
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+ This repo contains all assets needed to run 4CGT:
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+
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+ | File | Size | Description |
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+ |------|------|-------------|
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+ | `NoobAI-XL-Vpred-v1.0.safetensors` | 6.7 GB | NoobAI XL V-Pred 1.0 model (BF16) |
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+ | `controlnet/openpose_fp32.safetensors` | 4.7 GB | OpenPose ControlNet for pose control |
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+ | `dora/noobai_vp10_stabilizer_v0.280a_fp16.safetensors` | 64 MB | DoRA stabilizer v0.280a (recommended) |
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+ | `dora/noobai_vp10_stabilizer_v0.271_fp16.safetensors` | 44 MB | DoRA stabilizer v0.271 |
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+ | `style/*.csv` | 109 MB | Danbooru + e621 character/artist databases |
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+ | `poses/*.png` | 140 KB | Example OpenPose skeleton images |
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+
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+ ## Quick Start
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+
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+ ```bash
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+ # 1. Clone the tool
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+ git clone https://github.com/teenu/4cgt.git
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+ cd 4cgt
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+
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+ # 2. Install PyTorch (pick your GPU)
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+ pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
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+
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+ # 3. Install dependencies
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+ pip install -r requirements.txt
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+
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+ # 4. Download assets from this repo
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+ pip install huggingface-hub
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+ huggingface-cli download epigene/4cgt --local-dir assets
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+
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+ # 5. Move assets into place
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+ mv assets/NoobAI-XL-Vpred-v1.0.safetensors .
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+ mv assets/dora/* dora/
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+ mv assets/controlnet/* controlnet/
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+ mv assets/style/* style/
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+ mv assets/poses/* poses/
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+
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+ # 6. Run
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+ python main.py
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+ ```
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+
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+ ## Four Pillars
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+
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+ - **Anatomical Accuracy** — DoRA weight-decomposed adapters optimized for v-prediction
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+ - **Prompt Fidelity** — Long prompt support up to 600 tokens with A1111-compatible syntax
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+ - **Image Quality** — Lossless FP32 VAE, BF16/FP32 only, FP16 rejected by design
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+ - **Deterministic Output** — Same seed = identical image hash across macOS, Windows, Linux
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+
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+ ## License
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+
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+ AGPL-3.0. See [GitHub](https://github.com/teenu/4cgt) for full license.