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Interactive Guessing: make noise-seed freezing optional

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Add guess_freeze_seed (default True) threaded refine_v2 -> _v2_pack_batch
-> _v2_pack_guess_ladder and the Studio refine_v2 call. Frozen = every rung
shares one seed (only conditioning strength varies; cleanest for learning).
Off = distinct seed per rung (varied compositions too). New "Freeze seed"
toggle in the Interactive Guessing config; persisted on Arm.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-06-04 15:48:01 +03:00
co-authored by Claude Opus 4.8
parent c8e4f2c404
commit e4a74dc3b9
2 changed files with 38 additions and 15 deletions
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conditioning.py
+31 -14
View File
@@ -11481,7 +11481,8 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
value_guidance=True, latent=None, seed_output_connected=False,
steer_mode="relative", absolute_strength=0.6,
_seed=None, _seed_source="fresh seed", _scene_seeds=None, _velocity_keys=None,
batch_variants=1, guess_mode=False, guess_direction="up", guess_range=1.0):
batch_variants=1, guess_mode=False, guess_direction="up", guess_range=1.0,
guess_freeze_seed=True):
seed = int(_seed) if _seed is not None else random.randint(1, 0xffffffffffffffff)
encode_cache = {}
linked_refinement_key = str(refinement_key_input or "").strip()
@@ -12332,7 +12333,8 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
encode_cache, source_image, prompt_to_encode, global_state, learning_profile,
repair_feedback, current_family_slot, _vf_for_memory, _concept_dir,
_concept_strength, _current_final,
guess_mode=guess_mode, guess_direction=guess_direction, guess_range=guess_range)
guess_mode=guess_mode, guess_direction=guess_direction, guess_range=guess_range,
guess_freeze_seed=guess_freeze_seed)
return (
self._v2_finalize_conditioning(output_conditioning, refinement_key, value_guidance, steer_mode, absolute_strength, spread_cap=_guess_spread_cap),
status,
@@ -12361,13 +12363,16 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
video_latent,
)
def _v2_pack_guess_ladder(self, output_conditioning, n, direction, guess_range, seed):
def _v2_pack_guess_ladder(self, output_conditioning, n, direction, guess_range, seed,
freeze_seed=True):
"""Interactive Guessing: build N variants that scale the conditioning's spread (sigma) on
video channels by a LINEAR ramp, freezing everything else INCLUDING the noise seed so the
only variable across the ladder is conditioning strength. Variant 0 = base (factor 1.0);
the last = 1.0 +/- guess_range (up amplifies, down dampens). Each variant records
'funpack_guess_factor' so the rating ladder is interpretable and Phase-3 learning can read
each rung's strength."""
video channels by a LINEAR ramp. Variant 0 = base (factor 1.0); the last = 1.0 +/-
guess_range (up amplifies, down dampens). Each variant records 'funpack_guess_factor' so
the rating ladder is interpretable and Phase-3 learning can read each rung's strength.
freeze_seed=True (default): every rung shares the same noise, so the ONLY variable is
conditioning strength (cleanest for learning the safe ceiling). False: each rung gets a
distinct seed (different compositions too), like a regular batch."""
n = max(2, int(n))
guess_range = abs(float(guess_range or 1.0))
up = str(direction or "up").lower() != "down"
@@ -12382,23 +12387,33 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
m["funpack_batch_variant"] = i
m["funpack_guess_factor"] = round(float(factor), 4)
packed.append((scale_conditioning_spread(cond, factor), m))
# Freeze the noise: every variant shares the same per-scene seeds, so only the spread varies.
base = random.Random(int(seed) & 0xffffffffffffffff).randint(1, 0xffffffffffffffff)
# Seed assignment. Frozen: every rung shares one base seed (only spread varies). Unfrozen:
# a distinct seed per rung (composition varies too).
rng = random.Random(int(seed) & 0xffffffffffffffff)
frozen_base = rng.randint(1, 0xffffffffffffffff)
variant_seed = {}
scene_counter = {}
for entry in packed:
m = entry[1]
vi = int(m.get("funpack_batch_variant", 0))
if freeze_seed:
vseed = frozen_base
else:
if vi not in variant_seed:
variant_seed[vi] = rng.randint(1, 0xffffffffffffffff)
vseed = variant_seed[vi]
si = scene_counter.get(vi, 0)
m["funpack_scene_seed"] = (base + si) & 0xffffffffffffffff
m["funpack_scene_seed"] = (vseed + si) & 0xffffffffffffffff
scene_counter[vi] = si + 1
print(f"[FunPackVideoRefinerV2] Interactive Guessing: packed {n} rungs "
f"(direction={'up' if up else 'down'}, range={guess_range}, seed frozen).")
