Merge latest upstream Long Videos

H3_LONGVIDEOS_UPSTREAM_LICENSE.txt

@@ -0,0 +1,53 @@
+H3-LongVideos — Licence
+Copyright (c) 2026 Smite79. All rights reserved.
+
+This licence applies to every version published on or after 2026-09-07.
+
+WHAT YOU MAY DO
+
+  1. Download and use this software, in unmodified or modified form, for your
+     own purposes, personal or commercial. Rendering with it, and whatever you
+     render with it, is yours and is not covered by this licence.
+
+  2. Modify your own copy.
+
+  3. Submit changes back to the original project.
+
+WHAT YOU MAY NOT DO WITHOUT WRITTEN PERMISSION
+
+  4. Redistribute this software, in whole or in part, modified or unmodified.
+     That includes publishing it to any repository, registry, model hub, node
+     manager, marketplace, or mirror; bundling it inside another package,
+     product, image, or installer; and hosting it as a service.
+
+  5. Remove, alter, or obscure the copyright notice above, this licence, or the
+     attribution in the source files — including where a permitted redistribution
+     has been agreed.
+
+  6. Represent this software, or a derivative of it, as your own work.
+
+ASKING
+
+  Permission for anything under 4 is granted case by case and is usually given
+  for things like inclusion in a node manager. Ask via the project's GitHub
+  issues at https://github.com/Smite79/MiniMax-H3-LongVideos.
+
+EARLIER VERSIONS
+
+  Versions published before 2026-09-07 were released under Apache License 2.0.
+  That grant is irrevocable for those versions: copies obtained under it stay
+  under it, and this licence does not and cannot withdraw it retroactively. It
+  governs this version and every version after it.
+
+  Apache 2.0 also required attribution, so a copy of an earlier version
+  republished with the copyright notice stripped was already in breach of the
+  licence it was taken under.
+
+NO WARRANTY
+
+  This software is provided "as is", without warranty of any kind, express or
+  implied, including but not limited to the warranties of merchantability,
+  fitness for a particular purpose, and non-infringement. In no event shall the
+  copyright holder be liable for any claim, damages, or other liability, whether
+  in an action of contract, tort, or otherwise, arising from, out of, or in
+  connection with the software or the use or other dealings in the software.

README.md

@@ -33,35 +33,32 @@
   - Outputs: `plan`, `image1`..`image9`, `connected_images`
   - Reads back the nine optional images for a selected MiniMax H3 plan scene, for example by connecting the current `clip_index`.
 
-- `Dumas H3 Long Videos (FL2VA + REF2VA)`
-  - Inputs: H3 model stack, prompt socket, optional `first_frame`, optional `ref_1`..`ref_9`, plus the upstream long-video control surface for pacing, continuity, audio, overlays, and guards
-  - Outputs: `images`, `audio`, `info`, `script`, `frames_per_shot`, `total_frames`, `shots`, `video_seconds`, `fps`, `fps_int`, `latent`, `soundscape`
-  - First-pass Dumas port of the `MiniMax-H3-Longvideos` sampler, brought in as a local starting point for long-form H3 chaining work.
-  - Keeps the upstream split-beats / handoff / ref-routing behavior close to source so future Dumas-specific improvements can be compared against a known baseline.
-  - Full user guide: [`H3_LONG_VIDEOS_GUIDE.md`](./H3_LONG_VIDEOS_GUIDE.md)
-  - Only the canonical `DumasH3LongVideos` node key is exposed now; the older FL2VA/REF2VA alias entries are no longer duplicated in the Add Node menu.
-  - Prompt `<Picture N>` tags now map to the actual ref socket numbers you wire, even with gaps such as only `ref_2` and `ref_7` connected.
-  - Character refs now contribute appearance and wardrobe context from the same structured object, while location refs contribute environment context from theirs.
-  - The default ref2v bias is now stronger: `ref_mode` defaults to `auto ref2v` so untagged prompts condition every shot instead of only shot 1, and `ref_noise_aug` defaults to `0.95` rather than the upstream-literal `0.999`.
-  - `Dumas H3 Latent Upscale Params` provides the optional pre-decode latent refinement stage for the long-video node.
-  - Per-shot directives now support `continuity:`, `ref_mode:`, `ref_noise_aug:`, `anchor_add:`, `soundscape:`, and `music:` in addition to the existing timing and wardrobe directives.
+- `Dumas H3 Long Videos`
+  - Inputs/outputs: the current upstream `MiniMax-H3-Longvideos` sampler surface, exposed under the existing `DumasH3LongVideos` key for saved Dumas workflows.
+  - The local Dumas prompt-engineering fork has been removed from this node. Long Videos now wraps the upstream sampler/engine/runtime/audio/conditioning/shot-plan modules directly so it can track the source project again.
+  - Upstream compatibility keys `H3LongVideos`, `H3LongVideosFL2VA`, `H3LongVideosV1`, and `H3LongVideosREF2VA` are also registered to the same class.
+  - The old Dumas browser widget grouping script is disabled for this node because it targeted controls that no longer exist on the upstream sampler.
+  - `handoff_frames` extends the upstream last-frame handoff: `1` keeps the current single keyframe behavior; higher values keep that final-frame keyframe and add earlier tail frames from the previous shot as claimed reference context for the next beat.
+  - Upstream license text is included in [`H3_LONGVIDEOS_UPSTREAM_LICENSE.txt`](./H3_LONGVIDEOS_UPSTREAM_LICENSE.txt).
 
