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# OmniGen2
引入反思机制，多模态任务生成屠榜，一键解锁AI绘图「哆啦 A 梦」任意门。
## 论文
`OmniGen2: Exploration to Advanced Multimodal Generation`
- https://arxiv.org/pdf/2506.18871
## 模型结构
OmniGen2 利用一个基础的多模态大语言模型Transformer来处理文本与图像输入，对于文本生成任务，采用自回归语言头，而图像生成则通过专用的扩散模块完成，Transformer主干由Qwen2.5-VL-3B初始化。
<div align=center>
<img src="./doc/OmniGen2.png"/>
</div>
## 算法原理
MLLM在训练过程中其大部分参数保持冻结状态，以保留其多模态理解能力，仅新引入的特殊 token “<|img|>” 被更新，扩散模型从零开始训练，初期专注于文本到图像（T2I）生成任务，随后采用混合任务训练策略以适应多种目标，在反思训练阶段，所有模型参数解冻，允许模型生成反思性的文本描述并迭代优化图像输出。
<div align=center>
<img src="./doc/Reflection.png"/>
</div>
## 环境配置
```
mv OmniGen2_pytorch OmniGen2
```
### 硬件需求
DCU型号：K100AI，节点数量：1 台，卡数：1 张。
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04.1-py3.10
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：e50d644287fd
docker run -it --shm-size=64G -v $PWD/OmniGen2:/home/OmniGen2 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name og2 <your IMAGE ID> bash
cd /home/OmniGen2
pip install -r requirements.txt # requirements.txt
```
### Dockerfile（方法二）
```
cd /home/OmniGen2/docker
docker build --no-cache -t og2:latest .
docker run --shm-size=64G --name yolov13 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../OmniGen2:/home/OmniGen2 -it og2 bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.sourcefind.cn/tool/
```
DTK驱动:25.04.1
python:python3.10
torch:2.4.1
torchvision:0.19.1
triton:3.0.0
flash-attn:2.6.1
deepspeed:0.14.2
apex:1.4.0
onnxruntime:1.19.2
```
不同深度学习库可支持的DCU型号可在此处查询：[DAS资源下载](https://das.sourcefind.cn:55011/portal/#/home)
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
cd /home/OmniGen2
pip install -r requirements.txt # requirements.txt
```
## 数据集
`无`
## 训练
`无`
## 推理
预训练权重目录结构：
```
/home/OmniGen2/
└── OmniGen2/OmniGen2
```
### 单机单卡
```
export HIP_VISIBLE_DEVICES=0
cd /home/OmniGen2
# Visual Understanding
bash example_understanding.sh # 其它功能的推理脚本见run_example.sh
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)
## result
`输入: `
```
instruction: "Please describe this image briefly."
input_image_path: example_images/02.jpg
```
<div align=center>
<img src="./doc/02.png"/>
</div>
`输出:`
```
Text: The image shows a plush toy bear sitting on a grassy surface. The bear has a brown body with white paws and a white muzzle. It is wearing a blue bow on its head and a white bib with the text "Get Well" written on it. The background consists of green grass with some clover leaves visible.
```
官方其它演示效果示例：
<div align=center>
<img src="./doc/replace.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
`多模态`
### 热点应用行业
`制造,广媒,金融,能源,医疗,家居,教育`
## 预训练权重
HF下载地址为：[OmniGen2/OmniGen2](https://huggingface.co/OmniGen2/OmniGen2)
## 源码仓库及问题反馈
- http://developer.sourcefind.cn/codes/modelzoo/OmniGen2_pytorch.git
## 参考资料
- https://github.com/VectorSpaceLab/OmniGen2.git
README_origin.md 0 → 100644
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<p align="center">
<img src="assets/brand.png" width="65%">
</p>
<p align="center">
<a href="https://vectorspacelab.github.io/OmniGen2"><img src="https://img.shields.io/badge/Project%20Page-OmniGen2-yellow" alt="project page"></a>
<a href="https://arxiv.org/abs/2506.18871"><img src="https://img.shields.io/badge/arXiv%20paper-2506.18871-b31b1b.svg" alt="arxiv"></a>
<a href="https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo"><img src="https://img.shields.io/badge/Online%20Demo-🤗-blue" alt="demo"></a>
<a href="https://huggingface.co/spaces/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/HF%20Spaces-🤗-lightblue" alt="demo"></a>
<a href="https://huggingface.co/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/Model-🤗-yellow" alt="model"></a>
<a href="https://huggingface.co/datasets/OmniGen2/OmniContext"><img src="https://img.shields.io/badge/Benchmark-🤗-yellow" alt="model"></a>
<a href="https://huggingface.co/datasets/OmniGen2/X2I2"><img src="https://img.shields.io/badge/Dataset-🤗-yellow" alt="model"></a>
</p>
<h4 align="center">
<p>
<a href=#-news>News</a> |
<a href=#-quick-start>Quick Start</a> |
<a href=#-usage-tips>Usage Tips</a> |
<a href=#-limitations-and-suggestions>Limitations</a> |
<a href=#-gradio-demo>Online Demos</a> |
<a href=#%EF%B8%8F-citing-us>Citation</a>
<p>
</h4>
## 🔥 News
- **2025-07-05**: Training datasets [X2I2](https://huggingface.co/datasets/OmniGen2/X2I2) are available.
