Output width and height from empty latents

comfy_extras/nodes_chroma_radiance.py

@@ -19,13 +19,17 @@ def define_schema(cls) -> io.Schema:
                 io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input(id="batch_size", default=1, min=1, max=4096),
             ],
-            outputs=[io.Latent().Output()],
+            outputs=[
+                io.Latent().Output(),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+            ],
         )
 
     @classmethod
     def execute(cls, *, width: int, height: int, batch_size: int=1) -> io.NodeOutput:
         latent = torch.zeros((batch_size, 3, height, width), device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples":latent})
+        return io.NodeOutput({"samples":latent}, width, height)
 
 
 class ChromaRadianceOptions(io.ComfyNode):

comfy_extras/nodes_cosmos.py

@@ -20,13 +20,17 @@ def define_schema(cls) -> io.Schema:
                 io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
-            outputs=[io.Latent.Output()],
+            outputs=[
+                io.Latent.Output(),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+            ],
         )
 
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent}, width, height)
 
 
 def vae_encode_with_padding(vae, image, width, height, length, padding=0):

comfy_extras/nodes_lt.py

@@ -26,13 +26,15 @@ def define_schema(cls):
             ],
             outputs=[
                 io.Latent.Output(),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
             ],
         )
 
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent}, width, height)
 
 
 class LTXVImgToVideo(io.ComfyNode):

comfy_extras/nodes_mochi.py

@@ -19,13 +19,15 @@ def define_schema(cls):
             ],
             outputs=[
                 io.Latent.Output(),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
             ],
         )
 
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent}, width, height)
 
 
 class MochiExtension(ComfyExtension):

comfy_extras/nodes_sd3.py

@@ -35,14 +35,16 @@ def INPUT_TYPES(s):
         return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                               "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
-    RETURN_TYPES = ("LATENT",)
+    RETURN_TYPES = ("LATENT", "INT", "INT")
+    RETURN_NAMES = ("LATENT", "width", "height")
+    OUTPUT_TOOLTIPS = ("The empty latent image batch.", "The width of the latent images.", "The height of the latent images.")
     FUNCTION = "generate"
 
     CATEGORY = "latent/sd3"
 
     def generate(self, width, height, batch_size=1):
         latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
-        return ({"samples":latent}, )
+        return ({"samples":latent}, width, height)
 
 
 class CLIPTextEncodeSD3:

comfy_extras/nodes_stable_cascade.py

@@ -39,6 +39,8 @@ def define_schema(cls):
             outputs=[
                 io.Latent.Output(display_name="stage_c"),
                 io.Latent.Output(display_name="stage_b"),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
             ],
         )
 
@@ -50,7 +52,9 @@ def execute(cls, width, height, compression, batch_size=1):
             "samples": c_latent,
         }, {
             "samples": b_latent,
-        })
+        },
+        width,
+        height)
 
 
 class StableCascade_StageC_VAEEncode(io.ComfyNode):

nodes.py

@@ -1174,16 +1174,17 @@ def INPUT_TYPES(s):
                 "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."})
             }
         }
-    RETURN_TYPES = ("LATENT",)
-    OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
+    RETURN_TYPES = ("LATENT", "INT", "INT")
+    RETURN_NAMES = ("LATENT", "width", "height")
+    OUTPUT_TOOLTIPS = ("The empty latent image batch.", "The width of the latent images.", "The height of the latent images.")
     FUNCTION = "generate"
 
     CATEGORY = "latent"
     DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling."
 
     def generate(self, width, height, batch_size=1):
         latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
-        return ({"samples":latent}, )
+        return ({"samples":latent}, width, height)
 
 
 class LatentFromBatch: