Improve model card: Add pipeline tag, library name, GitHub link, and descriptive tags

This PR enhances the model card for FLUX-Text by:
- Adding the `pipeline_tag: image-to-image` to improve discoverability for scene text editing models on the Hugging Face Hub.
- Specifying `library_name: diffusers` to enable the "Use in Diffusers" widget and provide a standard way to interact with the model within the Hugging Face ecosystem.
- Including relevant `tags` such as `text-editing`, `multilingual`, `diffusion-transformer`, and `diffusion-model` for more detailed categorization.
- Adding a direct link to the GitHub repository as a badge for easier access to the code.
- Updating the main title in the markdown content to the full paper title for better clarity.

The existing ArXiv link to the paper is preserved.

README.md

@@ -1,15 +1,20 @@
 ---
 license: mit
+pipeline_tag: image-to-image
+library_name: diffusers
+tags:
+- text-editing
+- multilingual
+- diffusion-transformer
+- diffusion-model
 ---
 
-# Implementation of FLUX-Text
-
-FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing
+# FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing
 
 <a href='https://amap-ml.github.io/FLUX-text/'><img src='https://img.shields.io/badge/Project-Page-green'></a>
 <a href='https://arxiv.org/abs/2505.03329'><img src='https://img.shields.io/badge/Technique-Report-red'></a> 
-<a href="https://huggingface.co/GD-ML/FLUX-Text/"><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a>
-<!-- <a ><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a> -->
+<a href="https://github.com/AMAP-ML/FluxText"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&"></a>
+<a href="https://huggingface.co/GD-ML/FLUX-Text/"><img src="https://img.shields.io/badge/%F0%9F%A4%97_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a>
 
 > *[Rui Lan](https://scholar.google.com/citations?user=zwVlWXwAAAAJ&hl=zh-CN), [Yancheng Bai](https://scholar.google.com/citations?hl=zh-CN&user=Ilx8WNkAAAAJ&view_op=list_works&sortby=pubdate), [Xu Duan](https://scholar.google.com/citations?hl=zh-CN&user=EEUiFbwAAAAJ), [Mingxing Li](https://scholar.google.com/citations?hl=zh-CN&user=-pfkprkAAAAJ), [Lei Sun](https://allylei.github.io), [Xiangxiang Chu](https://scholar.google.com/citations?hl=zh-CN&user=jn21pUsAAAAJ&view_op=list_works&sortby=pubdate)*
 > <br>
@@ -266,11 +271,31 @@ python app.py --model_path xx.safetensors --config_path config.yaml
 
 1. Download training dataset [**AnyWord-3M**](https://modelscope.cn/datasets/iic/AnyWord-3M/summary) from ModelScope, unzip all \*.zip files in each subfolder, then open *\*.json* and modify the `data_root` with your own path of *imgs* folder for each sub dataset.
 
-2. Download the ODM weights in [HuggingFace](https://huggingface.co/GD-ML/FLUX-Text/blob/main/epoch_100.pt).
+2. Replace the old annotations in AnyWord with the new [annotations](https://huggingface.co/GD-ML/FLUX-Text/tree/main/data_text_recog_glyph). Change the dataset annotations path and image_root in [src/train/data_word.py](https://github.com/AMAP-ML/FluxText/blob/main/src/train/data_word.py#L538).
+
+```python
+json_paths = [
+        ['dataset/Anyword/data_text_recog_glyph/Art/data-info.json', 'AnyWord-3M/ocr_data/Art/imgs/'],
+        ['dataset/Anyword/data_text_recog_glyph/COCO_Text/data-info.json', 'AnyWord-3M/ocr_data/COCO_Text/imgs/'],
+        ['dataset/Anyword/data_text_recog_glyph/icdar2017rctw/data-info.json', 'AnyWord-3M/ocr_data/icdar2017rctw/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/LSVT/data-info.json', 'AnyWord-3M/ocr_data/LSVT/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/mlt2019/data-info.json', 'AnyWord-3M/ocr_data/mlt2019/imgs/'],
+        ['dataset/Anyword/data_text_recog_glyph/MTWI2018/data-info.json', 'AnyWord-3M/ocr_data/MTWI2018/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/ReCTS/data-info.json', 'AnyWord-3M/ocr_data/ReCTS/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/laion/data_v1.1-info.json', 'AnyWord-3M/laion/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/wukong_1of5/data_v1.1-info.json', 'AnyWord-3M/wukong_1of5/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/wukong_2of5/data_v1.1-info.json', 'AnyWord-3M/wukong_2of5/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/wukong_3of5/data_v1.1-info.json', 'AnyWord-3M/wukong_3of5/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/wukong_4of5/data_v1.1-info.json', 'AnyWord-3M/wukong_4of5/imgs'],
+        ['dataset/Anyword/data_text_recog_glyph/wukong_5of5/data_v1.1-info.json', 'AnyWord-3M/wukong_5of5/imgs'],
+        ]
+```
+
+3. Download the ODM weights in [HuggingFace](https://huggingface.co/GD-ML/FLUX-Text/blob/main/epoch_100.pt) and change `odm_loss/modelpath` in the [config file](https://github.com/AMAP-ML/FluxText/blob/main/train/config/word_multi_size.yaml#L60).
 
-3. (Optional) Download the pretrained weight in [HuggingFace](https://huggingface.co/GD-ML/FLUX-Text).
+3. (Optional) Download the pretrained weight in [HuggingFace](https://huggingface.co/GD-ML/FLUX-Text) and change `reuse_lora_path` in the [config file](https://github.com/AMAP-ML/FluxText/blob/main/train/config/word_multi_size.yaml#L44).
 
-4. Run the training scripts. With 48GB of VRAM, you can train at 512×512 resolution with a batch size of 2.
+4. Run the training scripts. With 48GB of VRAM, you can train at 512×512 resolution with a batch size of 2 in LoRA rank 8.
 
 ```bash
 bash train/script/train_word.sh