 - `Dumas H3 Latent Upscale Params`
   - Inputs: `mode`, `model_name`, `method`, `width`, `height`, `device`, `precision`, `sampler_name`, `scheduler`, `steps`, `denoise`, `megapixels`, `tile_width`, `tile_height`, `overlap`, `fade_width`, `fade_height`, `overlap_mode`, `overlap_blend`, `tile_size_mode`, `grid_rows`, `grid_cols`, `spatial_w_overlap`, `spatial_h_overlap`, `min_tile_size`, `masked_area_noise`, `brightness_match`, `dynamic_fade`, `dynamic_fade_min`, `chunk_length`, `temporal_overlap`, `resize_conditioning`, `anchor_strength`
   - Output: `latent_upscale_param`
-  - Bundles the optional latent-space upscaler settings used by `Dumas H3 Long Videos` before decode, so the main node can rebuild conditioning at the target size and run a short refinement pass with your chosen sampler, scheduler, step count, denoise, and the full upstream spatial split controls.
+  - Legacy helper from the abandoned Dumas Long Videos fork. The current upstream-backed `Dumas H3 Long Videos` node does not consume this socket; it uses the upstream latent-upscale controls on the Long Videos node itself.
 
 - `Dumas H3 Beat Prompt`
   - Inputs: authored through the custom front-end beat editor
   - Output: `prompt`
-  - Builds one H3 prompt block per beat, with quick controls for per-shot timing, continuity, ref behavior, anchor additions, soundscape, and music while staying compatible with direct text editing.
+  - Builds an upstream-compatible Long Videos prompt: optional scene paragraph, optional character sheet, then one blank-line-separated textbox per beat.
+  - Per-beat helpers only emit upstream-supported state directives: `remove:` / `removed:` / `off:` and `add:` / `wear:` / `wearing:`.
+  - Old Dumas-only beat directives such as `seconds:`, `continuity:`, `ref_mode:`, `ref_noise_aug:`, `anchor_add:`, `soundscape:`, and `music:` are stripped from the generated prompt so they are not sent to the upstream node as visible text.
 
 - `Dumas H3 Prompt Curator`
   - Inputs: `action_prompt`, `anatomy_guard`, `subject_count_guard`, optional `anchor`, optional `soundscape`, optional `bgm`, optional `ref_1` through `ref_9`
-  - Outputs: `prompt`, `ref_image_1` through `ref_image_9`, `reference_count`, `debug`, `anchor`, `sounds`, `bgm`, `original_ref_1` through `original_ref_9`
+  - Outputs: `prompt`, `ref_image_1` through `ref_image_9`, `reference_count`, `debug`, `anchor`, `sounds`, `bgm`, `original_ref_1` through `original_ref_9`, `compiled_ref_description_1` through `compiled_ref_description_9`
   - Builds one standalone MiniMax H3 prompt from your final action text plus structured character/location references.
   - The action text can mention references by character/location name, alias, `<Picture N>`, or `<refN>`. Only mentioned references are emitted, and the output images are compacted/renumbered so skipped inputs do not leave gaps.
-  - Extra component outputs expose the cleaned anchor, sounds, BGM, and each selected original reference image in compacted order.
+  - Extra component outputs expose the cleaned anchor, sounds, BGM, and each selected original reference image plus its compiled reference description in compacted order.
   - Adds curated reference context, anatomy guard text, optional subject-count guard text, anchor/style text, `overall_soundscape:` text, and `background_music:` text while respecting MiniMax H3's reference-generation shape: one prompt plus up to nine reference images.
 
