README.md

---
base_model: Lykon/dreamshaper-8
library_name: diffusers
pipeline_tag: text-to-image
license: creativeml-openrail-m
inference: false
tags:
- stable-diffusion
- stable-diffusion-1-5
- safetensors
- diffusers
- dark-anime
- text-to-image
- image-to-image
- turkish-prompts
---

<div align="center">

# InkNoir Core

### Dark anime illustration model · SD 1.5 · Diffusers · Safetensors

[![Hugging Face](https://img.shields.io/badge/Hugging_Face-InkNoir_Core-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/YunusTAS13/InkNoir-Core)
[![License](https://img.shields.io/badge/Base_License-CreativeML_OpenRAIL--M-4c6ef5?style=for-the-badge)](MODEL_LICENSE_NOTICE.md)
[![Pipeline](https://img.shields.io/badge/Pipeline-Diffusers-7c3aed?style=for-the-badge)](https://huggingface.co/docs/diffusers)
[![Model](https://img.shields.io/badge/Model-SD_1.5-111827?style=for-the-badge)](https://huggingface.co/YunusTAS13/InkNoir-Core)

**InkNoir Core**, YunusTAS13 tarafından geliştirilen karanlık anime ve illüstrasyon odaklı bir Stable Diffusion 1.5 modelidir.

[Modeli indir](https://huggingface.co/YunusTAS13/InkNoir-Core) · [Örnekler](examples/prompts.md) · [Bağımsız testler](examples/independent/benchmark_report.md)

</div>

---

## English

## What is InkNoir Core?

InkNoir Core is a dark-anime illustration model based on [DreamShaper 8](https://huggingface.co/Lykon/dreamshaper-8). The InkNoir style adapter was fused into a complete standalone pipeline at LoRA scale `0.4`.

This repository includes both a full Diffusers pipeline and a single-file checkpoint. It is not an adapter-only release.

### What can it do?

| Capability | Status |
|---|---|
| Text-to-image | Supported |
| Dark anime illustration | Main focus |
| Character portraits and full-body art | Supported, hands may fail |
| Image-to-image | Supported through Diffusers/local UIs |
| Inpainting | Use an SD 1.5 inpainting pipeline with this style |
| GIF/video frames | Possible as a separate frame workflow; no native temporal consistency |
| Pixel-art and sprite sheets | Experimental; exact cell layouts are not guaranteed |
| Turkish prompts | Basic external translation is recommended |

## Gallery — independent, unmasked subjects

<p align="center">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/05_bookstore_interior.png" width="31%" alt="Bookstore interior">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/06_still_life.png" width="31%" alt="Still life">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/04_red_sports_car.png" width="31%" alt="Red sports car">
</p>

<p align="center">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/03_mountain_lake.png" width="31%" alt="Mountain lake">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/07_fantasy_dragon.png" width="31%" alt="Fantasy dragon">
  <img src="https://huggingface.co/YunusTAS13/InkNoir-Core/resolve/main/examples/independent/08_robot_workshop.png" width="31%" alt="Robot workshop">
</p>

All gallery prompts, seeds and settings are available in [`examples/prompts.md`](examples/prompts.md). The unrelated-subject benchmark is available in [`examples/independent/benchmark_report.md`](examples/independent/benchmark_report.md).

## Files in this repository

| File or folder | Description |
|---|---|
| `InkNoir_Core_Full.safetensors` | Standalone fused SD 1.5 checkpoint |
| `model_index.json`, `unet/`, `text_encoder/`, `vae/`, `tokenizer/`, `scheduler/` | Complete Diffusers pipeline |
| `adapters/InkNoir_Core.safetensors` | Optional unfused LoRA adapter |
| `run_inknoir.py` | Beginner-friendly local launcher |
| `requirements.txt` | Python dependencies |
| `examples/` | Gallery, prompts, independent tests and comparisons |
| `MODEL_LICENSE_NOTICE.md` | Base-model license and attribution notes |
| `PUBLISH_CHECKLIST.md` | Release verification checklist |

## Quick start

### Easiest local installation

```bash
git lfs install
git clone https://huggingface.co/YunusTAS13/InkNoir-Core
cd InkNoir-Core

python -m venv .venv
source .venv/bin/activate              # Windows: .venv\\Scripts\\activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

python run_inknoir.py \\
  --prompt "dark anime illustration, a lone warrior in a rainy neon city" \\
  --output inknoir_output.png
```

The launcher automatically selects CUDA, Apple MPS or CPU. The first run downloads or loads the model and may take a few minutes.

