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. (*denotes equal contributions)
SPRIGHT-T2I model was finetuned from Stable Diffusion v2.1 on a 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.
Table of contents
The training code and more details are 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 -U
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")
![img](/SPRIGHT-T2I/spright-t2i-sd2/resolve/main/kitten_sitting_in_a_dish.png)
Additional examples that emphasize spatial coherence:
![img](/SPRIGHT-T2I/spright-t2i-sd2/resolve/main/result_images/visor.png)
Bias and Limitations
The biases and limitation as specified in Stable Diffusion v2-1 apply here as well.
Training
Training Data
Our training and validation set are a 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 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 ↓ | CMMD ↓ |
---|---|---|---|---|---|
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:
- We increase the VISOR 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 Resources
- Dataset: SPRIGHT Dataset
- Repository: SPRIGHT-T2I GitHub Repository
- Paper: Getting it Right: Improving Spatial Consistency in Text-to-Image Models
- Demo: SPRIGHT-T2I on Spaces
- Project Website: SPRIGHT Website
Citation
Coming soon