Update README.md

README.md

@@ -19,7 +19,7 @@ tags:
 # **Stable Diffusion 2-Based Gray-Inpainting to RGB**
 
 
-1. **Gray-Inpainting Model**: Fills missing regions of a grayscale image using a masked inpainting diffusion process based on an autoencoder (AE) instead of a variational autoencoder (VAE).
+1. **Gray-Inpainting Model**: Fills missing regions of a grayscale image using a masked inpainting diffusion process based on an autoencoder (AE) instead of a variational autoencoder (VAE). It Contains mask dectector to enable restoration without mask information(or you can pass explicitly)
 
 2. **Gray-to-RGB Conversion Model**: Converts the grayscale image (or inpainted output) into a full-color RGB image by adding a residual path in the AE. internel unet directly predicts difference between gray and color image's latent
 
@@ -69,5 +69,5 @@ gray2rgb.to('cuda')
 with torch.autocast('cuda',dtype=torch.bfloat16):
     with torch.no_grad():
         # each model's input image should be one of PIL.Image, List[PIL.Image], preprocessed tensor (B,3,H,W). Image must be 3 channel
-        image_gray_restored = gray_inpaintor(image_gray_masked, num_inference_steps=250, seed=10)[0].convert('L') # you can pass 'mask' arg explictly. mask : Tensor (B,1,512,512)
+        image_gray_restored = gray_inpaintor(image_gray_masked, num_inference_steps=250, seed=10)[0].convert('L') # you can pass 'mask' arg explicitly. mask : Tensor (B,1,512,512)
         image_restored = gray2rgb(image_gray_restored.convert('RGB'))