### Linux with fish

```fish
source .venv/bin/activate.fish
python -m pip install -r requirements.txt
python run_inknoir.py --prompt "dark anime illustration, yağmurlu neon şehir"
```

## System requirements

| Component | Minimum practical | Recommended |
|---|---:|---:|
| GPU | 4–6 GB VRAM at 512x512 | NVIDIA CUDA GPU with 8 GB+ VRAM |
| RAM | 8 GB | 16 GB |
| Storage | 5 GB free | 8–10 GB free on SSD |
| CPU | Modern 4-core CPU | Modern 6–8-core CPU |
| OS | Linux, Windows, macOS may work | Linux or Windows |
| Python | 3.10–3.12 | 3.11 or 3.12 |

CPU generation is supported but significantly slower. Lower the resolution and steps if memory is limited.

## Diffusers usage

```python
import torch
from diffusers import StableDiffusionPipeline

model_path = "YunusTAS13/InkNoir-Core"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

pipe = StableDiffusionPipeline.from_pretrained(
    model_path,
    torch_dtype=dtype,
    safety_checker=None,
    requires_safety_checker=False,
).to(device)
pipe.enable_attention_slicing()

image = pipe(
    "dark anime illustration, a quiet bookstore at night, cinematic lighting",
    negative_prompt="low quality, blurry, deformed, bad anatomy, text, watermark",
    width=512,
    height=512,
    num_inference_steps=28,
    guidance_scale=7.0,
).images[0]
image.save("result.png")
```

## Automatic1111, Forge and ComfyUI

Use `InkNoir_Core_Full.safetensors` as a normal **SD 1.5 checkpoint**:

1. Download the file from this repository.
2. Put it in the checkpoint/model folder.
3. Select it as the active checkpoint.
4. Generate with a prompt.

Do not put the full checkpoint in the LoRA folder. The style is already fused into the full checkpoint. If you use the optional adapter instead, load it on DreamShaper 8 with strength `0.3–0.5`.

## Recommended settings

| Setting | Starting value | Note |
|---|---:|---|
| Resolution | 512x512 | Safe SD 1.5 starting point |
| Steps | 24–32 | More steps are not always better |
| CFG | 6.5–7.5 | Very high CFG may add artifacts |
| Full checkpoint | No extra LoRA | Style is already fused |
| Separate LoRA | 0.3–0.4 | Start low and increase carefully |
| Sampler | DPM++ 2M Karras or Euler a | Good starting choices |

Start with a neutral negative prompt:

```text
low quality, blurry, deformed, bad anatomy, text, watermark
```

## Image-to-image and inpainting

For image-to-image, use `strength=0.25–0.40` when preserving the reference is important, and `0.45–0.65` for stronger changes. For precise object replacement, use a dedicated SD 1.5 inpainting pipeline.

```python
from PIL import Image
from diffusers import StableDiffusionImg2ImgPipeline

img2img = StableDiffusionImg2ImgPipeline.from_pipe(pipe)
source = Image.open("reference.png").convert("RGB").resize((512, 512))

result = img2img(
    prompt="dark anime style, preserve the character silhouette, colder blue lighting",
    image=source,
    strength=0.35,
    guidance_scale=7.0,
    num_inference_steps=28,
).images[0]
result.save("img2img_result.png")
```

## Turkish prompts

The SD 1.5 text encoder is more reliable with English. Turkish prompts can be translated before generation:

```text
Türkçe: karanlık anime tarzında, yağmurlu neon şehirde yalnız bir savaşçı
İngilizce: dark anime style, a lone warrior in a rainy neon city
```

The model itself does not contain a general Turkish translation model. See [`examples/prompts.md`](examples/prompts.md) for bilingual examples.

## Animation, GIF and sprite sheets

InkNoir Core is a still-image model. It can produce individual frames or keyframes, but it does not guarantee temporal consistency by itself. For animation:

- keep a fixed seed and character description;
- generate frames with a separate assembly/stabilization workflow;
- use a dedicated video model when motion consistency is required;
- generate sprite frames separately when exact frame count and cell placement matter.

