SPRIGHT-T2I
/

spright-t2i-sd2

@@ -2,199 +2,117 @@
 library_name: diffusers
 ---
-# Model Card for Model ID
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 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.
+SPRIGHT-T2I model was finetuned from stable diffusion v2.1 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.
+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 scipy safetensors
+```
+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")
+```
+<img src="kitten_sitting_in_a_dish.png" width="300" alt="img">
+Additional examples that emphasize spatial coherence:
+<img src="result_images/visor.png" width="1000" alt="img">
+## 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         |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](https://github.com/djghosh13/geneval) benchmark.
+### Model Sources
+- **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)
+## Citation
+Coming soon