Claude
73adf3e65d
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dy3QqNuYH9NSJgob83xcTX
91 lines
3.8 KiB
Python
91 lines
3.8 KiB
Python
"""Sharpness: win back fine detail late in the run, with any sampler.
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Each step's prediction of the finished picture is pushed away from the previous step's, which keeps the
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detail the smoothing steps wear off. It costs no extra model call (one picture-sized subtraction per
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step, only in the chosen last part of the run). It is a different thing from Detail, which filters the
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picture in space; this one compares steps in time.
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The edit rides into the next step's input (core/input_steer.py), so the last step is never edited on
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packed models. Samplers that call the model twice per step (heun, dpm_2) are not supported: it says so
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in the Log and stands down. Only the picture is touched, never the sound.
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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, streams
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ID = "sharpness"
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TITLE = "Sharpness"
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MOUNT = "generation.sampling"
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STAGE = "guidance"
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CATEGORY = "sampling"
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STATUS = "experimental"
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REQUIRES = ["temporal_latent"]
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SETTINGS = {
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"enabled": {
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"type": "bool", "default": False,
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"label": "Restore fine detail",
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"hint": "Wins back detail late in the run. No extra model calls; not for heun-style samplers.",
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},
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"amount": {
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"type": "float", "default": 0.3, "min": 0.0, "max": 1.0, "step": 0.05,
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"label": "How much", "ui": "slider",
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"hint": "0 = nothing. 0.2 to 0.4 is typical.",
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"when": {"enabled": True},
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},
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"last_part": {
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"type": "float", "default": 0.35, "min": 0.05, "max": 1.0, "step": 0.05,
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"label": "Last part of the run", "ui": "slider",
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"hint": "The share of the steps, counted from the end, that are sharpened.",
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"when": {"enabled": True},
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},
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}
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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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amount, last = float(values.get("amount", 0.3)), float(values.get("last_part", 0.35))
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if amount <= 0.0:
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return "amount 0: nothing to do"
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seen = {} # by cond/uncond call: (step, picture)
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steer = input_steer.Steer("Sharpness")
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steer.attach(patcher, key)
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def forget(executor, *args, **kwargs):
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seen.clear()
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try:
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return executor(*args, **kwargs)
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finally:
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seen.clear() # a latent-sized tensor must not outlive the run
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patcher.add_wrapper_with_key(WrappersMP.OUTER_SAMPLE, f"{key}.forget", forget)
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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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where, split = dit_hooks.current_step(to), streams.video_of(out, named)
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if where is None or split is None or dit_hooks.probing(to):
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return out
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video, rebuild = split
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which = tuple(int(v) for v in ((to or {}).get("cond_or_uncond") or ()))
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before = seen.get(which)
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seen[which] = (where[0], video.detach())
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if (before is None or where[0] < before[0] or before[1].shape != video.shape # nothing to compare, or a new pass
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or not step.steering or where[0] < where[1] * (1.0 - last)):
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return out
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edit = 0.5 * amount * (video - before[1].to(video.dtype))
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if x.dim() == 3: # packed: arrives scaled by (1 - next sigma), sized to move the input as a direct edit would
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sigma, nxt = float(t.max()), float(to["sample_sigmas"][where[0] + 1])
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edit = edit * (sigma - nxt) / max(sigma * (1.0 - nxt), 1e-6)
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return step.keep(out, rebuild(video + edit))
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patcher.add_wrapper_with_key(WrappersMP.APPLY_MODEL, key, apply_model)
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return f"amount {amount:g} on the last {last:.0%} of the steps"
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PROVIDES = {"modifier": install}
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