Update README.md

README.md

@@ -1,3 +1,87 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+  - text-to-image
+  - image-generation
+  - baai-nova
+---
+
+<div align="center">
+<h1>ReNeg: Learning Negative Embedding with Reward Guidance</h1>
+</div>
+
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv%20paper-2412.19637-b31b1b.svg)](https://arxiv.org/abs/2412.19637)&nbsp;
+[![code](https://img.shields.io/badge/%F0%9F%A4%96%20Code-AMD--AIG--AIMA/ReNeg-green)](https://github.com/AMD-AIG-AIMA/ReNeg)&nbsp;
+
+</div>
+
+
+
+We present **ReNeg**, a **Re**ward-guided approach that directly learns **Neg**ative embeddings through gradient descent. The global negative embeddings learned using **ReNeg** exhibit strong generalization capabilities and can be seamlessly adaptable to text-to-image and even text-to-video models. Strikingly simple yet highly effective, **ReNeg** amplifies the visual appeal of outputs from base Stable Diffusion models.
+
+## Examples
+
+Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run ReNeg in a simple and efficient manner.
+
+```bash
+pip install diffusers transformers accelerate
+git clone https://github.com/AMD-AIG-AIMA/ReNeg.git
+```
+
+Running ReNeg:
+
+```python
+import os
+from pathlib import Path
+import torch
+from diffusers import (
+    StableDiffusionPipeline,
+    DDIMScheduler,
+)
+from safetensors.torch import load_file
+
+model_path = "stable-diffusion-v1-5"
+neg_embeddings_path = "checkpoints/sd1.5_reneg_emb.safetensors"
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_path,
+    safety_checker=None,
+)
+pipe.scheduler = DDIMScheduler.from_pretrained(
+    model_path, subfolder="scheduler"
+)
+device = "cuda"
+pipe.to(device)
+
+neg_embeddings = load_file(neg_embeddings_path)["embedding"].to(device)  # Assuming the key is "embedding"
+output = pipe(
+    "A girl in a school uniform playing an electric guitar.",
+    negative_prompt_embeds=neg_embeddings,
+)
+
+image = output.images[0]
+# TextToImageModel is the model you want to evaluate
+image.save("output.png")
+```
+
+To compare with the inference results using `neg_emb`, you can perform inference using only positive prompt.
++ To perform **inference using only the pos_prompt**, you need to run `inference.py` with `args.prompt_type = only_pos`.
+```bash
+python inference.py --model_path "your_sd1.5_path" --prompt_type "only_pos" --prompt "A girl in a school uniform playing an electric guitar."
+```
+
+
+## Citation
+
+```
+@misc{li2024reneg,
+      title={ReNeg: Learning Negative Embedding with Reward Guidance},
+      author={Xiaomin Li, Yixuan Liu, Takashi Isobe, Xu Jia, Qinpeng Cui, Dong Zhou, Dong Li, You He, Huchuan Lu, Zhongdao Wang, Emad Barsoum},
+      year={2024},
+      eprint={2412.19637},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```