Wan2.2 (5B) tiled VAE encode

__init__.py

@@ -9,6 +9,7 @@ NODE_CLASS_MAPPINGS = {
     "WanCameraImageToVideoTiledVAE":WanCameraImageToVideoTiledVAE,
     "WanPhantomSubjectToVideoTiledVAE": WanPhantomSubjectToVideoTiledVAE,
     "WanTrackToVideoTiledVAE": WanTrackToVideoTiledVAE,
+    "Wan22ImageToVideoLatentTiledVAE":Wan22ImageToVideoLatentTiledVAE,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -20,4 +21,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "WanCameraImageToVideoTiledVAE": "WanCameraImageToVideo (Tiled VAE encode)",
     "WanPhantomSubjectToVideoTiledVAE": "WanPhantomSubjectToVideo (Tiled VAE encode)",
     "WanTrackToVideoTiledVAE": "WanTrackToVideo (Tiled VAE encode)",
+    "Wan22ImageToVideoLatentTiledVAE":"Wan22ImageToVideoLatent (Tiled VAE encode)",
 }

nodes_wan.py

@@ -495,3 +495,50 @@ class WanTrackToVideoTiledVAE:
         out_latent = {}
         out_latent["samples"] = latent
         return (positive, negative, out_latent)
+
+
+class Wan22ImageToVideoLatentTiledVAE:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE", ),
+                            "width": ("INT", {"default": 1280, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                            "height": ("INT", {"default": 704, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                            "length": ("INT", {"default": 49, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+
+                            "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                            "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                            "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Amount of frames to encode at a time."}),
+                            "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Amount of frames to overlap."})
+                },
+                "optional": {"start_image": ("IMAGE", ),
+                }}
+
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, vae, width, height, length, batch_size,  tile_size, overlap, temporal_size=64, temporal_overlap=8, start_image=None):
+        latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+
+        if start_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return (out_latent,)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            latent_temp = vae.encode_tiled(start_image, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        out_latent = {}
+        latent_format = comfy.latent_formats.Wan22()
+        latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return (out_latent,)