update Readme

README.md

@@ -2,199 +2,117 @@
 library_name: diffusers
 ---
 
-# Model Card for Model ID
+# SPRIGHT-T2I Model Card
 
-<!-- Provide a quick summary of what the model is/does. -->
+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 customized 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).
 
-## Model Details
+A demo is available on [Spaces](https://huggingface.co/spaces/SPRIGHT-T2I/SPRIGHT-T2I).
 
-### Model Description
+Use SPRIGHT-T2I with 🧨 [`diffusers`](https://huggingface.co/SPRIGHT-T2I/spright-t2i-sd2#usage).
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
+## Model Details
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **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 [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- **Finetuned from model:**  [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)
 
-### Direct Use
+## Usage
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Use the code below to run SPRIGHT-T2I seamlessly and effectively on [🤗's Diffusers library](https://github.com/huggingface/diffusers) .
 
-[More Information Needed]
+```bash
+pip install diffusers transformers accelerate scipy safetensors
+```
 
-### Downstream Use [optional]
+Running the pipeline:
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```python
+from diffusers import DiffusionPipeline
 
-[More Information Needed]
+pipe_id = "SPRIGHT-T2I/spright-t2i-sd2"
 
-### Out-of-Scope Use
+pipe = DiffusionPipeline.from_pretrained(
+    pipe_id,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+).to("cuda")
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+prompt = "a cute kitten is sitting in a dish on a table"
+image = pipe(prompt).images[0]
 
-[More Information Needed]
+image.save("kitten_sittin_in_a_dish.png")
+```
 
-## Bias, Risks, and Limitations
+<img src="kitten_sitting_in_a_dish.png" width="300" alt="img">
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Additional examples that emphasize spatial coherence:
+<img src="result_images/visor.png" width="1000" alt="img">
 
-[More Information Needed]
+## Uses, Bias and Limitations
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+The [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) Uses, limitations and biases apply.
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Our training and validation set are a customized 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. 
 
-[More Information Needed]
+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 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+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 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+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. 
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+The following table compares our SPRIGHT-T2I model with SD 2.1 across multiple spatial reasoning and image quality:
 
-[More Information Needed]
+|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.
 
-## More Information [optional]
+### Model Sources
 
-[More Information Needed]
+- **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)
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+Coming soon
 
-## Model Card Contact
 
-[More Information Needed]