fixed Wan 2.2 VAE crash

models/wan/modules/vae2_2.py

@@ -37,11 +37,29 @@ class CausalConv3d(nn.Conv3d):
             cache_x = cache_x.to(x.device)
             x = torch.cat([cache_x, x], dim=2)
             padding[4] -= cache_x.shape[2]
+            cache_x = None
         x = F.pad(x, padding)
-
-        return super().forward(x)
-
-
+        try:
+            out = super().forward(x)
+            return out
+        except RuntimeError as e:
+            if "miopenStatus" in str(e):
+                print("⚠️ MIOpen fallback: AMD gets upset when trying to work with large areas, and so CPU will be "
+                      "used for this decoding (which is very slow). Consider using tiled VAE Decoding.")
+                x_cpu = x.float().cpu()
+                weight_cpu = self.weight.float().cpu()
+                bias_cpu = self.bias.float().cpu() if self.bias is not None else None
+                print(f"[Fallback] x shape: {x_cpu.shape}, weight shape: {weight_cpu.shape}")
+                out = F.conv3d(x_cpu, weight_cpu, bias_cpu,
+                               self.stride, (0, 0, 0),  # avoid double padding here
+                               self.dilation, self.groups)
+                out = out.to(x.device)
+                if x.dtype in (torch.float16, torch.bfloat16):
+                    out = out.half()
+                if x.dtype != out.dtype:
+                    out = out.to(x.dtype)
+                return out
+            raise
 class RMS_norm(nn.Module):
 
     def __init__(self, dim, channel_first=True, images=True, bias=False):
@@ -1124,6 +1142,8 @@ class Wan2_2_VAE:
                 temperal_downsample=temperal_downsample,
             ).eval().requires_grad_(False).to(device))
         
+        self.model._model_dtype = dtype
+
 
     @staticmethod
     def get_VAE_tile_size(vae_config, device_mem_capacity, mixed_precision):