Update README.md

README.md

@@ -7,16 +7,16 @@ base_model:
 pipeline_tag: text-to-image
 library_name: diffusers
 ---
-# AMD Nitro Diffusion
+# AMD Nitro-1
 
 
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6355aded9c72a7e742f341a4/AsUvS7acUDLZhKOMRSH37.jpeg)
 
 ## Introduction
-AMD Nitro Diffusion is a series of efficient text-to-image generation models that are distilled from popular diffusion models on AMD Instinct™ GPUs. The release consists of:
+Nitro-1 is a series of efficient text-to-image generation models that are distilled from popular diffusion models on AMD Instinct™ GPUs. The release consists of:
 
-* [Stable Diffusion 2.1 Nitro](https://huggingface.co/amd/SD2.1-Nitro): a UNet-based one-step model distilled from [Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
-* [PixArt-Sigma Nitro](https://huggingface.co/amd/PixArt-Sigma-Nitro): a high resolution transformer-based one-step model distilled from [PixArt-Sigma](https://pixart-alpha.github.io/PixArt-sigma-project/).
+* [Nitro-1-SD](https://huggingface.co/amd/SD2.1-Nitro): a UNet-based one-step model distilled from [Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
+* [Nitro-1-PixArt](https://huggingface.co/amd/PixArt-Sigma-Nitro): a high resolution transformer-based one-step model distilled from [PixArt-Sigma](https://pixart-alpha.github.io/PixArt-sigma-project/).
 
 ⚡️ [Open-source code](https://github.com/AMD-AIG-AIMA/AMD-Diffusion-Distillation)! The models are based on our re-implementation of [Latent Adversarial Diffusion Distillation](https://arxiv.org/abs/2403.12015), the method used to build the popular Stable Diffusion 3 Turbo model. Since the original authors didn't provide training code, we release our re-implementation to help advance further research in the field.
 
@@ -24,9 +24,9 @@ AMD Nitro Diffusion is a series of efficient text-to-image generation models tha
 
 ## Details
 
-* **Model architecture**: PixArt-Sigma Nitro has the same architecture as PixArt-Sigma and is compatible with the diffusers pipeline.
+* **Model architecture**: Nitro-1-PixArt has the same architecture as PixArt-Sigma and is compatible with the diffusers pipeline.
 * **Inference steps**: This model is distilled to perform inference in just a single step. However, the training code also supports distilling a model for 2, 4 or 8 steps.
-* **Hardware**: We use a single node consisting of 4 AMD Instinct™ MI250 GPUs for distilling PixArt-Sigma Nitro.
+* **Hardware**: We use a single node consisting of 4 AMD Instinct™ MI250 GPUs for distilling Nitro-1-PixArt.
 * **Dataset**: We use 1M prompts from [DiffusionDB](https://huggingface.co/datasets/poloclub/diffusiondb) and generate the corresponding images from the base PixArt-Sigma model.
 * **Training cost**: The distillation process achieves reasonable results in less than 2 days on a single node.
 
@@ -64,7 +64,7 @@ Compared to [PixArt-Sigma](https://pixart-alpha.github.io/PixArt-sigma-project/)
 | Model    | FID ↓ | CLIP ↑ |FLOPs| Latency on AMD Instinct MI250 (sec)
 | :---: | :---: | :---: | :---: | :---:
 | PixArt-Sigma, 20 steps | 34.14   | 0.3289 |187.96 | 7.46
-| **PixArt-Sigma Nitro**, 1 step | 37.75     | 0.3167|17.04 | 0.53
+| **Nitro-1-PixArt**, 1 step | 37.75     | 0.3167|17.04 | 0.53