Update README.md

README.md

@@ -18,256 +18,10 @@ _**[Harold Haodong Chen](https://haroldchen19.github.io/)<sup>1,2*</sup>, [Xinxi
 <h5 align="center"> If you like our project, please give us a star ⭐ on huggingface for latest update.  </h2>
 
  <a href='https://arxiv.org/abs/2602.02227'><img src='https://img.shields.io/badge/arXiv-2602.02227-b31b1b.svg'></a>
+<a href='https://github.com/EnVision-Research/LatentMorph'><img src='https://img.shields.io/badge/GitHub-LatentMorph-181717.svg'></a>
 <br>
 
 </div>
 
 ![latentmorph](https://cdn-uploads.huggingface.co/production/uploads/69748068683fa304ce0f7368/HuNmWtBO2-eu8kkMHDONj.png)
 
-
-<!-- <table class="center">
-    <tr>
-    <td><img src="assets/latentmorph.png"></td>
-    </tr>
-</table> -->
-
-
-
-<!-- ## 🧰 TODO
-
-- [x] Release training code.
-- [x] Release inference code.
-- [ ] Release Paper.
-- [ ] Release model weights.
-
---- -->
-
-
-
-<a name="installation"></a>
-
-## 🚀 Installation
-
-### 1. Clone this repository and navigate to source folder
-
-```bash
-cd LatentMorph
-```
-
-### 2. Build Environment 
-
-This repo ships `environment.yml`.
-
-```bash
-conda env create -f environment.yml
-conda activate ./envs/latentmorph
-```
-
-If you don't use conda, make sure you can run:
-
-```bash
-python -c "import torch; import transformers; print(torch.__version__)"
-```
-
----
-
-
-
-<a name="data&model"></a>
-
-## 🌏 Data & Model
-
-This repo does not ship training datasets under `data/`. Please download them locally via Hugging Face.
-
-### 1. Create the local data layout
-
-```bash
-mkdir -p data/.cache/huggingface data/.cache/torch data/hps_ckpt outputs_sft/checkpoints_control outputs/rl_result
-```
-
-### 2. Download model weights into the local cache
-
-We store Hugging Face cache inside the repo:
-
-```bash
-export HF_HOME="$(pwd)/data/.cache/huggingface"
-export TORCH_HOME="$(pwd)/data/.cache/torch"
-python -m pip install huggingface_hub
-```
-
-Download Janus and CLIP:
-
-```bash
-python -m huggingface_hub.cli download deepseek-ai/Janus-Pro-7B --local-dir "$HF_HOME"
-python -m huggingface_hub.cli download openai/clip-vit-large-patch14 --local-dir "$HF_HOME"
-```
-
-Download HPS v2.1 reward weights:
-
-```bash
-bash scripts/download_required_assets.sh
-python -m pip install "git+https://github.com/tgxs002/HPSv2.git"
-```
-
-### 3. Datasets / prompts (download from Hugging Face)
-
-We expect the following local layout:
-
-- **SFT dataset**: `data/midjourney-prompts/data/*.zstd.parquet`
-- **RL prompts**: `data/T2I-CompBench/examples/dataset/*.txt`
-
-Download with Hugging Face (replace the repo ids):
-
-```bash
-# Midjourney prompts (parquet shards) -> data/midjourney-prompts/data/*.zstd.parquet
-huggingface-cli download --repo-type dataset vivym/midjourney-prompts \
-  --local-dir data/midjourney-prompts --resume-download
-
-# T2I-CompBench prompts (.txt) -> data/T2I-CompBench/examples/dataset/*.txt
-huggingface-cli download --repo-type dataset NinaKarine/t2i-compbench \
-  --include "examples/dataset/*.txt" \
-  --local-dir data/T2I-CompBench --resume-download
-```
-
-Quick sanity checks:
-
-```bash
-ls -lh data/midjourney-prompts/data | head
-ls -lh data/T2I-CompBench/examples/dataset | head
-```
-
----
-
-
-
-<a name="inference_Suite"></a>
-
-## 📍 Inference Suite
-
-LatentMorph has two Inference part provided : 
-
-- **SFT Inference Part (`inference_sft`)**  
-
-- **RL Inference  Part (`inference_rl`)**
-
-Before running inference, ensure you have activated the environment:
-
-```bash
-conda activate latentmorph
-```
-
-### 1. Prepare Model Weights
-
-You can download our pre-trained checkpoints from [Hugging Face](https://huggingface.co/CheeseStar/LatentMorph):