- **2025-07-03**: OmniGen2 now supports [TeaCache](https://github.com/ali-vilab/TeaCache) and [TaylorSeer](https://github.com/Shenyi-Z/TaylorSeer) for faster inference, see [Usage Tips](#-usage-tips) for details. Thanks @legitnull for great [TeaCache-PR](https://github.com/VectorSpaceLab/OmniGen2/pull/52) and [TaylorSeer-PR](https://github.com/VectorSpaceLab/OmniGen2/pull/76).
- **2025-07-01**: OmniGen2 is supported by [ComfyUI official](https://comfyanonymous.github.io/ComfyUI_examples/omnigen), thanks !!
- **2025-06-30**: Training code is available, see [fine-tuning](docs/FINETUNE.md) for details.
- **2025-06-28**: We release [OmniContext](https://huggingface.co/datasets/OmniGen2/OmniContext) benchmark. The evaluation codes are in [omnicontext](https://github.com/VectorSpaceLab/OmniGen2/tree/main/omnicontext).
- **2025-06-24**: [Technical Report](https://arxiv.org/abs/2506.18871) is available.
- **2025-06-23**: We’ve updated our code and HF model—OmniGen2 now runs *without* `flash-attn`. Users can still install it for optimal performance.
- **2025-06-20**: Updated [resource requirements](#-resources-requirement), adding CPU offload support for devices with limited VRAM.
- **2025-06-16**: [Gradio](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) and [Jupyter](https://github.com/VectorSpaceLab/OmniGen2/blob/main/example.ipynb) is available. Online Gradio Demo: [Demo1](https://9c4426d27c3b9ecbed.gradio.live); [Chat-Demo1](https://0351497834a4d7226c.gradio.live); see more demo links in [gradio section](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo)
- **2025-06-16**: We release **OmniGen2**, a multimodal generation model, model weights can be accessed in [huggingface](https://huggingface.co/OmniGen2/OmniGen2) and [modelscope](https://www.modelscope.cn/models/OmniGen2/OmniGen2).
## Introduction
**OmniGen2** is a powerful and efficient generative model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. OmniGen2 has competitive performance across four primary capabilities:
- **Visual Understanding**: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation.
- **Text-to-Image Generation**: Creates high-fidelity and aesthetically pleasing images from textual prompts.
- **Instruction-guided Image Editing**: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models.
- **In-context Generation**: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs.
**We will release the training code and dataset. Stay tuned!**
Some good cases of OmniGen2:
<p align="center">
<img src="assets/teaser.jpg" width="95%">
<br>
<em>Demonstrations.</em>
</p>
<p align="center">
<img src="assets/examples_edit.png" width="95%">
<br>
<em> Good demonstrations of OmniGen2's image editing capabilities.</em>
</p>
<p align="center">
<img src="assets/examples_subject.png" width="95%">
<br>
<em> Good demonstrations of OmniGen2's in-context generation capabilities.</em>
</p>
## 📌 TODO
- [x] Technical report.
- [x] Support CPU offload and improve inference efficiency.
- [x] In-context generation benchmark: **OmniContext**.
- [ ] Integration of diffusers.
- [x] Training datasets.
- [ ] Training data construction pipeline.
- [ ] ComfyUI Demo (**commuity support will be greatly appreciated!**).
## 🚀 Quick Start
### 🛠️ Environment Setup
#### ✅ Recommended Setup
```bash
# 1. Clone the repo
git clone git@github.com:VectorSpaceLab/OmniGen2.git
cd OmniGen2
# 2. (Optional) Create a clean Python environment
conda create -n omnigen2 python=3.11
conda activate omnigen2
# 3. Install dependencies
# 3.1 Install PyTorch (choose correct CUDA version)
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124
# 3.2 Install other required packages
pip install -r requirements.txt
# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation
```
#### 🌏 For users in Mainland China
```bash
# Install PyTorch from a domestic mirror
pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124
# Install other dependencies from Tsinghua mirror
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple
```
---
### 🧪 Run Examples
```bash
# Visual Understanding
bash example_understanding.sh
# Text-to-image generation
bash example_t2i.sh
# Instruction-guided image editing
bash example_edit.sh
# In-context generation
bash example_in_context_generation.sh
```
---
### 🌐 Gradio Demo
* **Online Demo**: [HF Spaces](https://huggingface.co/spaces/OmniGen2/OmniGen2). Beyond Hugging Face Spaces, we are *temporarily* allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links:
[Demo1](https://9c4426d27c3b9ecbed.gradio.live), [Demo2](https://06574c5e62d815f799.gradio.live), [Demo3](https://e0a82fd380d2ff17ac.gradio.live), [Demo4](https://d9c4410ee48ce35051.gradio.live)
[Chat-Demo1](https://0351497834a4d7226c.gradio.live), [Chat-Demo2](https://032160099388d1d10c.gradio.live), [Chat-Demo3](https://cf9f2797e92cfa2767.gradio.live), [Chat-Demo4](https://b87b82fd14215affc2.gradio.live)
<!-- [Available on Hugging Face Spaces 🚀](https://huggingface.co/spaces/Shitao/OmniGen2) -->
* **Run Locally**:
```bash
# for only generating image
pip install gradio
python app.py
# Optional: Share demo with public link (You need to be able to access huggingface)
python app.py --share
# for generating image or text
pip install gradio
python app_chat.py
```
## 💡 Usage Tips
To achieve optimal results with OmniGen2, you can adjust the following key hyperparameters based on your specific use case.
- `text_guidance_scale`: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance).
- `image_guidance_scale`: This controls how much the final image should resemble the input reference image.
- **The Trade-off**: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence.
- **Tip**: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0.
- `max_pixels`: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage.
- **Tip**: Default value is 1024*1024. You can reduce this value if you encounter memory issues.
- `max_input_image_side_length`: Maximum side length for input images.
- `negative_prompt`: Tell the model what you don't want to see in the image.
- **Example**: blurry, low quality, text, watermark
- **Tip**: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt.
- `enable_model_cpu_offload`: **Reduces VRAM usage by nearly 50% with a negligible impact on speed**.
- This is achieved by offloading the model weights to CPU RAM when they are not in use.
- See: [Model Offloading](https://huggingface.co/docs/diffusers/optimization/memory#model-offloading)
- `enable_sequential_cpu_offload`: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance.
- This works by offloading the model in submodules and loading them onto the GPU sequentially as needed.
- See: [CPU Offloading](https://huggingface.co/docs/diffusers/optimization/memory#cpu-offloading)
- `cfg_range_start`, `cfg_range_end`: Define the timestep range where CFG is applied. Per this [paper](https://arxiv.org/abs/2404.07724), reducing `cfg_range_end` can significantly decrease inference time with a negligible impact on quality.
- `scheduler`: Choose between `[euler, dpmsolver++]`. Default is `euler`. For potentially better performance with fewer steps, try `dpmsolver++`.
- `num_inference_step`: Number of discretization steps for the ODE solver. Default is `50`.
- `enable_teacache`: Whether or not enable [teacache](https://github.com/ali-vilab/TeaCache) for faster inference.
- `teacache_rel_l1_thresh`: The threshold for accumulated L1 distance for the timestep embedding-modulated noisy input. It serves as an indicator of whether to cache the model output. You can modify the `teacache_rel_l1_thresh` parameter to achieve your desired trade-off between latency and visual quality. The default value of 0.05 provides approximately a **30% speedup** compared to the baseline. Increasing this value can further reduce latency, but may result in some loss of detail.
- `enable_taylorseer`: Whether or not enable [taylorseer](https://github.com/Shenyi-Z/TaylorSeer) for faster inference. When enabled, inference speed can improve by up to **2X**, with negligible quality loss compared to the baseline.
**Some suggestions for improving generation quality:**
1. Use High-Quality Images
- Provide clear images, preferably with a resolution **greater than 512×512 pixels**.
- Small or blurry inputs will result in low-quality outputs.
2. Be Specific with Instructions
- Clearly describe both **what to change** and **how you want it changed**.
3. Prioritize English
The model currently performs best with **English** prompts.
4. Change instructions to enhance subject consistency.
When the generated image does not align well with the input image, you can try the following methods to improve subject consistency:
- **Use images with larger size, as well as images in which people occupy a larger proportion of the frame.**
- **Increase the Image Guidance Scale**, for example to 3.0. The trade-off may be slight overexposure or a greasy look in the image.
- **When using a single input image**, you can try to use the following prompt template: "she/he ..., maintaining her/his facial features, hairstyle, and other attributes."
- **Increase the parameter--Number of images per prompt** to generate more outputs, giving you a better chance to find one with stronger subject consistency and a more satisfactory result.
- **Longer prompts generally yield better results than shorter ones.** More detailed descriptions of the scene and character interactions can provide additional benefits.
5. For in-context edit (edit based multiple images), we recommend using the following prompt format: "Edit the first image: add/replace (the [object] with) the [object] from the second image. [descripton for your target image]."
For example: "Edit the first image: add the man from the second image. The man is talking with a woman in the kitchen". The descition for your target image should be as detailed as possible.
## 🎨 Fine-tune
See [fine-tuning](docs/FINETUNE.md) for details.
## ❌ Limitations and Suggestions
The current model sometimes does not follow instructions. You can increase the "Number of images per prompt" to generate multiple images at once, so you can choose the result you are satisfied with, or try different prompts. In our own experience, being as detailed as possible tends to work better.
The current model cannot decide the output image size by itself; the default size is 1024×1024. You need to set a specific size if you require a different one. When you input an image, we will set the output size to match the input image (this works best for editing tasks). If you want to modify just one image out of several, you should also set the output size to match the image you want to edit; otherwise, it may lead to low-quality outputs.
The in-context generation capability sometimes produces objects that differ from the original ones. Some suggested improvements are: increasing `image_guidance_scale` (it is recommended to set it to 3) can help alleviate this issue; using high-resolution images, increasing the size of the input image, and ensuring that the object to be used occupies a larger proportion of the image; and modifying the prompt. However, there is still a gap compared to GPT-4o.
Compared to OmniGen 1.0, although OmniGen 2 has made some improvements, many issues still remain. It may take multiple attempts to achieve a satisfactory result.
## 💻 Resources Requirement
OmniGen2 natively requires an **NVIDIA RTX 3090** or an equivalent GPU with approximately **17GB of VRAM**. For devices with less VRAM, you can enable **CPU Offload** to run the model.
**Performance Tip**: To improve inference speed, consider decreasing the `cfg_range_end` parameter. Within a reasonable range, this has a negligible impact on output quality.
The following table details the inference performance of OmniGen2 on an **A800 GPU**:
<p align="center">
<img src="assets/efficiency.png" width="95%">
<br>
<em>Inference Efficiency of OmniGen2.</em>
</p>
## 🤝 Community Efforts
We’re honored and grateful for the support from the open source community. Here are some unofficial implementations contributed by the community(**Currently, we have not confirmed whether there are no bugs. Please try to use the our official demo as much as possible.**):
- ComfyUI:
- [ComfyUI Official](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
- [https://github.com/Yuan-ManX/ComfyUI-OmniGen2](https://github.com/Yuan-ManX/ComfyUI-OmniGen2)
- [https://github.com/neverbiasu/ComfyUI-OmniGen2](https://github.com/neverbiasu/ComfyUI-OmniGen2)
- Quantization:
- [DFloat11, a lossless compression using 11 bits](https://github.com/LeanModels/OmniGen2-DFloat11)
## ❤️ Citing Us
If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated:
```bibtex
@article{wu2025omnigen2,
title={OmniGen2: Exploration to Advanced Multimodal Generation},
author={Chenyuan Wu and Pengfei Zheng and Ruiran Yan and Shitao Xiao and Xin Luo and Yueze Wang and Wanli Li and Xiyan Jiang and Yexin Liu and Junjie Zhou and Ze Liu and Ziyi Xia and Chaofan Li and Haoge Deng and Jiahao Wang and Kun Luo and Bo Zhang and Defu Lian and Xinlong Wang and Zhongyuan Wang and Tiejun Huang and Zheng Liu},
journal={arXiv preprint arXiv:2506.18871},
year={2025}
}
```
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app_chat.py 0 → 100644
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# !/bin/bash
SHELL_FOLDER=$(cd "$(dirname "$0")";pwd)
cd $SHELL_FOLDER
source "$(dirname $(which conda))/../etc/profile.d/conda.sh"
conda activate py3.11+pytorch2.6+cu124
RANK=0
# 处理命名参数
while [[ $# -gt 0 ]]; do
case "$1" in
--rank=*)
RANK="${1#*=}"
shift
;;
*)
echo "未知参数: $1"
shift
;;
esac
done
CUDA_VISIBLE_DEVICES=${RANK} python app_chat.py \
--port $((7860 + RANK)) \
--share
\ No newline at end of file
convert_ckpt_to_hf_format.py 0 → 100644
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import dotenv
dotenv.load_dotenv(override=True)
import argparse
from omegaconf import OmegaConf
import torch
from accelerate import init_empty_weights
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict
from omnigen2.models.transformers.transformer_omnigen2 import OmniGen2Transformer2DModel
from omnigen2.pipelines.omnigen2.pipeline_omnigen2 import OmniGen2Pipeline
def main(args):
config_path = args.config_path
model_path = args.model_path
conf = OmegaConf.load(config_path)
arch_opt = conf.model.arch_opt
arch_opt = OmegaConf.to_object(arch_opt)
# Convert lists to tuples in conf.model.arch_opt
for key, value in arch_opt.items():
if isinstance(value, list):
arch_opt[key] = tuple(value)
with init_empty_weights():
transformer = OmniGen2Transformer2DModel(**arch_opt)
if conf.train.get('lora_ft', False):
target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
# now we will add new LoRA weights the transformer layers
lora_config = LoraConfig(
r=conf.train.lora_rank,
lora_alpha=conf.train.lora_rank,
lora_dropout=conf.train.lora_dropout,
init_lora_weights="gaussian",
target_modules=target_modules,
)
transformer.add_adapter(lora_config)
state_dict = torch.load(model_path, mmap=True, weights_only=True)
missing, unexpect = transformer.load_state_dict(
state_dict, assign=True, strict=False
)
print(f"missed parameters: {missing}")
print(f"unexpected parameters: {unexpect}")
save_path = args.save_path
if conf.train.get('lora_ft', False):
transformer_lora_layers = get_peft_model_state_dict(transformer)
OmniGen2Pipeline.save_lora_weights(
save_directory=save_path,
transformer_lora_layers=transformer_lora_layers,
)
else:
transformer.save_pretrained(save_path)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--model_path", type=str, required=True)
parser.add_argument("--save_path", type=str, required=True)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
main(args)
data_configs/train/example/edit/edit.yml 0 → 100644
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data:
-
path: "data_configs/example/edit/jsonls/0.jsonl"
type: "edit"
ratio: !!float 1
-
path: "data_configs/example/edit/jsonls/1.jsonl"
type: "edit"
ratio: !!float 1
\ No newline at end of file
data_configs/train/example/edit/jsonls/0.jsonl 0 → 100644
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{"task_type": "edit", "instruction": "add a hat to the person", "input_images": ["/path/to/your/data/edit/0.png"], "output_image": "/path/to/your/data/edit/0.png"}
{"task_type": "edit", "instruction": "add a dog behind the person", "input_images": ["/path/to/your/data/edit/1.png"], "output_image": "/path/to/your/data/edit/1.png"}
\ No newline at end of file
data_configs/train/example/ic/ic.yml 0 → 100644
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data:
-
path: "data_configs/example/ic/jsonls/0.jsonl"
type: "ic"
ratio: !!float 1
-
path: "data_configs/example/ic/jsonls/1.jsonl"
type: "ic"
ratio: !!float 1
\ No newline at end of file
data_configs/train/example/ic/jsonls/0.jsonl 0 → 100644
View file @ 109f0842
{"task_type": "ic", "instruction": "A big tree is in the forest", "input_images": ["/path/to/your/data/ic/0.png", "/path/to/your/data/ic/1.png"], "output_image": "/path/to/your/data/ic/0.png"}
{"task_type": "ic", "instruction": "a dog is running on grass", "input_images": ["/path/to/your/data/ic/2.png", "/path/to/your/data/ic/3.png"], "output_image": "/path/to/your/data/ic/1.png"}
\ No newline at end of file
data_configs/train/example/mix.yml 0 → 100644
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data:
-
path: 'data_configs/example/t2i/t2i.yml'
type: 't2i'
ratio: !!float 0.33
-
path: 'data_configs/example/edit/edit.yml'
type: 'edit'
ratio: !!float 0.33
-
path: 'data_configs/example/ic/ic.yml'
type: 'ic'
ratio: !!float 0.33
\ No newline at end of file
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