Add alternative VAE decoding node

This was actually unused code in the VAE model, only does spatial tiling though, but seams look better

fp8_optimization.py

@@ -30,7 +30,7 @@ def fp8_linear_forward(cls, original_dtype, input):
 
             if isinstance(o, tuple):
                 o = o[0]
-                
+
             if tensor_2d:
                 return o.reshape(input.shape[0], -1)
 
@@ -38,7 +38,6 @@ def fp8_linear_forward(cls, original_dtype, input):
         else:
             cls.to(original_dtype)
             out = cls.original_forward(input.to(original_dtype))
-            cls.to(original_dtype)
             return out
     else:
         return cls.original_forward(input)

nodes.py

@@ -464,6 +464,56 @@ class MochiDecode:
 
         return (frames,)
 
+class MochiDecodeSpatialTiling:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "vae": ("MOCHIVAE",),
+            "samples": ("LATENT", ),
+            "enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": "Drastically reduces memory use but may introduce seams"}),
+            "num_tiles_w": ("INT", {"default": 4, "min": 2, "max": 64, "step": 2, "tooltip": "Number of horizontal tiles"}),
+            "num_tiles_h": ("INT", {"default": 4, "min": 2, "max": 64, "step": 2, "tooltip": "Number of vertical tiles"}),
+            "overlap": ("INT", {"default": 16, "min": 0, "max": 256, "step": 1, "tooltip": "Number of pixel of overlap between adjacent tiles"}),
+            "min_block_size": ("INT", {"default": 1, "min": 1, "max": 256, "step": 1, "tooltip": "Minimum number of pixels in each dimension when subdividing"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "decode"
+    CATEGORY = "MochiWrapper"
+
+    def decode(self, vae, samples, enable_vae_tiling, num_tiles_w, num_tiles_h, overlap, 
+               min_block_size):
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        intermediate_device = mm.intermediate_device()
+        samples = samples["samples"]
+        samples = samples.to(torch.bfloat16).to(device)
+
+        B, C, T, H, W = samples.shape
+
+        vae.to(device)
+        
+        with torch.autocast(mm.get_autocast_device(device), dtype=torch.bfloat16):
+            if enable_vae_tiling:
+                from .mochi_preview.vae.model import apply_tiled
+                logging.warning("Decoding with tiling...")
+                frames = apply_tiled(vae, samples, num_tiles_w = num_tiles_w, num_tiles_h = num_tiles_h, overlap=overlap, min_block_size=min_block_size)
+            else:
+                logging.info("Decoding without tiling...")
+                frames = vae(samples)
+                
+        vae.to(offload_device)
+
+        frames = frames.float()
+        frames = (frames + 1.0) / 2.0
+        frames.clamp_(0.0, 1.0)
+
+        frames = rearrange(frames, "b c t h w -> (t b) h w c").to(intermediate_device)
+
+        return (frames,)
+
 
 NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadMochiModel": DownloadAndLoadMochiModel,
@@ -472,6 +522,7 @@ NODE_CLASS_MAPPINGS = {
     "MochiTextEncode": MochiTextEncode,
     "MochiModelLoader": MochiModelLoader,
     "MochiVAELoader": MochiVAELoader,
+    "MochiDecodeSpatialTiling": MochiDecodeSpatialTiling
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DownloadAndLoadMochiModel": "(Down)load Mochi Model",
@@ -480,4 +531,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "MochiTextEncode": "Mochi TextEncode",
     "MochiModelLoader": "Mochi Model Loader",
     "MochiVAELoader": "Mochi VAE Loader",
+    "MochiDecodeSpatialTiling": "Mochi VAE Decode Spatial Tiling"
     }