-
-
-| Weight Type                | Filename           | Download Command                                             |
-| -------------------------- | ------------------ | ------------------------------------------------------------ |
-| **SFT Controller**         | `ckpt_sft.pt`      | `huggingface-cli download CheeseStar/LatentMorph sft.pt --local-dir .` |
-| **RL Policy**              | `ckpt_rl.pt`       | `huggingface-cli download CheeseStar/LatentMorph rl.pt --local-dir .` |
-| **SFT Controller w/ LoRA** | `ckpt_sft_LoRA.pt` | (User Trained)                                               |
-| **RL Policy w/ LoRA**      | `ckpt_rl_LoRA.pt`  | (User Trained)                                               |
-
----
-
-
-### 2. Run Inference
-
-We provide two modes for both **SFT** and **RL** stages. Choose the corresponding script folder (`inference_sft` or `inference_rl`).
-
-#### **Option A: Single Prompt (Quick Test)**
-
-Generate an image from a specific text prompt.
-
-```bash
-# Example for SFT
-bash inference_sft/run_infer_one.bash
-```
-
-> **Customization:** Open `run_infer_one.bash` to modify the `prompt` string and `output` path.
-> **Result:** View your image at `inference_[sft/rl]_out/single.png`.
-
-#### **Option B: Batch Processing (Group of Prompts)**
-
-Generate multiple images using a `.txt` file (one prompt per line).
-
-```bash
-# Example for RL
-bash inference_rl/run_infer.bash
-```
-
-> **Setup:** Ensure your `prompts_file` path in the bash script points to your text file.
-> **Result:** All generated images will be saved in `inference_[sft/rl]_out/batch/`.
-
----
-
-
-<a name="training_suite"></a>
-
-## ▶️ Training Suite
-
-LatentMorph has two training stages:
-
-- **SFT (`latent_sft`)**: train lightweight control modules (controller) with teacher-forcing while freezing the large Janus model.
-- **RL (`latent_rl`)**: train a trigger policy + condenser with CLIP/HPS rewards (the rest of Janus/control stack stays frozen).
-
-
-### SFT: train controller (teacher-forcing)
-
-```bash
-bash sft_train.sh
-```
-
-> You can control the training depth using the `--lora_control` flag in the training script:
->
-> * `--lora_control 0`: Trains **only** the control modules (Backbone remains frozen).
-> * `--lora_control 1`: Fine-tunes the **Backbone** and control modules together via LoRA.
-
-
-**Outputs:**
-
-- `outputs_sft/checkpoints_control/ckpt_latest.pt`
-- `outputs_sft/checkpoints_control/ckpt_step_*.pt`
-
-### RL: train trigger policy (policy gradient)
-
-Ensure your SFT checkpoint exists at `outputs_sft/checkpoints_control/ckpt_latest.pt`.
-
-```bash
-bash rl_train.sh
-```
-
-**Outputs:**
-
-- `outputs/rl_result/ckpt_latest.pt`
-- `outputs/rl_result/ckpt_step_*.pt`
-- `outputs/rl_result/logs/`
-
----
-
-<a name="citation"></a>
-
-## 📝 Citation
-
-Please consider citing our paper if you find LatentMorph is useful:
-
-```bib
-@article{chen2026show,
-  title={Show, Don't Tell: Morphing Latent Reasoning into Image Generation},
-  author={Chen, Harold Haodong and Yin, Xinxiang and Shu, Wen-Jie and Zhang, Hongfei and Zhang, Zixin and Liao, Chenfei and Guo, Litao and Chen, Qifeng and Chen, Ying-Cong},
-  journal={arXiv preprint arXiv:2602.02227},
-  year={2026}
-}
-```
-
----
-
-## 🍗 Acknowledgement
-
-Our LatentMorph is developed based on the codebases of [Janus-Pro](https://github.com/deepseek-ai/Janus), [Janus-Pro-R1](https://github.com/wendell0218/Janus-Pro-R1) and [DanceGRPO](https://github.com/XueZeyue/DanceGRPO), and we would like to thank the developers of them.
-
----
-
-## 📪 Contact
-
-For any question, feel free to open an issue or email `haroldchen328@gmail.com`.