Add metadata and sample usage

This PR adds missing metadata to the model card, including the `pipeline_tag`, `library_name`, and license, making the model more discoverable on the Hugging Face Hub. It also includes a basic sample usage example for multimodal understanding.

README.md

@@ -1,3 +1,114 @@
----
-license: mit
----
+---
+license: mit
+library_name: diffusers
+pipeline_tag: any-to-any
+---
+
+<div align="center">
+<br>
+<img src="docs/title.png" width="166"> <!-- Replace with your logo -->
+<h3>Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and Generation</h3>
+
+[Anonymous CVPR submission]
+
+[![ArXiv](https://img.shields.io/badge/ArXiv-PaperID12251-<COLOR>.svg)](https://arxiv.org/abs/your_paper_id)  [![Demo](https://img.shields.io/badge/Demo-ComingSoon-<COLOR>.svg)](https://your_demo_link) [![Discord](https://img.shields.io/badge/Discord-join-blueviolet?logo=discord&amp)](https://your_discord_link) 
+
+</div>
+
+## News
+* **[2024-11-29]** We release a [256-resolution version of the weights](https://huggingface.co/SJTU-Deng-Lab/Show-o-Turbo-256) for Show-o Turbo on Hugging Face.
+
+  
+
+## What's New about Show-o Turbo?
+
+Show-o Turbo builds upon Show-o to address its inefficiency issues in both image and text generation. While Show-o relies on progressive denoising for images and autoregressive decoding for text, Show-o Turbo introduces a unified denoising perspective for both modalities, leading to significantly faster generation speeds.  Show-o Turbo achieves this through several key innovations:
+
+<p align="center">
+<img src="docs/trajectory.png" style="max-width: 100%;"> <!-- Charts and graphs showcasing results -->
+</p>
+
+
+* **Unified Denoising:**  Show-o Turbo utilizes parallel text decoding techniques (Jacobi decoding) to reframe text generation as a denoising process, analogous to image generation. This enables a unified view of both modalities as denoising trajectories.
+* **Consistency Distillation:**  Show-o Turbo employs consistency distillation, a technique inspired by diffusion model acceleration, to shorten these multimodal denoising trajectories. This allows the model to generate meaningful content faster.
+* **Trajectory Segmentation and Curriculum Learning:** To enhance convergence, Show-o Turbo uses a staged training approach with decreasing trajectory segments and curriculum learning.
+* **Top-k Sampling:**  Show-o Turbo utilizes top-k sampling during inference to improve sample quality, especially with fewer sampling steps.
+
+## Results
+
+Show-o Turbo shows significant speedups in both text-to-image and image-to-text generation, while maintaining comparable performance to Show-o.
+
+* In text-to-image generation, it achieves performance close to that of Show-o at 8-step sampling at 4-step sampling, and surpasses Show-o at 4-step sampling at 2-step sampling.
+  <p align="center">
+  <img src="docs/t2i_result.png" width="777"> <!-- Charts and graphs showcasing results -->
+  </p>
+
+* In multimodal understanding tasks, it is about 1.5 times faster without much performance loss.
+  <p align="center">
+  <img src="docs/mmu_result.png" width="777"> <!-- Charts and graphs showcasing results -->
+  </p>
+
+## Getting Started
+
+First, set up the environment:
+```bash
+pip3 install -r requirements.txt
+```
+
+### Inference
+
+**Multimodal Understanding:**
+
+```bash
+python3 inference_mmu.py config=configs/showo_turbo_mmu.yaml
+```
+
+This will run the multimodal understanding inference with the default settings from `configs/showo_turbo_mmu.yaml`.  You can modify this config file to customize the input and other parameters.
+
+<p align="center">
+<img src="docs/mmu.png" style="max-width: 100%;"> <!-- Example output of MMU inference -->
+</p>
+
+
+**Text-to-Image Generation:**
+
+```bash
+python3 inference_t2i.py config=configs/showo_turbo_t2i.yaml
+```
+
+This will run text-to-image generation with default settings.  Similar to MMU, you can adjust parameters in the config file.
+
+<p align="center">
+<img src="docs/t2i.png" style="max-width: 100%;"> <!-- Example output of T2I inference -->
+</p>
+
+
+
+## Training pipeline
+
+**(Coming Soon)** Details about the training process, including data preparation, scripts, and configuration options will be provided here upon release. Example command:
+
+```bash
+accelerate launch --config_file path/to/your/accelerate_config --main_process_port=8888 training/train_showo_turbo.py config=configs/showo_turbo_training.yaml
+```
+
+
+## TODO
+
+- [X] Release the inference and training code.
+- [X] Release the model weights.
+- [ ] Conduct further experiments with larger model sizes and datasets.
+
+## Contributing
+
+We welcome contributions to Show-o Turbo!  If you have ideas for new features or improvements, please open an issue or submit a pull request.
+
+
+## Citation
+
+**(Coming Soon)** Citation information will be provided here upon publication.
+
+
+## Acknowledgments
+
+We would like to thank the authors of Show-o and the developers of the libraries and frameworks upon which Show-o Turbo is built, including  open-muse, Phi-1.5,  maskgit, taming-transformers, transformers, accelerate, diffusers. Thanks to all the authors for their great work.