 - `Dumas H3 Shot Length`

dumas_h3_beat_prompt.py

@@ -2,11 +2,46 @@ import json
 
 
 _DEFAULT_BEAT = "Describe this beat."
-_DEFAULT_STATE = {"beats": [{"text": _DEFAULT_BEAT}]}
+_DEFAULT_STATE = {"scene": "", "character_sheet": "", "beats": [{"text": _DEFAULT_BEAT}]}
+_LEGACY_DIRECTIVE_PREFIXES = (
+    "seconds",
+    "duration",
+    "continuity",
+    "ref_mode",
+    "ref_noise_aug",
+    "anchor_add",
+    "overall_soundscape",
+    "soundscape",
+    "non_diegetic_music",
+    "music",
+    "wardrobe",
+    "enter",
+    "exit",
+)
 
 
 def _clone_default_state():
-    return {"beats": [{"text": _DEFAULT_BEAT}]}
+    return {
+        "scene": "",
+        "character_sheet": "",
+        "beats": [{"text": _DEFAULT_BEAT}],
+    }
+
+
+def _strip_legacy_directives(text):
+    """Remove directives from the abandoned Dumas Long Videos fork.
+
+    The upstream Long Videos node sends unknown field labels to the model as text,
+    so this builder strips the old managed controls rather than emitting prompts
+    that ask H3 to draw labels such as "seconds:" or "music:" in the frame.
+    """
+    kept = []
+    for line in str(text or "").splitlines():
+        lowered = line.strip().lower()
+        if any(lowered.startswith(f"{name}:") for name in _LEGACY_DIRECTIVE_PREFIXES):
+            continue
+        kept.append(line)
+    return "\n".join(kept).strip()
 
 
 def _parse_beat_prompt_state(value):
@@ -21,6 +56,8 @@ def _parse_beat_prompt_state(value):
         except Exception:
             return _clone_default_state()
 
+    scene = str(raw.get("scene") or "")
+    character_sheet = str(raw.get("character_sheet") or "")
     beats = []
     for item in list(raw.get("beats") or []):
         if isinstance(item, dict):
@@ -30,15 +67,25 @@ def _parse_beat_prompt_state(value):
         beats.append({"text": text})
 
     if not beats:
-        return _clone_default_state()
-    return {"beats": beats}
+        beats = [{"text": _DEFAULT_BEAT}]
+    return {
+        "scene": scene,
+        "character_sheet": character_sheet,
+        "beats": beats,
+    }
 
 
 def _assemble_beat_prompt(state):
     parsed = _parse_beat_prompt_state(state)
     chunks = []
+    scene = str(parsed.get("scene") or "").strip()
+    if scene:
+        chunks.append(scene)
+    character_sheet = str(parsed.get("character_sheet") or "").strip()
+    if character_sheet:
+        chunks.append(character_sheet)
     for beat in parsed["beats"]:
-        text = str(beat.get("text") or "").strip()
+        text = _strip_legacy_directives(beat.get("text") or "")
         if text:
             chunks.append(text)
     return "\n\n".join(chunks)
@@ -46,9 +93,9 @@ def _assemble_beat_prompt(state):
 
 class DumasH3BeatPromptNode:
     DESCRIPTION = (
-        "Build a MiniMax H3 prompt from one textbox per beat, with a front-end beat "
-        "editor that can append directive examples and expose per-shot controls for "
-        "timing, continuity, ref behavior, anchor additions, soundscape, and music."
+        "Build an upstream MiniMax H3 Long Videos prompt: optional scene paragraph, "
+        "optional character sheet, then one blank-line-separated textbox per beat. "
+        "Per-beat helpers only emit directives the upstream node understands."
     )
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("prompt",)

