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* Move dataset/text nodes to text category * Rename category utils into utilities * Rename category api node into partner * Move categories conditioning, latent, sampling, model_patches, training, etc. under model category * Dispatch partner nodes in to 3d, audio, image, text, video categories * Move PreviewAny node to utilities category
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
from comfy import model_management
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from comfy_api.latest import ComfyExtension, IO
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from typing_extensions import override
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import math
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class LTXVLatentUpsampler(IO.ComfyNode):
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"""
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Upsamples a video latent by a factor of 2.
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"""
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="LTXVLatentUpsampler",
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category="model/latent/video",
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is_experimental=True,
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inputs=[
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IO.Latent.Input("samples"),
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IO.LatentUpscaleModel.Input("upscale_model"),
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IO.Vae.Input("vae"),
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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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@classmethod
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def execute(cls, samples, upscale_model, vae) -> IO.NodeOutput:
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"""
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Upsample the input latent using the provided model.
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Args:
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samples (dict): Input latent samples
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upscale_model (LatentUpsampler): Loaded upscale model
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vae: VAE model for normalization
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Returns:
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tuple: Tuple containing the upsampled latent
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"""
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device = model_management.get_torch_device()
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memory_required = model_management.module_size(upscale_model)
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model_dtype = next(upscale_model.parameters()).dtype
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latents = samples["samples"]
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input_dtype = latents.dtype
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memory_required += math.prod(latents.shape) * 3000.0 # TODO: more accurate
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model_management.free_memory(memory_required, device)
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try:
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upscale_model.to(device) # TODO: use the comfy model management system.
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latents = latents.to(dtype=model_dtype, device=device)
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"""Upsample latents without tiling."""
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latents = vae.first_stage_model.per_channel_statistics.un_normalize(latents)
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upsampled_latents = upscale_model(latents)
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finally:
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upscale_model.cpu()
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upsampled_latents = vae.first_stage_model.per_channel_statistics.normalize(
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upsampled_latents
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)
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upsampled_latents = upsampled_latents.to(dtype=input_dtype, device=model_management.intermediate_device())
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return_dict = samples.copy()
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return_dict["samples"] = upsampled_latents
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return_dict.pop("noise_mask", None)
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return IO.NodeOutput(return_dict)
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upsample_latent = execute # TODO: remove
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class LTXVLatentUpsamplerExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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return [LTXVLatentUpsampler]
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async def comfy_entrypoint() -> LTXVLatentUpsamplerExtension:
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return LTXVLatentUpsamplerExtension()
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