Instructions to use sanaka87/Show-o-RecA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sanaka87/Show-o-RecA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sanaka87/Show-o-RecA", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
base_model:
- showlab/show-o-w-clip-vit
datasets:
- brivangl/midjourney-v6-llava
language:
- en
- zh
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
Show-o-RecA
A self-supervised training framework that aligns understanding and generation in modest compute, with huge zero-shot gain on generation and editing capability.
This repository hosts the model weights for Show-o-RecA. For installation, usage instructions, and further documentation, please visit Show-o's original GitHub repository.
π§ Method
π Benchmarks
| Model | GenEval β | DPGBench β | WISE β |
|---|---|---|---|
| Show-o | 0.57 | 70.65 | 0.33 |
| Show-o-RecA | 0.62 | 75.70 | 0.34 |
License
Show-o-RecA is licensed under the Apache 2.0 license.
βοΈ Citation
If you find our work inspiring or use our codebase in your research, please consider giving a star β and a citation~
@article{xie2025reconstruction,
title={Reconstruction Alignment Improves Unified Multimodal Models},
author={Xie, Ji and Darrell, Trevor and Zettlemoyer, Luke and Wang, XuDong},
journal={arXiv preprint arXiv:2509.07295},
year={2025}
}