dumas_h3_longvideos_audio.py

@@ -0,0 +1,319 @@
+# H3-LongVideos -- https://github.com/Smite79/MiniMax-H3-LongVideos
+# Copyright (c) 2026 Smite79. All rights reserved.
+# Redistribution, in whole or in part, requires written permission.
+# This notice may not be removed or altered. See LICENSE.
+"""Audio policy shared by conditioning and soundtrack assembly."""
+
+from dataclasses import dataclass
+
+import torch
+import comfy.nested_tensor
+from h3_runtime import temporal_shape
+
+
+@dataclass(frozen=True)
+class ShotAudio:
+    speech: bool
+    sounded: bool
+    voiced_only: bool
+    silence_enabled: bool
+    lead_seconds: float
+    latent_fps: int
+    # The tail. Everything after the line's expected end is pinned the way the lead
+    # pins everything before its start. All three default off, so a ShotAudio built
+    # the old way -- six positional arguments -- behaves exactly the old way.
+    line_seconds: float = 0.0      # planner's estimate of the spoken line
+    tail_seconds: float = 0.0      # free audio kept after that estimate; 0 = no tail pin
+    frame_count: int = 0           # the shot in pixel frames; the audio T comes from it
+
+    @property
+    def pinned(self):
+        return self.silence_enabled and not self.speech and not self.sounded
+
+    @property
+    def lead_frames(self):
+        if not self.speech or self.lead_seconds <= 0:
+            return 0
+        return round(self.lead_seconds * self.latent_fps)
+
+    @property
+    def tail_frames(self):
+        """Audio latent frames pinned at the END of a dialogue shot.
+
+        The lead pins the opening so the line cannot start early; nothing pinned the
+        close, and a 2s line in a 9s shot left 7s of open branch in a shot the model
+        knows has a voice in it -- which is where speech carries on past the line, or
+        doubles it. The free span is lead + the line's estimate + tail_seconds; the
+        rest is held at encoded silence. The model chooses WHEN to speak, so the
+        margin is the author's dial: a clipped word costs more than a second of babble.
+        Off unless the shot speaks, the margin is set, and at least half a second would
+        be pinned -- a sliver is not worth the risk of clipping."""
+        if (not self.speech or self.tail_seconds <= 0 or self.line_seconds <= 0
+                or self.frame_count <= 0):
+            return 0
+        total = temporal_shape(self.frame_count)[2]
+        free = self.lead_frames + round((self.line_seconds + self.tail_seconds) * self.latent_fps)
+        tail = total - free
+        return tail if tail >= round(0.5 * self.latent_fps) else 0
+
+
+_SILENT_UNIT = {"lat": None, "key": None}
+
+# ---------------------------------------------------------------------------
+# NOTHING HERE IS SYNTHESISED ANY MORE. Removed on the report: "Just get rid of
+# the ambient sounds all together. They sound horrid. Go back to the model's
+# natural audio."
+#
+# What was here built the soundtrack's non-vocal half out of shaped noise: a room
+# tone from the scene's own wording (synth_ambient, over a table of recipes, with
+# plain_bed under it as a floor) and 21 foley recipes laid into the shots whose
+# audio branch is pinned to silence (foley_for, over _hits/_band/_room and later
+# _contact/_flow/_creak, timed off the picture's own movement for footsteps).
+#
+# It went in because a shot pinned to silence cannot get audio from the model at
+# all -- prompt text never opens a branch -- so auto_sound was writing sounds into
+# prompts that could not make them. That reasoning was sound and the thing it built
+# still did not pass: reported first as footsteps sounding like heartbeats and a
+# bathroom that tapped, and then, once both of those measured clean, as horrid
+# anyway. Synthesis that measures right and sounds wrong is the end of that road.
+#
+# So the audio is the model's, whole. H3 is a joint model and the audio branch is
+# where its sound comes from; the prompt still describes what a shot sounds like,
+# which is the half that was always doing the real work.
+#
+# The consequence, which is real and is reported in info rather than left to be
+# discovered: a shot with no line and no sound you wrote is pinned to silence and
+# is now SILENT. The pin is not a bug and is deliberately untouched -- it is what
+# stops a free branch filling itself with babble and a face lip-syncing to it.
+# Write the sound into the beat to open the branch on purpose, or wire a recording
+# to ambient_audio, which is played under the finished track and conditions
+# nothing. mix_ambient below is that path, and it is all that is left here.
+# ---------------------------------------------------------------------------
+
+
+def _seamless_loop(x, n, sr):
+    """[C, M] -> [C, n], looped with a crossfade so the join does not click.
+
+    Plain tiling puts a discontinuity at every repeat, once per loop length. In a
+    bed that is meant to sit under everything unnoticed, a regular click is the one
+    thing that gets noticed -- the same objection that made the silence latent
+    ping-pong its interior rather than tile it. Here the material is real audio
+    being PLAYED rather than a latent being conditioned on, so it cannot be
+    reversed: a room tone read backwards is fine, but footsteps are not. Crossfade
+    instead, which works on both."""
+    m = int(x.shape[-1])
+    if m <= 0:
+        return None
+    if m >= n:
+        return x[..., :n]
+    fade = min(int(0.25 * sr), m // 4)
+    if fade < 1:
+        reps = -(-n // m)
+        return x.repeat(1, reps)[..., :n]
+    # OVERLAP-ADD the tail onto the head, and shorten the unit by the overlap. The
+    # unit then runs x[m-fade] .. x[m-fade-1], so tiling it steps between samples
+    # that were adjacent in the source and there is no discontinuity anywhere.
+    #
+    # Measured, because the obvious construction is wrong: appending the crossfade
+    # to the END of a full-length unit leaves it finishing on x[fade-1] while the
+    # next repeat starts on x[0], which are not adjacent -- a 2s tone that does not
+    # divide evenly gave a 64x jump at the join, worse than plain tiling's 41x.
+    t = torch.linspace(0.0, 1.0, fade, dtype=x.dtype, device=x.device)
+    head = x[..., :fade] * t + x[..., m - fade:] * (1.0 - t)
+    unit = torch.cat([head, x[..., fade:m - fade]], dim=-1)
+    if int(unit.shape[-1]) < 1:
+        reps = -(-n // m)
+        return x.repeat(1, reps)[..., :n]
+    reps = -(-n // int(unit.shape[-1]))
+    return unit.repeat(1, reps)[..., :n]
+
+
+def mix_ambient(audio, sr, bed, level):
+    """Lay an ambient bed UNDER a finished soundtrack. -> (waveform, note).
+
+    The bed is PLAYED, not conditioned on: it is the file, at the level asked for,
+    under whatever the model generated. That is the whole reason to do it here
+    rather than in the sampler -- ambience needs no cooperation from a joint model,
+    has nothing to lip-sync to, and so cannot put a voice in a wordless shot. The
+    conditioning path can only steer the branch toward something bed-LIKE, and on a
+    shot with a line it competes with the line.
+
+    Defensive throughout, like the silence latent: any failure returns the audio
+    untouched with a note saying so, because a bed is a nicety and a render is not.
+    """
+    try:
+        if audio is None or bed is None or float(level or 0.0) <= 0.0:
+            return audio, ""
+        w = bed.get("waveform") if isinstance(bed, dict) else None
+        if w is None or not int(getattr(w, "ndim", 0)):
+            return audio, ("ambient_audio is wired but carries no waveform, so nothing "
+                           "was laid under the soundtrack")
+        w = w[0] if w.dim() == 3 else w              # [B, C, M] -> [C, M]
+        if w.dim() != 2 or w.shape[-1] < 2:
+            return audio, ("ambient_audio is too short to loop, so nothing was laid "
+                           "under the soundtrack")
+        w = w.detach().to(dtype=audio.dtype, device=audio.device)
+        b_sr = int((bed.get("sample_rate") if isinstance(bed, dict) else 0) or 0)
+        # RESAMPLE, or the bed plays at the wrong speed and pitch. Linear is coarse
+        # for music and inaudible on a room tone, which is what this input is for.
+        resampled = ""
+        if b_sr > 0 and b_sr != int(sr):
+            want = max(2, int(round(w.shape[-1] * float(sr) / float(b_sr))))
+            w = torch.nn.functional.interpolate(
+                w.unsqueeze(0), size=want, mode="linear", align_corners=False)[0]
+            resampled = f", resampled from {b_sr} Hz"
+        ch = int(audio.shape[1])
+        if int(w.shape[0]) != ch:
+            w = (w.mean(dim=0, keepdim=True).repeat(ch, 1) if int(w.shape[0]) > ch
+                 else w[:1].repeat(ch, 1))
+        n = int(audio.shape[-1])
+        loop = _seamless_loop(w, n, int(sr))
+        if loop is None:
+            return audio, ""
+        out = audio + loop.unsqueeze(0) * float(level)
+        # NORMALISE rather than clip. Clipping a bed that pushed a loud line over
+        # the top distorts the LINE, which is the thing worth keeping.
+        peak = float(out.abs().max())
+        gain = ""
+        if peak > 1.0:
+            out = out / peak
+            gain = f", and the mix was scaled by {1.0 / peak:.2f} to stop it clipping"
+        secs = w.shape[-1] / float(sr)
+        return out, (f"an ambient bed was laid under the whole soundtrack at level "
+                     f"{float(level):.2f} -- {secs:.1f}s of audio{resampled}, looped "
+                     f"with a crossfade so the join does not click{gain}. It is your "
+                     f"file, played under what the model generated: it conditions "
+                     f"nothing, so it cannot put a voice in a wordless shot the way "
+                     f"an inferred bed did. Shots pinned to silence keep their silent "
+                     f"conditioning and get the bed on top, which is what makes a "
+                     f"wordless shot sound like a room instead of a mute")
+    except Exception as exc:
+        return audio, (f"the ambient bed could not be mixed ({type(exc).__name__}), so "
+                       f"the soundtrack is unchanged")
+
+
+_SILENCE_STATUS = {"asked": 0, "applied": 0, "why": ""}
+
+
+_SILENT_SECONDS = 2
+
+
+_SILENT_EDGE = 4
+
+
+def _silent_audio_latent(audio_vae, frame_count, fps):
+    """A keyframe audio latent of actual SILENCE, or None if it cannot be made.
+
+    H3 is a JOINT model: the mouth follows the audio branch. On a shot with no
+    scripted line the branch is otherwise unconditioned, and an unconditioned audio
+    branch invents a voice -- which the picture then lip-syncs to. The lips-closed
+    sentence is arguing with a stream that has already decided someone is talking.
+
+    REBUILT 2026-09-05, from measurements against the real VAE rather than from
