Update to version 1.7.0

README.md

@@ -29,15 +29,15 @@ The folder structure is organized to mirror the main components of the diffusion
 ├─ unet/
 └─ vae_decoder/
 ```
-The [scheduler](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/main/scheduler) 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](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/main/text_encoder) folder contains the text encoder model used to convert the input prompt into conditioning embeddings for the diffusion model.
+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](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/main/tokenizer) contains the tokenizer configuration and vocabulary files required to preprocess the text prompt before it is fed to the text encoder.
+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](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/main/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 [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](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/main/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.
+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.
 
@@ -45,11 +45,11 @@ The [vae_decoder](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/tree/ma
 | ----------- | ----------- | 
 | 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 | February 2026 | 
-| Model version | 1.8.0 | 
+| 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](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu/blob/main/LICENSE) |
+| 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