readme updates

README.md

@@ -1,21 +1,26 @@
 ---
 language: en
+license: creativeml-openrail-m
 tags:
 - stable-diffusion
 - stable-diffusion-diffusers
 - text-to-image
+- RyzenAI
+- Quantization
+- ONNX
+- Computer Vision
 inference: true
 ---
-# Stable Diffusion 1.5 on AMD NPU
+# 🚀 Stable Diffusion 1.5 on AMD AI PC NPU
 
-"Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
-For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion)". (Original Hugging Face model card: [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)).
+"Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion)". 
+More details about this model can be found on the original Hugging Face model card: [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
 
-This repository contains the ONNX models required to run the image generation pipeline on AMD NPUs.
+This model repo contains the optimized ONNX models required to run the image generation pipeline for Stable Diffusion 1.5 on AMD NPUs. 
 
-The folder structure is organized to mirror the main components of the diffusion pipeline (scheduler, text encoder, tokenizer, UNet, and VAE decoder).
+## Model Details
 
-## Repository structure
+The folder structure is organized to mirror the main components of the diffusion pipeline (scheduler, text encoder, tokenizer, UNet, and VAE decoder).
 
 ```text
 ├─ scheduler/
@@ -24,27 +29,44 @@ The folder structure is organized to mirror the main components of the diffusion
 ├─ unet/
 └─ vae_decoder/
 ```
-### `scheduler/`
-This folder contains the scheduler configuration (timesteps, betas, alphas, etc.) used during the diffusion sampling process.
+The [scheduler](scheduler) folder contains the scheduler configuration (timesteps, betas, alphas, etc.) used during the diffusion sampling process.
+
+The [text_encoder](text_encoder) folder contains the text encoder model used to convert the input prompt into conditioning embeddings for the diffusion model.
+
+The [tokenizer](tokenizer) contains the tokenizer configuration and vocabulary files required to preprocess the text prompt before it is fed to the text encoder.
+
+The [unet](unet) folder contains the UNet model used in the diffusion process. The UNet is exported and structured specifically to leverage the AMD NPU accelerator for the denoising steps.
+
+The [vae_decoder](vae_decoder) folder contains the VAE decoder model used to map latent representations back to the image space. The VAE decoder is also structured to make use of the NPU accelerator for efficient image reconstruction.
+
+> Note: UNet and VAE decoder models are optimized and structured to run on AMD NPUs. The other components (text encoder, tokenizer and scheduler) are shared between GPU and NPU pipelines, but are provided here for completeness.
+
+| Model Details | Description |
+| ----------- | ----------- | 
+| Person or organization developing model | [Giovanni Guasti (AMD)](https://huggingface.co/gguasti), [Benjamin Consolvo (AMD)](https://huggingface.co/bconsolvo) | 
+| Original model authors | [Robin Rombach](https://huggingface.co/rromb), [Patrick Esser](https://huggingface.co/pesser) | 
+| Model date | January 2026 | 
+| Model version | 1.7.0 | 
+| Model type | Diffusion-based text-to-image generation model | 
+| Information about training algorithms, parameters, fairness constraints or other applied approaches, and features | This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). | 
+| License | [CreativeML OpenRAIL-M](LICENSE) |
+| Where to send questions or comments about the model | [Community Tab](https://hf.co/amd/stable-diffusion-1.5-amdnpu/discussions) and [AMD Developer Community Discord](https://discord.gg/amd-dev) |
+
+## ⚡ Intended Use
+
+#### Getting Started
 
-### `text_encoder/`
-This folder contains the text encoder model used to convert the input prompt into conditioning embeddings for the diffusion model.
+A comprehensive set of documentation is kept on the AMD SD-Sandbox GitHub repository here: [github.com/amd/sd-sandbox](https://github.com/amd/sd-sandbox). Please refer to that for how to get started with this model.
 
-### `tokenizer/`
-This folder contains the tokenizer configuration and vocabulary files required to preprocess the text prompt before it is fed to the text encoder.
+## ⚓ Ethical Considerations
 
-### `unet/`
-This folder contains the UNet model used in the diffusion process.
-The UNet is exported and structured specifically to leverage the AMD NPU accelerator for the denoising steps.
+AMD is committed to conducting our business in a fair, ethical and honest manner and in compliance with all applicable laws, rules and regulations. You can find out more at the [AMD Ethics and Compliance](https://www.amd.com/en/corporate/corporate-responsibility/ethics-and-compliance.html) page.
 
-### `vae_decoder/`
-This folder contains the VAE decoder model used to map latent representations back to the image space.
-The VAE decoder is also structured to make use of the NPU accelerator for efficient image reconstruction.
+## ⚠️ Caveats and Recommendations
 
-## Notes
+Please visit the original model card for more details: [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
 
-- UNet and VAE decoder models are optimized and structured to run on AMD NPUs.
-- The other components (text encoder, tokenizer and scheduler) are shared between GPU and NPU pipelines, but are provided here for completeness.
+## 📌 Citation Details
 
 ```bibtex
     @InProceedings{Rombach_2022_CVPR,