+    reasoning. The previous version encoded one second, kept a SINGLE interior
+    frame and repeated it, on the argument that silence is homogeneous. It is not,
+    in latent space: encoded silence has genuine frame-to-frame variation (delta
+    mean 0.002-0.004, max 0.021), and a repeated frame has a delta of exactly
+    0.000000. That is a flat signal no encoder produces, and a model handed
+    conditioning outside its own distribution has every reason to disregard it --
+    which is an audio branch back to inventing a voice, with the report saying
+    silence went on.
+
+    The fix that version was avoiding is real too: tiling the whole encoded second
+    end to end leaves a 25x spike at each join (0.554 against 0.022), once per
+    second, which is a metronome in the conditioning of a joint model.
+
+    So: encode two seconds, drop the padded ends, and PING-PONG the interior --
+    forward, reversed, forward. Every join repeats a frame, so there is no seam,
+    and the interior statistics are the encoder's own. Measured over a 9s shot:
+
+        one frame repeated   peak 0.000686   delta mean 0.000000   max 0.000000
+        whole 2s tiled       peak 0.000314   delta mean 0.017451   max 0.554715
+        interior ping-pong   peak 0.000566   delta mean 0.002039   max 0.021159
+
+    where the encoder's own interior is mean 0.0021, max 0.0212. Decoded peak
+    0.000566 on a +/-1.0 scale is about -65 dBFS: silence.
+
+    Everything here stays defensive. Shapes are CHECKED against what the layout
+    expects rather than assumed, and any failure returns None so the shot falls
+    back to an unconditioned branch instead of breaking the render -- the caller
+    reports when that happens, so it is no longer a silent failure.
+    """
+    try:
+        sr = int(getattr(audio_vae, "audio_sample_rate", 0) or 0)
+        if sr <= 0:
+            return None
+        _, _, want_t = temporal_shape(frame_count, fps)
+        if want_t <= 0:
+            return None
+        key = (id(audio_vae), sr)
+        block = _SILENT_UNIT.get("lat") if _SILENT_UNIT.get("key") == key else None
+        if block is None:
+            # CHANNELS LAST. comfy.sd.VAE.encode() does `pixel_samples.movedim(-1, 1)`
+            # before handing off, so the audio VAE -- which wants [B, 2, L] -- must be
+            # given [B, L, 2]. Passing [B, 2, L] raises inside the encoder, and an
+            # early version did exactly that: swallowed by the guard below, so the
+            # whole layer silently did nothing.
+            #
+            # Two seconds, encoded ONCE and cached. Encoding a full 15s shot instead
+            # cost a VAE pass big enough to OOM mid-render on a 16GB card, where the
+            # failure again degraded silently to no conditioning at all.
+            enc = audio_vae.encode(torch.zeros((1, sr * _SILENT_SECONDS, 2)))
+            if enc is None or enc.dim() != 4 or enc.shape[1] != 32:
+                return None
+            if enc.shape[-1] <= 2 * _SILENT_EDGE + 1:
+                return None
+            block = enc[..., _SILENT_EDGE:-_SILENT_EDGE].detach().to("cpu").clone()
+            _SILENT_UNIT["lat"] = block
+            _SILENT_UNIT["key"] = key
+        n = block.shape[-1]
+        if n < 1:
+            return None
+        # Forward, reversed, forward... Each join repeats a frame, so the seam that
+        # plain tiling leaves is gone while the interior variation is the encoder's.
+        pieces, have, i = [], 0, 0
+        while have < want_t:
+            piece = block if i % 2 == 0 else torch.flip(block, dims=[-1])
+            pieces.append(piece)
+            have += n
+            i += 1
+        out = torch.cat(pieces, dim=-1)[..., :want_t].clone()
+        if out.shape[-1] != want_t:
+            return None
+        return out
+    except Exception:
+        return None                         # never fail a render for a nicety
+
+
+def _pin_audio_silence(latent, silence, lead_frames=None, tail_frames=0):
+    """Start target audio at encoded silence and preserve the requested span(s).
+
+    lead_frames None pins the whole shot. Otherwise the first lead_frames and the
+    last tail_frames are held at silence and the span between is left to the model
+    -- that is where the line goes. The tail is clipped to what the lead leaves, so
+    the two can never overlap. Nothing pinned at all is a no-op, reported as False
+    so the caller does not count it as applied."""
+    try:
+        video, audio = latent["samples"].unbind()
+        silence = silence.to(device=audio.device, dtype=audio.dtype)
+        if silence.shape != audio.shape:
+            return False
+        audio_mask = torch.ones_like(audio[:, :1])
+        if lead_frames is None:
+            audio_mask.zero_()
+        else:
+            t = audio.shape[-1]
+            n = min(t, max(0, int(lead_frames)))
+            m = min(t - n, max(0, int(tail_frames or 0)))
+            if n <= 0 and m <= 0:
+                return False
+            if n > 0:
+                audio_mask[..., :n] = 0
+            if m > 0:
+                audio_mask[..., t - m:] = 0
+        latent["samples"] = comfy.nested_tensor.NestedTensor((video, silence))
+        latent["noise_mask"] = comfy.nested_tensor.NestedTensor(
+            (torch.ones_like(video[:, :1]), audio_mask))
+        return True
+    except Exception:
+        return False

dumas_h3_longvideos_conditioning.py

@@ -0,0 +1,310 @@
+# H3-LongVideos -- https://github.com/Smite79/MiniMax-H3-LongVideos
+# Copyright (c) 2026 Smite79. All rights reserved.
+# Redistribution, in whole or in part, requires written permission.
+# This notice may not be removed or altered. See LICENSE.
+"""Decisions about which pictures may condition a shot."""
+
+import torch
+import node_helpers
+from h3_runtime import (H3_FPS, AUDIO_LATENT_FPS, _empty_av_latent, _resize, ref_image_canvas,
+                        frame_levels)
+from h3_audio import _SILENCE_STATUS, _silent_audio_latent, _pin_audio_silence
+
+
+def may_carry_room(previous_cast, current_cast, tagged_names):
+    """A previous frame is safe as a reference only when it adds no subject."""
+    previous = [name for name in (previous_cast or ()) if name]
+    current = set(current_cast or ())
+    tagged = set(tagged_names or ())
+    return bool(previous) and all(name in current for name in previous) \
+        and not any(name in tagged for name in previous)
+
+
+def may_carry_frame(previous_cast, current_cast, tagged_names):
+    """A previous frame of the SAME room is safe as a reference claimed with everyone in it.
+
+    Unlike may_carry_room, somebody this shot does not describe may be in it: the claim
+    names them, and they are still in that room. Refused only for an empty frame, or
+    one holding somebody whose own portrait also rides this shot -- two pictures of one
+    person is how a second one gets drawn."""
+    previous = [name for name in (previous_cast or ()) if name]
+    current = set(current_cast or ())
+    tagged = set(tagged_names or ())
+    return bool(previous) and not any(name in tagged and name in current
+                                      for name in previous)
+
+
+def recoverable_subject(cast, tagged_names, returning_names, captured):
+    """Return the sole safe recovered subject, or an empty string."""
+    people = [name for name in (cast or ()) if name]
+    if len(people) != 1:
+        return ""
+    name = people[0]
+    return name if (name not in set(tagged_names or ())
+                    and name in set(returning_names or ())
+                    and captured.get(name) is not None) else ""
+
+
+KEYFRAME_SAFE_AUG = 0.99       # below this, a ref aug would soften the keyframe too
+
+# What ONE boundary is allowed to claim it measured. Wider than any real per-pass drift,
+# narrow enough that a bad frame -- a flash, a cut to black, a frame the model lost --
+# cannot swing the estimate. The median across boundaries does the real rejecting.
+LEVEL_GAIN_CAP = 0.12          # in log-gain, so +-12.7% of contrast
+LEVEL_OFFSET_CAP = 0.05
+# The within-shot term is believed only when boundaries AGREE on its sign, and even then
+# only this far: within-shot change is often the author's (a light switched off), so it is
+# the half of the signal that cannot be trusted on its own.
+LEVEL_SHOT_GAIN_CAP = 0.015
+LEVEL_SHOT_OFFSET_CAP = 0.010
+LEVEL_AGREE = 2.0 / 3.0
+LEVEL_MIN_OBS = 3
+# What the correction may do to one handoff, whatever it measured. A cut should not carry
+# a visible grade step: shot N's last frame reaches the video uncorrected while N+1 is
+# sampled from a corrected keyframe, so an uncapped correction trades burn-in for a pop at
+# every join -- the same class of complaint, differently shaped.
+LEVEL_GAIN_LO, LEVEL_GAIN_HI = 0.80, 1.25
+LEVEL_OFFSET_BOUND = 0.02
+# Below this a frame is too flat for a contrast RATIO to mean anything.
+LEVEL_MIN_SIGMA = 0.01
+
+
+class HandoffLevels:
+    """Takes the grade the chain adds to itself back out of the handoff.
+
+    THE MEASUREMENT, which is the whole reason this needs no scene list. At every
+    boundary the render holds two pictures that are SUPPOSED to be the same frame: K,
+    the handoff it gave the shot, and R, frame one of what came back -- the model's own
+    reproduction of K, from a keyframe labelled sigma 0.001. Nothing was asked to change
+    between them, so everything separating them is the chain's own doing and none of it
+    is the author's intent. That is the one difference in the loop that can be corrected
+    without guessing at anybody's lighting, and R costs nothing to look at: it is the
+    frame trim_seam throws away.
+
+    A beat that walks into a darker room moves K, and R follows it there. So the level is
+    never anchored, never compared to shot 1, and never compared to a target -- only K
+    against its own reproduction, boundary by boundary.
+
+    WHAT IT WILL NOT FIX. Clipping already baked into earlier shots, because the VAE
+    clamps every decode and headroom spent is gone. Softening, which is a different
