Update README for MaPO Pick-Safety checkpoint (#1)

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- Update README for MaPO Pick-Safety checkpoint (f8c30f3ded7799aa8945cca3aed5c833605dafbd)
- Add disclaimer for unsafe instances (eeee8d0973b0f70073aeb750d6520ae40c5173db)

Co-authored-by: Jiwoo Hong <JW17@users.noreply.huggingface.co>

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 ---
 library_name: diffusers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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 ---
+license: openrail++
 library_name: diffusers
+tags:
+- text-to-image
+- text-to-image
+- diffusers-training
+- diffusers
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
 ---
+# Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
+<div align="center">
+<img src="assets/mapo_overview.png" width=750/>
+</div><br>
+We propose **MaPO**, a reference-free, sample-efficient, memory-friendly alignment technique for text-to-image diffusion models. For more details on the technique, please refer to our paper [here] (TODO).
+## Developed by
+* Jiwoo Hong<sup>*</sup> (KAIST AI)
+* Sayak Paul<sup>*</sup> (Hugging Face)
+* Noah Lee (KAIST AI)
+* Kashif Rasul (Hugging Face)
+* James Thorne (KAIST AI)
+* Jongheon Jeong (Korea University)
+## Dataset
+This model was fine-tuned from [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) on the [Pick-Safety](mapo-t2i/pick-safety). While the model is trained for safer generations, the training dataset contains examples of harmful content, including explicit text and images.
+## Training Code
+Refer to our code repository [here](https://github.com/mapo-t2i/mapo).
+## Inference
+```python
+from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel
+import torch
+sdxl_id = "stabilityai/stable-diffusion-xl-base-1.0"
+vae_id = "madebyollin/sdxl-vae-fp16-fix"
+unet_id = "mapo-t2i/mapo-pick-safety"
+vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16)
+unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder='unet', torch_dtype=torch.float16)
+pipeline = DiffusionPipeline.from_pretrained(sdxl_id, vae=vae, unet=unet, torch_dtype=torch.float16).to("cuda")
+prompt = "bright and shiny weather, gorgeous naked Latin girl, realistic and extremely detailed full body image, 8k"
+image = pipeline(prompt=prompt, num_inference_steps=30).images[0]
+```
+For qualitative results, please visit our [project website] (TODO).
+## Citation
+```bibtex
+@misc{todo,
+    title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference},
+    author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasuland James Thorne and Jongheon Jeong},
+    year={2024},
+    eprint={todo},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV,cs.LG}
+}
+```