Explore my workflow creation space! Feel free to click the link below and experience the future of AIGC generation directly.<a target="_blank" rel="ugc" href="https://www.runninghub.ai?inviteCode=rh-v1559">https://www.runninghub.ai?inviteCode=rh-v1559</a>Fan benefits,register to get 1000 points,daily login 100 points,play 4090!Experience the super power of 48G.## Model OverviewWelcome to Krea2_MangaTint_HardboiledNoir!A specialized anime style LoRA tailored for Hardboiled Action Anime, violent cinematic aesthetic, film noir atmospheres, and high-contrast storytelling illustrations, newly added to the Krea2_MangaTint family.## ⚙️ Recommended Parameters (Optimized for Krea2 Turbo)To capture the sharp cinematic lighting and dark gritty atmosphere, please apply the following configurations:* Base Architecture: Krea2* **Inference Pipeline:** Krea2 Turbo Version* Sampling Steps: 8 steps (Strictly optimized for Turbo speed)* CFG Scale: 1.0 (Crucial for Turbo models)* LoRA Weight: 0.8 - 1.0 (0.9 is highly recommended for balanced lighting contrast)* Core Trigger Tags: <code>mangatint style</code>, <code>hardboiled anime</code>, <code>cinematic action scene</code>, <code>backlighting</code>, <code>noir style</code>, <code>blood splatters</code>, <code>gunfight aftermath</code>, <code>intense shadow contrast</code>## 🎨 Prompting Guide* To boost the dark action &amp; badass vibe: <code>cool female agent holding gun</code>, <code>aftermath of intense shootout</code>, <code>blood splatters on clothes</code>, <code>dramatic stance</code>* To enhance the cinematic room lighting: <code>strong backlighting through giant windows</code>, <code>vibrant orange curtain background</code>, <code>deep pitch dark indoor shadows</code>, <code>cinematic contrast</code>

Krea2_MangaTint_CinematicAction

Krea2_MangaTint_CinematicAction.safetensors

v1.0


# Krea2_漫彩_暴力美学

## 模型介绍

本模型依托魔搭社区（ModelScope）AIGC专区[模型训练](https://modelscope.cn/aigc/modelTraining)环境与算力完成训练。

* 模型类型：LoRA
* 基础模型：[krea/Krea-2-Turbo](https://modelscope.cn/models/krea/Krea-2-Turbo)
* 训练代码：[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* 训练数据量：90
* 总训练步数：10000
* 开源协议：Apache-2.0

## 推理代码

安装 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)：

```bash
pip install diffsynth
```

开始推理：

```python
from diffsynth.pipelines.krea2 import Krea2Pipeline, ModelConfig
import torch

pipe = Krea2Pipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="krea/Krea-2-Turbo", origin_file_pattern="turbo.safetensors"),
        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="zhouwenbin1994/Krea2_MangaTint_CinematicAction", origin_file_pattern="Krea2_MangaTint_CinematicAction_c1-st10000.safetensors"))
prompt = "a cat"
image = pipe(
    prompt, seed=0,
    num_inference_steps=8, cfg_scale=1, mu=1.15,
)
image.save("image.jpg")
```



Files with SHA256: c0da12080c00027db4f1abf60befc55f85a6ef03ccceb3794f9b88439e0abbe4