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Shorter input type name
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@ -10,7 +10,7 @@ from comfy_extras.frame_interpolation_models.ifnet import IFNet, detect_rife_con
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from comfy_extras.frame_interpolation_models.film_net import FILMNet
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from comfy_extras.frame_interpolation_models.film_net import FILMNet
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from comfy_api.latest import ComfyExtension, io
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from comfy_api.latest import ComfyExtension, io
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FrameInterpolationModel = io.Custom("FRAME_INTERPOLATION_MODEL")
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FrameInterpolationModel = io.Custom("INTERP_MODEL")
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class FrameInterpolationModelLoader(io.ComfyNode):
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class FrameInterpolationModelLoader(io.ComfyNode):
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@ -81,7 +81,7 @@ class FrameInterpolate(io.ComfyNode):
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category="image/video",
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category="image/video",
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search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
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search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
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inputs=[
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inputs=[
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FrameInterpolationModel.Input("frame_interpolation_model"),
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FrameInterpolationModel.Input("interp_model"),
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io.Image.Input("images"),
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io.Image.Input("images"),
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io.Int.Input("multiplier", default=2, min=2, max=16),
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io.Int.Input("multiplier", default=2, min=2, max=16),
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],
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],
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@ -91,17 +91,17 @@ class FrameInterpolate(io.ComfyNode):
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)
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)
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@classmethod
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@classmethod
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def execute(cls, frame_interpolation_model, images, multiplier) -> io.NodeOutput:
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def execute(cls, interp_model, images, multiplier) -> io.NodeOutput:
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offload_device = model_management.intermediate_device()
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offload_device = model_management.intermediate_device()
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num_frames = images.shape[0]
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num_frames = images.shape[0]
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if num_frames < 2 or multiplier < 2:
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if num_frames < 2 or multiplier < 2:
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return io.NodeOutput(images)
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return io.NodeOutput(images)
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model_management.load_model_gpu(frame_interpolation_model)
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model_management.load_model_gpu(interp_model)
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device = frame_interpolation_model.load_device
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device = interp_model.load_device
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dtype = frame_interpolation_model.model_dtype()
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dtype = interp_model.model_dtype()
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inference_model = frame_interpolation_model.model
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inference_model = interp_model.model
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# Free VRAM for inference activations (model weights + ~20x a single frame's worth)
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# Free VRAM for inference activations (model weights + ~20x a single frame's worth)
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H, W = images.shape[1], images.shape[2]
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H, W = images.shape[1], images.shape[2]
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