---
library_name: diffusers
---
# SPRIGHT-T2I Model Card
The SPRIGHT-T2I model is a text-to-image diffusion model with high spatial coherency. It was first introduced in [Getting it Right: Improving Spatial Consistency in Text-to-Image Models](https://),
authored by Agneet Chatterjee\*, Gabriela Ben Melech Stan*, Estelle Aflalo, Sayak Paul, Dhruba Ghosh,
Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, and Yezhou Yang.
_(* denotes equal contributions)_
SPRIGHT-T2I model was finetuned from [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) on a subset
of the [SPRIGHT dataset](https://huggingface.co/datasets/SPRIGHT-T2I/spright), which contains images and spatially focused
captions. Leveraging SPRIGHT, along with efficient training techniques, we achieve state-of-the art
performance in generating spatially accurate images from text.
## Table of contents
* [Model details](#model-details)
* [Usage](#usage)
* [Bias and Limitations](#bias-and-limitations)
* [Training](#training)
* [Evaluation](#evaluation)
* [Model Resources](#model-resources)
* [Citation](#citation)
The training code and more details available in [SPRIGHT-T2I GitHub Repository](https://github.com/orgs/SPRIGHT-T2I).
A demo is available on [Spaces](https://huggingface.co/spaces/SPRIGHT-T2I/SPRIGHT-T2I).
Use SPRIGHT-T2I with 🧨 [`diffusers`](https://huggingface.co/SPRIGHT-T2I/spright-t2i-sd2#usage).
## Model Details
- **Developed by:** Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, and Yezhou Yang
- **Model type:** Diffusion-based text-to-image generation model with spatial coherency
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model:** [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)
## Usage
Use the code below to run SPRIGHT-T2I seamlessly and effectively on [🤗's Diffusers library](https://github.com/huggingface/diffusers) .
```bash
pip install diffusers transformers accelerate -U
```
Running the pipeline:
```python
from diffusers import DiffusionPipeline
pipe_id = "SPRIGHT-T2I/spright-t2i-sd2"
pipe = DiffusionPipeline.from_pretrained(
pipe_id,
torch_dtype=torch.float16,
use_safetensors=True,
).to("cuda")
prompt = "a cute kitten is sitting in a dish on a table"
image = pipe(prompt).images[0]
image.save("kitten_sittin_in_a_dish.png")
```
Additional examples that emphasize spatial coherence:
## Bias and Limitations
The biases and limitation as specified in [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) apply here as well.
## Training
#### Training Data
Our training and validation set are a subset of the [SPRIGHT dataset](https://huggingface.co/datasets/SPRIGHT-T2I/spright), and consists of 444 and
50 images respectively, randomly sampled in a 50:50 split between LAION-Aesthetics and Segment Anything. Each image is paired with both, a general and a spatial caption
(from SPRIGHT). During fine-tuning, for each image, we randomly choose one of the given caption types in a 50:50 ratio.
We find that SPRIGHT largely improves upon existing datasets in capturing spatial relationships.
Additionally, we find that training on images containing a large number of objects results in substantial improvements in spatial consistency.
To construct our dataset, we focused on images with object counts larger than 18, utilizing the open-world image tagging model
[Recognize Anything](https://huggingface.co/xinyu1205/recognize-anything-plus-model) to achieve this constraint.
#### Training Procedure
Our base model is Stable Diffusion v2.1. We fine-tune the U-Net and the OpenCLIP-ViT/H text-encoder as part of our training for 10,000 steps, with different learning rates.
- **Training regime:** fp16 mixed precision
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 4 x 8 = 32
- **UNet learning rate:** 0.00005
- **CLIP text-encoder learning rate:** 0.000001
- **Hardware:** Training was performed using NVIDIA RTX A6000 GPUs and Intel®Gaudi®2 AI accelerators.
## Evaluation
We find that compared to the baseline model SD 2.1, we largely improve the spatial accuracy, while also enhancing the non-spatial aspects associated with a text-to-image model.
The following table compares our SPRIGHT-T2I model with SD 2.1 across multiple spatial reasoning and image quality:
|Method |OA(%) ↑|VISOR-4(%) ↑|T2I-CompBench ↑|FID ↓|CCMD ↓|
|------------------|-------|------------|---------------|-----|------|
|SD v2.1 |47.83 |4.70 |0.1507 |21.646|1.060 |
|SPRIGHT-T2I (ours)|60.68 |16.15 |0.2133 |16.149|0.512 |
Our key findings are:
- Increased the Object Accuracy (OA) score by 26.86%, indicating that we are much better at generating objects mentioned in the input prompt
- Visor-4 score of 16.15% denotes that for a given input prompt, we consistently generate a spatially accurate image
- Improve on all aspects of the VISOR score while improving the ZS-FID and CMMD score on COCO-30K images by 23.74% and 51.69%, respectively
- Enhance the ability to generate 1 and 2 objects, along with generating the correct number of objects, as indicated by evaluation on the [GenEval](https://github.com/djghosh13/geneval) benchmark.
### Model Resources
- **Dataset**: [SPRIGHT Dataset](https://huggingface.co/datasets/SPRIGHT-T2I/spright)
- **Repository:** [SPRIGHT-T2I GitHub Repository](https://github.com/orgs/SPRIGHT-T2I)
- **Paper:** [Getting it Right: Improving Spatial Consistency in Text-to-Image Models](https://)
- **Demo:** [SPRIGHT-T2I on Spaces](https://huggingface.co/spaces/SPRIGHT-T2I/SPRIGHT-T2I)
- **Project Website**: [SPRIGHT Website](https://spright.github.io/)
## Citation
Coming soon
