Merge remote-tracking branch 'origin/master' into pysssss/glsl-blueprints

comfy_extras/nodes_color.py

@@ -0,0 +1,42 @@
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class ColorToRGBInt(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ColorToRGBInt",
+            display_name="Color to RGB Int",
+            category="utils",
+            description="Convert a color to a RGB integer value.",
+            inputs=[
+                io.Color.Input("color"),
+            ],
+            outputs=[
+                io.Int.Output(display_name="rgb_int"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        color: str,
+    ) -> io.NodeOutput:
+        # expect format #RRGGBB
+        if len(color) != 7 or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB")
+        r = int(color[1:3], 16)
+        g = int(color[3:5], 16)
+        b = int(color[5:7], 16)
+        return io.NodeOutput(r * 256 * 256 + g * 256 + b)
+
+
+class ColorExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [ColorToRGBInt]
+
+
+async def comfy_entrypoint() -> ColorExtension:
+    return ColorExtension()

comfy_extras/nodes_hunyuan.py

@@ -56,7 +56,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples":latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
 
     generate = execute  # TODO: remove
 
@@ -73,7 +73,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         # Using scale factor of 16 instead of 8
         latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
 
 
 class HunyuanVideo15ImageToVideo(io.ComfyNode):