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convert nodes_mochi.py to V3 schema (#10069)
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@ -1,23 +1,40 @@
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import nodes
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from typing_extensions import override
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import torch
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import torch
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import comfy.model_management
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import comfy.model_management
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import nodes
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from comfy_api.latest import ComfyExtension, io
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class EmptyMochiLatentVideo:
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class EmptyMochiLatentVideo(io.ComfyNode):
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@classmethod
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@classmethod
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def INPUT_TYPES(s):
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def define_schema(cls):
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return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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return io.Schema(
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"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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node_id="EmptyMochiLatentVideo",
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"length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}),
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category="latent/video",
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
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inputs=[
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RETURN_TYPES = ("LATENT",)
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io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
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FUNCTION = "generate"
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io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("length", default=25, min=7, max=nodes.MAX_RESOLUTION, step=6),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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],
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outputs=[
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io.Latent.Output(),
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],
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)
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CATEGORY = "latent/video"
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@classmethod
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def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
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def generate(self, width, height, length, batch_size=1):
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latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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return ({"samples":latent}, )
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return io.NodeOutput({"samples": latent})
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NODE_CLASS_MAPPINGS = {
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"EmptyMochiLatentVideo": EmptyMochiLatentVideo,
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class MochiExtension(ComfyExtension):
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}
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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EmptyMochiLatentVideo,
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]
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async def comfy_entrypoint() -> MochiExtension:
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return MochiExtension()
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