f"(direction={'up' if up else 'down'}, range={guess_range}, "
f"seed={'frozen' if freeze_seed else 'varied'}).")
return packed
def _v2_pack_batch(self, output_conditioning, n, raw_prompt, seed, clip, encode_cache,
source_image, base_prompt, global_state, learning_profile, repair_feedback,
intent_family_slot, vf, concept_dir, concept_strength, current_final_texts,
guess_mode=False, guess_direction="up", guess_range=1.0):
guess_mode=False, guess_direction="up", guess_range=1.0, guess_freeze_seed=True):
"""Pack N Batch-Training variant entries onto the FINAL conditioning, each tagged
'funpack_batch_variant' so the Chain Sampler samples one chain per variant. Single-scene
output -> re-resolve shortcuts per variant (different option picks). Multi-scene output
@@ -12410,7 +12425,8 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
return output_conditioning
# Interactive Guessing: a conditioning-spread ladder (seed frozen), not shortcut/seed variants.
if guess_mode:
return self._v2_pack_guess_ladder(output_conditioning, n, guess_direction, guess_range, seed)
return self._v2_pack_guess_ladder(output_conditioning, n, guess_direction, guess_range, seed,
freeze_seed=guess_freeze_seed)
# Build the N variant entries. Single-scene output -> _v2_build_batch_variants (re-resolves
# shortcuts per variant). Multi-scene (transition split) -> duplicate the whole scene-set
# per variant. Each variant tags ALL its scene entries with the same index.
@@ -13919,6 +13935,7 @@ class FunPackStudio:
guess_mode=bool(rf.get("guess_mode", False)),
guess_direction=str(rf.get("guess_direction", "up") or "up"),
guess_range=float(rf.get("guess_range", 1.0) or 1.0),
guess_freeze_seed=bool(rf.get("guess_freeze_seed", True)),
reset_session=reset_session,
lora_stack=active_lora_stack,
im_feeling_lucky=im_feeling_lucky,
web/funpack_studio.js
+7 -1
View File
@@ -572,7 +572,7 @@ function openBatchPanel(node) {
modeSelect.addEventListener("change", () => { rf.batch_mode = modeSelect.value; saveSettings(node, settings); render(); });
body.append(row("Mode", modeSelect));
let dirSelect = null, rangeInput = null;
let dirSelect = null, rangeInput = null, freezeToggle = null;
if (rf.batch_mode === "Interactive Guessing") {
if (!rf.guess_direction) rf.guess_direction = "up";
dirSelect = selectEl(["up", "down"], rf.guess_direction);
@@ -582,6 +582,11 @@ function openBatchPanel(node) {
rangeInput = numInput(rf.guess_range, 0.1, 2.0, 0.1);
body.append(el("div", "funpack-studio-hint", "How far the last rung goes: up → 1.0+range (e.g. 1.0 = up to 2.0×). down → 1.0−range (capped above 0). The ladder ramps linearly from the untouched base to this."));
body.append(row("Range", rangeInput));
if (rf.guess_freeze_seed === undefined) rf.guess_freeze_seed = true;
freezeToggle = toggleEl(!!rf.guess_freeze_seed, "Freeze noise seed across rungs");
freezeToggle.inp.addEventListener("change", () => { rf.guess_freeze_seed = freezeToggle.inp.checked; saveSettings(node, settings); });
body.append(el("div", "funpack-studio-hint", "On: every rung shares the same noise, so conditioning strength is the ONLY variable (cleanest for learning the safe ceiling). Off: each rung gets a different seed — varied compositions too."));
body.append(row("Freeze seed", freezeToggle.wrap));
}
if (rf.batch_learning === undefined) rf.batch_learning = true;
@@ -606,6 +611,7 @@ function openBatchPanel(node) {
s.refiner.guess_mode = (modeSelect.value === "Interactive Guessing");
if (dirSelect) s.refiner.guess_direction = dirSelect.value;
if (rangeInput) s.refiner.guess_range = Math.max(0.1, Math.min(2.0, parseFloat(rangeInput.value) || 1.0));
if (freezeToggle) s.refiner.guess_freeze_seed = freezeToggle.inp.checked;
s.refiner.batch_learning = learnToggle.inp.checked;
saveSettings(node, s);
refreshBatchButton(node); render();