Claude
3adebb731f
Old project/workflow names and browser settings migrate on load; /funpack redirects to /fun. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016acRss69eH1235eRdFTZHY
102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
"""Taste guidance: nudge each late step's predicted picture toward what you rated up.
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Every run banks a small fingerprint of its final predicted picture on the taste
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key. Once 10+ rated clips exist, a tiny judge learns to rank them (see the taste
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module's value.py), and over the last half of the steps the prediction is moved
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along the judge's gradient. No extra model pass: one backward through a few
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hundred thousand weights.
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`strength` is a fraction of the picture's own size per fully-open step (0.02 =
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2%): the raw gradient reaches each latent value through a pooling window, so
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used as-is it would be far too small to matter.
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Sound untouched. Refuses batched calls (CFG > 1 puts the unconditioned
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prediction in the same batch; steering their blend steers toward a prediction
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averaged with its own opposite).
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"""
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from comfy.patcher_extension import WrappersMP
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from ..._core import dit_hooks, input_steer, log, registry, streams
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ID = "output_guidance"
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TITLE = "Taste guidance"
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MOUNT = "generation.sampling"
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STAGE = "guidance"
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CATEGORY = "guidance"
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STATUS = "experimental"
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REQUIRES = ["temporal_latent"]
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USES = ["taste_store"]
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SETTINGS = {
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"enabled": {
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"type": "bool", "default": False,
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"label": "Taste guidance",
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"hint": "After 10+ rated clips (some liked, some disliked), nudges the finish of each clip toward what you rated up.",
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},
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"strength": {
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"type": "float", "default": 0.02, "min": 0.0, "max": 0.2, "step": 0.005,
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"label": "Strength", "ui": "slider",
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"hint": "0 = only learn. 0.02 is a gentle start.",
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"when": {"enabled": True},
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},
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}
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KIND = "x0_final"
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NAME = "final"
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def _say(message):
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log.once(f"{ID}:{message}", log.ALERT, "FunUI Taste guidance", message)
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def install(patcher, values, key):
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if not values.get("enabled"):
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return None
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taste = registry.current().ask("taste_store", patcher)
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if taste is None:
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_say("off: set a Taste key first -- it is what your ratings teach")
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return None
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strength = float(values.get("strength", 0.02))
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live = {"judge": None}
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def fresh():
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live["judge"] = taste.judge(KIND, NAME) if strength > 0 else None
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liked, disliked = taste.counts(KIND, NAME)
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log.once(f"{ID}:state", log.INFO, "FunUI Taste guidance",
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f"key {taste.key!r}: " + (f"steering the last half from {liked} liked / {disliked} disliked"
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if live["judge"] is not None else
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f"learning ({liked} liked / {disliked} disliked; "
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"steers from 10 rated clips with both kinds)"))
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captured = taste.collect(patcher, key, KIND, fresh=fresh)
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steer = input_steer.Steer("Taste guidance")
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steer.attach(patcher, key)
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def apply_model(executor, x, t, *args, **kwargs):
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named = streams.model_args(args, kwargs)
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to = named.get("transformer_options")
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step = steer.begin(x, t, to)
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out = executor(step.x, t, *args, **kwargs)
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split = streams.video_of(out, named)
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if split is None:
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_say("off this run: could not find the picture in this model's latent")
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return out
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video, rebuild = split
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if video.shape[0] != 1:
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_say("off this run: batched predictions (CFG above 1) can't be steered honestly")
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return out
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if dit_hooks.last_step(to):
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captured[NAME] = taste.describe(video.detach())
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amount = strength * step.gate
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if live["judge"] is None or amount <= 0.0:
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return out
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return step.keep(out, rebuild(live["judge"].nudge(video, amount)))
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patcher.add_wrapper_with_key(WrappersMP.APPLY_MODEL, key, apply_model)
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return (f"strength {strength:g}, last half of steps; learns every run, "
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"steers from 10 rated clips (read fresh every run)")
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PROVIDES = {"modifier": install}
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