Update README for pixel art MaPO checkpoint
Browse filesThis is the model for pixel art split of Pick-Style!
@sayakpaul
README.md
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library_name: diffusers
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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license: openrail++
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library_name: diffusers
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tags:
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- text-to-image
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- text-to-image
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- diffusers-training
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- diffusers
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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# Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
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<div align="center">
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<img src="assets/mapo_overview.png" width=750/>
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</div><br>
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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).
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## Developed by
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* Jiwoo Hong<sup>*</sup> (KAIST AI)
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* Sayak Paul<sup>*</sup> (Hugging Face)
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* Noah Lee (KAIST AI)
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* Kashif Rasul (Hugging Face)
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* James Thorne (KAIST AI)
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* Jongheon Jeong (Korea University)
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## Dataset
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This model was fine-tuned from [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) on the [pixel art split of Pick-Style](https://huggingface.co/datasets/mapo-t2i/pick-style-pixel-art).
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## Training Code
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Refer to our code repository [here](https://github.com/mapo-t2i/mapo).
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## Inference
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```python
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from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel
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import torch
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sdxl_id = "stabilityai/stable-diffusion-xl-base-1.0"
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vae_id = "madebyollin/sdxl-vae-fp16-fix"
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unet_id = "mapo-t2i/mapo-pick-style-pixel-art"
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vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16)
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unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder='unet', torch_dtype=torch.float16)
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pipeline = DiffusionPipeline.from_pretrained(sdxl_id, vae=vae, unet=unet, torch_dtype=torch.float16).to("cuda")
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prompt = "portrait of gorgeous cyborg with golden hair, high resolution"
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image = pipeline(prompt=prompt, num_inference_steps=30).images[0]
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```
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For qualitative results, please visit our [project website] (TODO).
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## Citation
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```bibtex
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@misc{todo,
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title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference},
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author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasuland James Thorne and Jongheon Jeong},
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year={2024},
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eprint={todo},
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archivePrefix={arXiv},
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primaryClass={cs.CV,cs.LG}
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}
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```
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