metadata

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, 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.

SPRIGHT-T2I model was finetuned from stable diffusion v2.1 on a customized subset of the SPRIGHT dataset, 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.

The training code and more details available in SPRIGHT-T2I GitHub Repository.

A demo is available on Spaces.

Use SPRIGHT-T2I with 🧨 diffusers.

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

Usage

Use the code below to run SPRIGHT-T2I seamlessly and effectively on 🤗's Diffusers library .

pip install diffusers transformers accelerate scipy safetensors

Running the pipeline:

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:

Uses, Bias and Limitations

The Stable Diffusion v2-1 Uses, limitations and biases apply.

Training Details

Training Data

Our training and validation set are a customized subset of the SPRIGHT dataset, 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 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 Intel Gaudi 2 and NVIDIA RTX A6000 GPUs

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	27.39	1.060
SPRIGHT-T2I (ours)	60.68	16.15	0.2133	27.82	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 benchmark.

Model Sources

Repository: SPRIGHT-T2I GitHub Repository
Paper: Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Demo: SPRIGHT-T2I on Spaces

Citation

Coming soon