## Independent evaluation

The independent test set intentionally includes unrelated subjects: an unmasked woman, golden retriever, mountain lake, red sports car, bookstore, still life, dragon and robot workshop. It is included to show both strengths and failure cases instead of only the training-like masked character.

- [Independent benchmark report](examples/independent/benchmark_report.md)
- [Prompt and strength control comparison](examples/prompt_control/README.md)
- [All prompts and seeds](examples/prompts.md)

## Known limitations

- This is an experimental derivative model, not a perfect general-purpose image model.
- Hands, text, exact identity and complex object insertion may fail.
- High adapter strength can produce face artifacts.
- The full checkpoint does not provide native temporal consistency.
- Turkish works best after an external translation step.
- Pixel-art and exact sprite-sheet layouts are experimental.

## License and attribution

The base [DreamShaper 8](https://huggingface.co/Lykon/dreamshaper-8) model is listed by its author under `creativeml-openrail-m`. This fused derivative must retain the base model’s usage restrictions and attribution requirements. Read [`MODEL_LICENSE_NOTICE.md`](MODEL_LICENSE_NOTICE.md) before redistribution.

The publisher must verify that all curated training images are cleared for redistribution. Users are responsible for their outputs and use of the model.

---

## Türkçe

## InkNoir Core nedir?

InkNoir Core, [DreamShaper 8](https://huggingface.co/Lykon/dreamshaper-8) tabanlı, karanlık anime ve illüstrasyon üretimine odaklanan Stable Diffusion 1.5 modelidir. InkNoir stil adaptörü `0.4` gücüyle tam modele birleştirilmiştir.

Bu repo yalnızca küçük bir LoRA dosyasından oluşmaz. Yaklaşık 2 GB’lık tam checkpoint, eksiksiz Diffusers pipeline’ı, çalıştırıcıyı, örnekleri ve testleri içerir.

## Hızlı kullanım

```bash
git clone https://huggingface.co/YunusTAS13/InkNoir-Core
cd InkNoir-Core
python -m venv .venv
source .venv/bin/activate       # Windows: .venv\\Scripts\\activate
python -m pip install -r requirements.txt
python run_inknoir.py --prompt "karanlık anime tarzında yağmurlu neon şehir"
```

Başlangıç için 512x512, 24–32 adım ve CFG 6.5–7.5 kullanın. Türkçe promptları İngilizceye çevirerek göndermek daha tutarlı sonuç verir.

## İçerik

- `InkNoir_Core_Full.safetensors`: tek dosyalık tam model;
- `model_index.json`, `unet`, `text_encoder`, `vae`, `tokenizer`, `scheduler`: eksiksiz Diffusers pipeline’ı;
- `adapters/InkNoir_Core.safetensors`: isteğe bağlı ayrı LoRA;
- `run_inknoir.py`: kolay yerel çalıştırıcı;
- `examples/`: görseller, promptlar ve bağımsız testler.

## Görseli değiştirmeden görme özelliği

InkNoir Core kendi başına görüntü-anlama modeli değildir. Hugging Face’te bir VLM/image-text-to-text modeli eklenerek fotoğrafı değiştirmeden analiz ettirmek mümkündür. Örneğin kullanıcı fotoğraf yükleyip “Bu görselde ne var?” diye sorabilir; model yalnızca metin cevabı üretir. Bu özellik image-to-image işleminden farklıdır.

## Sınırlamalar

Model deneysel bir stil modelidir. El, yazı, kesin karakter kimliği, karmaşık nesne ekleme ve animasyon tutarlılığı garanti edilmez. GIF/video için ayrı kare üretim ve birleştirme süreci gerekir.

## Lisans

DreamShaper 8’in lisans ve kullanım koşulları geçerlidir. Ayrıntılar için [`MODEL_LICENSE_NOTICE.md`](MODEL_LICENSE_NOTICE.md) ve [DreamShaper 8 model kartına](https://huggingface.co/Lykon/dreamshaper-8) bakın.

---

<div align="center">

**InkNoir Core · YunusTAS13**

[Hugging Face](https://huggingface.co/YunusTAS13/InkNoir-Core) · [GitHub](https://github.com/YunusTAS13)

</div>