diff --git a/examples/research_projects/diffusion_orpo/README.md b/examples/research_projects/diffusion_orpo/README.md
index aab70b1b8e..3f1ee0413e 100644
--- a/examples/research_projects/diffusion_orpo/README.md
+++ b/examples/research_projects/diffusion_orpo/README.md
@@ -1,121 +1 @@
-This project is an attempt to check if it's possible to apply to [ORPO](https://arxiv.org/abs/2403.07691) on a text-conditioned diffusion model to align it on preference data WITHOUT a reference model. The implementation is based on https://github.com/huggingface/trl/pull/1435/. 
-
-> [!WARNING] 
-> We assume that MSE in the diffusion formulation approximates the log-probs as required by ORPO (hat-tip to [@kashif](https://github.com/kashif) for the idea). So, please consider this to be extremely experimental.
-
-## Training
-
-Here's training command you can use on a 40GB A100 to validate things on a [small preference
-dataset](https://hf.co/datasets/kashif/pickascore): 
-
-```bash
-accelerate launch train_diffusion_orpo_sdxl_lora.py \
-  --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0  \
-  --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
-  --output_dir="diffusion-sdxl-orpo" \
-  --mixed_precision="fp16" \
-  --dataset_name=kashif/pickascore \
-  --train_batch_size=8 \
-  --gradient_accumulation_steps=2 \
-  --gradient_checkpointing \
-  --use_8bit_adam \
-  --rank=8 \
-  --learning_rate=1e-5 \
-  --report_to="wandb" \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=2000 \
-  --checkpointing_steps=500 \
-  --run_validation --validation_steps=50 \
-  --seed="0" \
-  --report_to="wandb" \
-  --push_to_hub
-```
-
-We also provide a simple script to scale up the training on the [yuvalkirstain/pickapic_v2](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2) dataset:
-
-```bash
-accelerate launch --multi_gpu train_diffusion_orpo_sdxl_lora_wds.py \
-  --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0  \
-  --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
-  --dataset_path="pipe:aws s3 cp s3://diffusion-preference-opt/{00000..00644}.tar -" \
-  --output_dir="diffusion-sdxl-orpo-wds" \
-  --mixed_precision="fp16" \
-  --gradient_accumulation_steps=1 \
-  --gradient_checkpointing \
-  --use_8bit_adam \
-  --rank=8 \
-  --dataloader_num_workers=8 \
-  --learning_rate=3e-5 \
-  --report_to="wandb" \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=50000 \
-  --checkpointing_steps=2000 \
-  --run_validation --validation_steps=500 \
-  --seed="0" \
-  --report_to="wandb" \
-  --push_to_hub
-```
-
-We tested the above on a node of 8 H100s but it should also work on A100s. It requires the `webdataset` library for faster dataloading. Note that we kept the dataset shards on an S3 bucket but it should be also possible to have them stored locally. 
-
-You can use the code below to convert the original dataset into `webdataset` shards:
-
-```python
-import os
-import io
-import ray
-import webdataset as wds
-from datasets import Dataset
-from PIL import Image
-
-ray.init(num_cpus=8)
-
-
-def convert_to_image(im_bytes):
-    return Image.open(io.BytesIO(im_bytes)).convert("RGB")
-
-def main():
-    dataset_path = "/pickapic_v2/data"
-    wds_shards_path = "/pickapic_v2_webdataset"
-    # get all .parquet files in the dataset path
-    dataset_files = [
-        os.path.join(dataset_path, f)
-        for f in os.listdir(dataset_path)
-        if f.endswith(".parquet")
-    ]
-
-    @ray.remote
-    def create_shard(path):
-        # get basename of the file
-        basename = os.path.basename(path)
-        # get the shard number data-00123-of-01034.parquet -> 00123
-        shard_num = basename.split("-")[1]
-        dataset = Dataset.from_parquet(path)
-        # create a webdataset shard
-        shard = wds.TarWriter(os.path.join(wds_shards_path, f"{shard_num}.tar"))
-        
-        for i, example in enumerate(dataset):
-            wds_example = {
-                "__key__": str(i),
-                "original_prompt.txt": example["caption"],
-                "jpg_0.jpg": convert_to_image(example["jpg_0"]),
-                "jpg_1.jpg": convert_to_image(example["jpg_1"]),
-                "label_0.txt": str(example["label_0"]),
-                "label_1.txt": str(example["label_1"])
-            }
-            shard.write(wds_example)
-        shard.close()
-
-    futures = [create_shard.remote(path) for path in dataset_files]
-    ray.get(futures)
-
-
-if __name__ == "__main__":
-    main()
-```
-
-## Inference
-
-Refer to [sayakpaul/diffusion-sdxl-orpo](https://huggingface.co/sayakpaul/diffusion-sdxl-orpo) for an experimental checkpoint.
\ No newline at end of file
+This project has a new home now: [https://mapo-t2i.github.io/](https://mapo-t2i.github.io/). We formally studied the use of ORPO in the context of diffusion models and open-sourced our codebase, models, and datasets. We released our paper too!