digital-garbageandClaude Opus 5
a81ac48083
Decode now takes the MODEL, so a provider answers "is this mine" by identity instead of by shape. H3 was claiming any latent that happened to have two parts -- right today because it is the only thing producing one, wrong the moment a second AV model exists, and wrong in the way that reads as an unrelated fault rather than an error. It declines when the model is not wired, and the node says that is what is missing. That completes the shape a model module should have: one folder saying how this model behaves at each stage -- what it is, how its latent is built, how its output is decoded -- instead of that knowledge spreading through the conditioning and sampling code as special cases. Added a modifier that runs on SD1.5, because a feature nobody can watch is a feature nobody can check. It declares no traits, so it applies everywhere, which is the other half of the rule ALG demonstrates by narrowing. Measured on this machine at a fixed seed: off and on differ, 0.4 gives a visibly richer picture that still reads correctly, and 1.2 blows the highlights -- so the range is -1..1, set from what was measured rather than from a round number. A slider whose top half destroys the image lies about its range. Two things found while building it. ComfyUI has a real circular import between nodes_latent and nodes_post_processing that resolves only because init_extra_nodes reaches one first; a custom node has to break it the same way. And the accumulation bug returned through a third door: stripping handled keyed wrappers but not the anonymous function lists on model_options, so chaining doubled the effect. Tagging is now generic -- it looks for our mark rather than for a list of hook names that would need keeping current. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
89 lines
4.2 KiB
Python
89 lines
4.2 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.failed(f"{spec.id}.{CAPABILITY}", exc)
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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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