initial commit for the model card

README.md

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+---
+library_name: diffusers
+pipeline_tag: text-to-image
+---
+
+## Model Details
+
+### Model Description
+
+This model is fine-tuned from [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on 110,000 image-text pairs from the MIMIC dataset using the attention-tuning PEFT method. Under this fine-tuning strategy, fine-tune only the attention weights in the U-Net while keeping everything else frozen. Attention tuning was first shown to be effective for fine-tuning vision transformers in this [paper](https://arxiv.org/abs/2203.09795)
+
+- **Developed by:** [Raman Dutt](https://twitter.com/RamanDutt4)
+- **Shared by:** [Raman Dutt](https://twitter.com/RamanDutt4)
+- **Model type:** [Stable Diffusion fine-tuned using Parameter-Efficient Fine-Tuning]
+- **Finetuned from model:** [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
+
+### Model Sources
+
+
+- **Paper:** [Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity](https://arxiv.org/abs/2305.08252)
+- **Demo:** [MIMIC-SD-PEFT-Demo](https://huggingface.co/spaces/raman07/MIMIC-SD-Demo-Memory-Optimized?logs=container)
+
+## Direct Use
+
+This model can be directly used to generate realistic medical images from text prompts. 
+
+
+## How to Get Started with the Model
+
+```python
+from diffusers.pipelines import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained(sd_folder_path, revision="fp16")
+
+```
+
+
+## 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+
+#### Metrics
+
+This model has been evaluated using the Fréchet inception distance (FID) Score on MIMIC dataset.
+
+### Results
+
+[More Information Needed]
+
+
+## Environmental Impact
+
+
+
+## Citation
+
+
+**BibTeX:**
+
+@article{dutt2023parameter,
+  title={Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity},
+  author={Dutt, Raman and Ericsson, Linus and Sanchez, Pedro and Tsaftaris, Sotirios A and Hospedales, Timothy},
+  journal={arXiv preprint arXiv:2305.08252},
+  year={2023}
+}
+
+**APA:**  
+Dutt, R., Ericsson, L., Sanchez, P., Tsaftaris, S. A., & Hospedales, T. (2023). Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity. arXiv preprint arXiv:2305.08252.
+
+## Model Card Authors
+
+Raman Dutt  
+[Twitter](https://twitter.com/RamanDutt4)  
+[LinkedIn](https://www.linkedin.com/in/raman-dutt/)  
+[Email](mailto:s2198939@ed.ac.uk)