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README.md

@@ -13,9 +13,9 @@ tags:
 - **Source**: `https://huggingface.co/spacepxl/Wan2.1-VAE-upscale2x`
 - **File**: `Wan2.1_VAE_upscale2x_imageonly_real_v1.safetensors`
 - **FP8 Format**: `E5M2`
-- **LoRA Rank**: 128
+- **LoRA Rank**: 64
 - **Architecture Target**: vae
-- **LoRA File**: `Wan2.1_VAE_upscale2x_imageonly_real_v1-lora-r128-vae.safetensors`
+- **LoRA File**: `Wan2.1_VAE_upscale2x_imageonly_real_v1-lora-r64-vae.safetensors`
 - **FP8 File**: `Wan2.1_VAE_upscale2x_imageonly_real_v1-fp8-e5m2.safetensors`
 
 ## Usage (Inference)
@@ -25,7 +25,7 @@ import torch
 
 # Load FP8 model
 fp8_state = load_file("Wan2.1_VAE_upscale2x_imageonly_real_v1-fp8-e5m2.safetensors")
-lora_state = load_file("Wan2.1_VAE_upscale2x_imageonly_real_v1-lora-r128-vae.safetensors")
+lora_state = load_file("Wan2.1_VAE_upscale2x_imageonly_real_v1-lora-r64-vae.safetensors")
 
 # Reconstruct approximate original weights
 reconstructed = {}
@@ -36,11 +36,12 @@ for key in fp8_state:
         if A.ndim == 2 and B.ndim == 2:
             lora_weight = B @ A
         else:
-            # Handle convolutional LoRA (simplified)
-            lora_weight = torch.zeros_like(fp8_state[key], dtype=torch.float32)
+            # Conv LoRA: simplified reconstruction
+            lora_weight = F.conv2d(fp8_state[key].unsqueeze(0).to(torch.float32), A, groups=1)[:, :B.shape[0]]
+            lora_weight = lora_weight.squeeze(0) + F.conv2d(fp8_state[key].unsqueeze(0).to(torch.float32), B, groups=1).squeeze(0)
         reconstructed[key] = fp8_state[key].to(torch.float32) + lora_weight
     else:
         reconstructed[key] = fp8_state[key].to(torch.float32)
 ```
 
-> Requires PyTorch ≥ 2.1 for FP8 support. Use the same architecture selection during inference.
+> Requires PyTorch ≥ 2.1 for FP8 support. Use matching architecture during inference.