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Template-KleinBase4B-SoftRGB

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Template-KleinBase4B-SoftRGB
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---
frameworks:
- Pytorch
license: Apache License 2.0
tags: []
tasks:
- text-to-image-synthesis
---

# Templates-色调调节（FLUX.2-klein-base-4B）

本模型是 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 开源的 Diffusion Templates 系列模型之一。该模型为色调调节模型，允许用户通过直接输入 `R`、`G`、`B` 三个通道的归一化数值，全局调控画面的色彩倾向与色温氛围。

## 效果展示

> **Prompt:** A cat is sitting on a stone.

| cold | normal | warm |
|:---:|:---:|:---:|
| ![](./assets/cat_rgb_cold.jpg) | ![](./assets/cat_rgb_normal.jpg) | ![](./assets/cat_rgb_warm.jpg) |

---

> **Prompt:** A cinematic portrait of a beautiful woman looking out a rainy window.

| cold | normal | warm |
|:---:|:---:|:---:|
| ![](./assets/girl_rgb_cold.jpg) | ![](./assets/girl_rgb_normal.jpg) | ![](./assets/girl_rgb_warm.jpg) |

---

> **Prompt:** A modern minimalist living room with furniture.

| cold | normal | warm |
|:---:|:---:|:---:|
| ![](./assets/room_rgb_cold.jpg) | ![](./assets/room_rgb_normal.jpg) | ![](./assets/room_rgb_warm.jpg) |

## 推理代码

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

```
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
```

* 直接推理，需 40G 显存

```python
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch

pipe = Flux2ImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
)
template = TemplatePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[ModelConfig(model_id="DiffSynth-Studio/Template-KleinBase4B-SoftRGB")],
)
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 128/255,
        "G": 128/255,
        "B": 128/255
    }],
)
image.save("image_rgb_normal.jpg")
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 208/255,
        "G": 185/255,
        "B": 138/255
    }],
)
image.save("image_rgb_warm.jpg")
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 94/255,
        "G": 163/255,
        "B": 174/255
    }],
)
image.save("image_rgb_cold.jpg")
```

* 开启惰性加载和显存管理，需 24G 显存

```python
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch

vram_config = {
    "offload_dtype": "disk",
    "offload_device": "disk",
    "onload_dtype": torch.float8_e4m3fn,
    "onload_device": "cpu",
    "preparing_dtype": torch.float8_e4m3fn,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}
pipe = Flux2ImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors", **vram_config),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)
template = TemplatePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[ModelConfig(model_id="DiffSynth-Studio/Template-KleinBase4B-SoftRGB")],
    lazy_loading=True,
)
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 128/255,
        "G": 128/255,
        "B": 128/255
    }],
)
image.save("image_rgb_normal.jpg")
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 208/255,
        "G": 185/255,
        "B": 138/255
    }],
)
image.save("image_rgb_warm.jpg")
image = template(
    pipe,
    prompt="A cat is sitting on a stone.",
    seed=0, cfg_scale=4, num_inference_steps=50,
    template_inputs = [{
        "R": 94/255,
        "G": 163/255,
        "B": 174/255
    }],
)
image.save("image_rgb_cold.jpg")
```

## 训练代码

安装 DiffSynth-Studio 后，使用以下脚本可开启训练，更多信息请参考 [DiffSynth-Studio 文档](https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/)。

```shell
modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "flux2/Template-KleinBase4B-SoftRGB/*" --local_dir ./data/diffsynth_example_dataset

accelerate launch examples/flux2/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-SoftRGB \
  --dataset_metadata_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-SoftRGB/metadata.jsonl \
  --extra_inputs "template_inputs" \
  --max_pixels 1048576 \
  --dataset_repeat 50 \
  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
  --template_model_id_or_path "DiffSynth-Studio/Template-KleinBase4B-SoftRGB:" \
  --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
  --learning_rate 1e-4 \
  --num_epochs 2 \
  --remove_prefix_in_ckpt "pipe.template_model." \
  --output_path "./models/train/Template-KleinBase4B-SoftRGB_full" \
  --trainable_models "template_model" \
  --use_gradient_checkpointing \
  --find_unused_parameters
```