digital-garbage
65b35e9cf3
The wording fix a commit ago was written by hand in two files and the same mislabel was still sitting in four others -- traits, chain, modifier install and node schema all reported a thing that loaded, recognised its work and then raised as one that "did not load", sending a reader after an import error that never happened. Three hand-written strings would have drifted, so log.broke() now says it once and every site calls it. Two of the new tests were vacuous when first written -- "nothing said did not load" is also true where nothing ran at all, and picking the first provider found lands on one whose traits exclude the test's model. Both assert the line is present before asserting the wrong line is not. One finding, confirmed, plus four sites it did not name.
89 lines
4.3 KiB
Python
89 lines
4.3 KiB
Python
"""Latent to pictures, and to sound where there is any.
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This is the only part of getting a result out that FunPack needs to own. Core
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already turns images into a video (`CreateVideo`) and writes the file
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(`SaveVideo`, `SaveWEBM`, `SaveImage`), and those handle containers, codecs and
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audio muxing properly -- writing our own would be duplication with a worse
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version of somebody else's tested code. VHS is unnecessary either way.
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What core cannot generalise is the decode itself for a model whose latent is not
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one tensor. An AV latent carries a video branch and an audio branch that need
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different VAEs, and how they are arranged is the model's business. So the same
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rule as the empty latent applies: a model's own module may claim the decode, and
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otherwise it is the ordinary single-tensor path.
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The node takes the MODEL for one reason: so a provider can answer "is this mine"
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by IDENTITY rather than by shape. Without it, the H3 module claimed any latent
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that happened to have two parts -- correct today, because H3 is the only thing
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producing one, and silently wrong the moment a second AV model exists. Deciding
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from shape is how a model mismatch turns into an unrelated-looking fault instead
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of an error.
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"""
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from comfy_api.latest import io
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from ..._core import log, registry as registry_mod
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CAPABILITY = "decode"
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class FunPackDecode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="FunPackDecode",
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display_name="FunPack Decode",
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category="FunPack/Output",
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description="Decode a latent to images, and to audio when the model has any.",
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inputs=[
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io.Latent.Input("samples"),
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io.Vae.Input("vae", tooltip="The picture VAE."),
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io.Model.Input("model", optional=True,
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tooltip="The model this latent came from. Needed only when a "
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"model has its own way of being decoded."),
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io.Vae.Input("audio_vae", optional=True,
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tooltip="Only for models that generate sound alongside the video."),
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],
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outputs=[
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io.Image.Output(display_name="images"),
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io.Audio.Output(display_name="audio"),
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io.String.Output(display_name="status"),
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],
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)
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@classmethod
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def execute(cls, samples, vae, model=None, audio_vae=None) -> io.NodeOutput:
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latent = samples["samples"]
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for spec, decode in registry_mod.current().providers(CAPABILITY):
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try:
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claimed = decode(latent, model=model, vae=vae, audio_vae=audio_vae)
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except Exception as exc: # noqa: BLE001
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# Same protocol as the empty latent: returning None means "not my
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# model", so getting this far means it WAS and it broke. Falling
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# back would decode a nested latent as if it were one tensor.
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log.broke(f"{spec.id}.{CAPABILITY}", exc, "decoding this model's latent")
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raise RuntimeError(
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f"{spec.id} handles this model's decode and failed: "
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f"{type(exc).__name__}: {exc}. Refusing to decode it as a plain "
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f"latent, which would not be the same picture."
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) from exc
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if claimed is not None:
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images, audio = claimed
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return io.NodeOutput(images, audio, f"{spec.id} decoded this")
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if getattr(latent, "is_nested", False):
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# Nothing claimed it and it is not one tensor: vae.decode would either
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# raise somewhere confusing or quietly decode the wrong branch.
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raise RuntimeError(
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"This latent has more than one part and no installed module knows "
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"how to decode it."
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+ ("" if model is not None else
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" The model input is not wired, so no module could recognise it.")
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+ " The module for this model is missing.")
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images = vae.decode(latent)
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if len(images.shape) == 5: # a batch of clips -> a strip of frames
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images = images.reshape(-1, *images.shape[-3:])
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return io.NodeOutput(images, None, "decoded as a single latent")
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