Update README.md

README.md

@@ -8,7 +8,6 @@ This model card focuses on the models associated with the StableSR, available [h
 ## Model Details
 - **Developed by:** Jianyi Wang
 - **Model type:** Diffusion-based image super-resolution model
-- **Language(s):** English
 - **License:** [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt)
 - **Model Description:** This is the model used in [Paper](https://arxiv.org/abs/2305.07015).
 - **Resources for more information:** [GitHub Repository](https://github.com/IceClear/StableSR).
@@ -39,7 +38,7 @@ Such strong conditions make our model less likely to be affected.
 ## Training
 
 **Training Data**
-The model developers used the following dataset for training the model:
+The model developer used the following dataset for training the model:
 
 - Our diffusion model is finetuned on DF2K (DIV2K and Flickr2K) + OST datasets, available [here](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/Training.md).
 - We further generate 100k synthetic LR-HR pairs on DF2K_OST using the finetuned diffusion model for training the CFW module.
@@ -47,19 +46,17 @@ The model developers used the following dataset for training the model:
 **Training Procedure**
 StableSR is an image super-resolution model finetuned on [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), further equipped with a time-aware encoder and a controllable feature wrapping (CFW) module.
 
-- Following Stable Diffusion, images are encoded through the fixed VQGAN encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
+- Following Stable Diffusion, images are encoded through the fixed VQGAN encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4.
 - The latent representations are fed to the time-aware encoder as guidance.
 - The loss is the same as Stable Diffusion.
 - After finetuning the diffusion model, we further train the CFW module using the data generated by the finetuned diffusion model.
 - The VQGAN model is fixed and only CFW is trainable.
-- The loss is similar to training a VQGAN except that we use a fixed adversarial loss weight of 0.025 rather than a self-adjustable one.
+- The loss is similar to training a VQGAN, except that we use a fixed adversarial loss weight of 0.025 rather than a self-adjustable one.
 
-We currently provide the following checkpoints, for various versions:
+We currently provide the following checkpoints:
 
 - `stablesr_000117.ckpt`: Diffusion model finetuned on DF2K_OST dataset for 117 epochs.
 - `vqgan_cfw_00011.ckpt`: CFW module with fixed VQGAN trained on synthetic paired data for 11 epochs.
 
 ## Evaluation Results 
-See [Paper](https://arxiv.org/abs/2305.07015) for details.
-
-
+See [Paper](https://arxiv.org/abs/2305.07015) for details.