convert nodes_rebatch.py to V3 schema (#9945)

comfy_extras/nodes_rebatch.py

@@ -1,18 +1,25 @@
+from typing_extensions import override
 import torch
 
-class LatentRebatch:
+from comfy_api.latest import ComfyExtension, io
+
+
+class LatentRebatch(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "latents": ("LATENT",),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              }}
-    RETURN_TYPES = ("LATENT",)
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True, )
-
-    FUNCTION = "rebatch"
-
-    CATEGORY = "latent/batch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RebatchLatents",
+            display_name="Rebatch Latents",
+            category="latent/batch",
+            is_input_list=True,
+            inputs=[
+                io.Latent.Input("latents"),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(is_output_list=True),
+            ],
+        )
 
     @staticmethod
     def get_batch(latents, list_ind, offset):
@@ -53,7 +60,8 @@ class LatentRebatch:
         result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
         return result
 
-    def rebatch(self, latents, batch_size):
+    @classmethod
+    def execute(cls, latents, batch_size):
         batch_size = batch_size[0]
 
         output_list = []
@@ -63,24 +71,24 @@ class LatentRebatch:
         for i in range(len(latents)):
             # fetch new entry of list
             #samples, masks, indices = self.get_batch(latents, i)
-            next_batch = self.get_batch(latents, i, processed)
+            next_batch = cls.get_batch(latents, i, processed)
             processed += len(next_batch[2])
             # set to current if current is None
             if current_batch[0] is None:
                 current_batch = next_batch
             # add previous to list if dimensions do not match
             elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
-                sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+                sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
                 output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
                 current_batch = next_batch
             # cat if everything checks out
             else:
-                current_batch = self.cat_batch(current_batch, next_batch)
+                current_batch = cls.cat_batch(current_batch, next_batch)
 
             # add to list if dimensions gone above target batch size
             if current_batch[0].shape[0] > batch_size:
                 num = current_batch[0].shape[0] // batch_size
-                sliced, remainder = self.slice_batch(current_batch, num, batch_size)
+                sliced, remainder = cls.slice_batch(current_batch, num, batch_size)
 
                 for i in range(num):
                     output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
@@ -89,7 +97,7 @@ class LatentRebatch:
 
         #add remainder
         if current_batch[0] is not None:
-            sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+            sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
             output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
 
         #get rid of empty masks
@@ -97,23 +105,27 @@ class LatentRebatch:
             if s['noise_mask'].mean() == 1.0:
                 del s['noise_mask']
 
-        return (output_list,)
+        return io.NodeOutput(output_list)
 
-class ImageRebatch:
+class ImageRebatch(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "images": ("IMAGE",),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              }}
-    RETURN_TYPES = ("IMAGE",)
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True, )
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RebatchImages",
+            display_name="Rebatch Images",
+            category="image/batch",
+            is_input_list=True,
+            inputs=[
+                io.Image.Input("images"),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Image.Output(is_output_list=True),
+            ],
+        )
 
-    FUNCTION = "rebatch"
-
-    CATEGORY = "image/batch"
-
-    def rebatch(self, images, batch_size):
+    @classmethod
+    def execute(cls, images, batch_size):
         batch_size = batch_size[0]
 
         output_list = []
@@ -125,14 +137,17 @@ class ImageRebatch:
         for i in range(0, len(all_images), batch_size):
             output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
 
-        return (output_list,)
+        return io.NodeOutput(output_list)
 
-NODE_CLASS_MAPPINGS = {
-    "RebatchLatents": LatentRebatch,
-    "RebatchImages": ImageRebatch,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "RebatchLatents": "Rebatch Latents",
-    "RebatchImages": "Rebatch Images",
-}
+class RebatchExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LatentRebatch,
+            ImageRebatch,
+        ]
+
+
+async def comfy_entrypoint() -> RebatchExtension:
+    return RebatchExtension()