Create README.md

README.md

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+---
+license: creativeml-openrail-m
+datasets:
+- morj/renaissance_portraits
+language:
+- en
+library_name: keras
+tags:
+- '#stablediffusion '
+- '#renaissance'
+- '# finetune'
+- '#kerascv'
+- '#keras'
+- '#tensorflow'
+- '#diffusers'
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+This model uses the KerasCV implementation of stability.ai's text-to-image model. Unlike other open-source alternatives like Hugging Face's Diffusers, KerasCV offers advantages such as XLA compilation and mixed precision support, resulting in state-of-the-art generation speed.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+This model can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H) to generate high-quality Reniassance portraits from textual prompts. 
+
+This model uses the KerasCV implementation of stability.ai's text-to-image model, Stable Diffusion.
+
+
+- **Developed by:** Martin Gasparyan and Tatev Kyosababyan
+- **Model type:** Diffusion-based text-to-image generative model
+- **Language(s) (NLP):** Python 
+- **License:** CreativeML Open RAIL++-M License
+- **Finetuned from model [https://huggingface.co/CompVis/stable-diffusion-v1-4]:** https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion