--- 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 are 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") ```
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Additional examples that emphasize spatial coherence:
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## 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 ↓|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](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-t2i.github.io/) ## Citation Coming soon