Update README.md

README.md

@@ -60,6 +60,64 @@ This demonstrates the fidelity of VQGAN latents for reconstruction and downstrea
 
 ---
 
+## Decoding Latents
+
+You can decode the latent representations back into images using a pre-trained VQGAN.  
+Here is an example workflow:
+
+```python
+import torch
+from PIL import Image
+import matplotlib.pyplot as plt
+from torchvision import transforms
+from ldm.util import instantiate_from_config
+from omegaconf import OmegaConf
+
+# ----------------------------
+# 1) Initialize VQGAN
+# ----------------------------
+class VQGANProcessor:
+    def __init__(self, config_path, ckpt_path, device=None):
+        self.device = device or (torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'))
+        config = OmegaConf.load(config_path)
+        sd = torch.load(ckpt_path, map_location=self.device)['state_dict']
+        model = instantiate_from_config(config.model)
+        model.load_state_dict(sd, strict=False)
+        model.eval().to(self.device)
+        self.first_stage = model.first_stage_model
+
+    def decode(self, latent: torch.Tensor) -> torch.Tensor:
+        with torch.no_grad():
+            rec = self.first_stage.decode(latent.to(self.device))
+            rec = torch.clamp((rec + 1.)/2., 0, 1)
+        return rec
+
+# ----------------------------
+# 2) Load a latent sample
+# ----------------------------
+latent_path = "latent_imagenet/class_name/sample.pt"
+latent = torch.load(latent_path).unsqueeze(0)  # [1,3,64,64]
+
+# ----------------------------
+# 3) Decode the latent
+# ----------------------------
+processor = VQGANProcessor("configs/latent-diffusion/cin256-v2.yaml",
+                           "models/ldm/cin256-v2/model.ckpt")
+recon = processor.decode(latent).squeeze(0).permute(1,2,0).cpu().numpy()  # [H,W,3] ∈ [0,1]
+
+# ----------------------------
+# 4) (Optional) Compare with original image
+# ----------------------------
+orig_path = "process_data/imagenet/val/class_name/sample.JPEG"
+orig = Image.open(orig_path).convert("RGB")
+orig_resized = orig.resize((256,256))
+
+fig, axes = plt.subplots(1,3, figsize=(18,6))
+axes[0].imshow(orig); axes[0].set_title("Original"); axes[0].axis("off")
+axes[1].imshow(orig_resized); axes[1].set_title("Resized 256×256"); axes[1].axis("off")
+axes[2].imshow(recon); axes[2].set_title("Reconstruction"); axes[2].axis("off")
+plt.show()
+
 ## Example Usage
 
 ```python