+    measurement and a different cause. Anything spatial -- ghosting, local burn, identity
+    drift. A tone curve with a knee in it, since this is affine per channel; the residual
+    in the report is how that would show itself. The first boundary, which has nothing to
+    measure yet. And a deliberate monotone move -- a film that dims every single beat --
+    loses a bounded, reported fraction of itself."""
+
+    def __init__(self):
+        self._bg, self._bo = [], []      # per boundary: K -> R, the chain's own drift
+        self._sg, self._so = [], []      # per shot: R -> last frame, believed only on agreement
+        self.applied = []                # (gain, offset) actually used, for the report
+
+    def observe(self, given, repro, last=None, pre_up_last=None):
+        """Record one boundary. given is the keyframe this shot got, repro is frame one
+        of what it produced, last is its final frame, pre_up_last the handoff it hands on.
+
+        last/pre_up_last are how the pre-upscale handoff and the post-upscale output are
+        put in the same frame of reference: their difference IS the pipeline's own offset,
+        measured on one frame that went through both, so it can be subtracted from the
+        K->R reading instead of being mistaken for drift. With latent_upscale off they are
+        the same frame and the term is zero."""
+        gm, gs = frame_levels(given)
+        rm, rs = frame_levels(repro)
+        if gm is None or rm is None:
+            return False
+        if float(gs.min()) < LEVEL_MIN_SIGMA or float(rs.min()) < LEVEL_MIN_SIGMA:
+            return False
+        ug = torch.zeros(3)
+        uo = torch.zeros(3)
+        lm, ls = frame_levels(last) if last is not None else (None, None)
+        if pre_up_last is not None and lm is not None:
+            pm, ps = frame_levels(pre_up_last)
+            if pm is not None and float(ps.min()) >= LEVEL_MIN_SIGMA:
+                ug = torch.log(ls / ps)
+                uo = lm - pm
+        self._bg.append((torch.log(rs / gs) - ug).clamp(-LEVEL_GAIN_CAP, LEVEL_GAIN_CAP))
+        self._bo.append((rm - gm - uo).clamp(-LEVEL_OFFSET_CAP, LEVEL_OFFSET_CAP))
+        if lm is not None and float(ls.min()) >= LEVEL_MIN_SIGMA:
+            self._sg.append(torch.log(ls / rs))
+            self._so.append(lm - rm)
+        return True
+
+    def _agreed(self, rows, cap):
+        """The median of rows, but only per channel where at least LEVEL_AGREE of them
+        share its sign. A within-shot change the boundaries disagree about is content, not
+        drift, and content must not be corrected."""
+        out = torch.zeros(3)
+        if len(rows) < LEVEL_MIN_OBS:
+            return out
+        st = torch.stack(rows)
+        med = st.median(dim=0).values
+        agree = ((st * med.sign().unsqueeze(0)) > 0).float().mean(dim=0)
+        keep = agree >= LEVEL_AGREE
+        return torch.where(keep, med.clamp(-cap, cap), out)
+
+    def estimate(self):
+        """(gain_log, offset) the chain is drifting by per boundary, per channel."""
+        if not self._bg:
+            return None, None
+        g = torch.stack(self._bg).median(dim=0).values + self._agreed(self._sg, LEVEL_SHOT_GAIN_CAP)
+        o = torch.stack(self._bo).median(dim=0).values + self._agreed(self._so, LEVEL_SHOT_OFFSET_CAP)
+        return g, o
+
+    def gains(self, strength):
+        """(gain, offset) as 3-vectors, or (None, None) when there is nothing worth doing.
+
+        Separate from note() because more than one frame leaves a shot -- the handoff,
+        and any face captured for a return several shots later -- and they have to carry
+        the SAME grade. A recovered face arriving at a different exposure from the shot
+        around it would be a new bug of exactly the kind this is fixing."""
+        g, o = self.estimate()
+        if g is None or strength <= 0:
+            return None, None
+        gain = torch.exp(-float(strength) * g).clamp(LEVEL_GAIN_LO, LEVEL_GAIN_HI)
+        off = (-float(strength) * o).clamp(-LEVEL_OFFSET_BOUND, LEVEL_OFFSET_BOUND)
+        # The next thing this frame meets is an 8-bit quantisation, so a correction under
+        # 1/255 would be erased on the way there. Claiming it would be worse than silence.
+        if float((gain - 1.0).abs().max()) < 1e-3 and float(off.abs().max()) < 1.0 / 255.0:
+            return None, None
+        return gain, off
+
+    def note(self, gain, off):
+        """Record what was applied, and say it in one clause."""
+        self.applied.append((gain.clone(), off.clone()))
+        return (f"gain {'/'.join(f'{float(v):.3f}' for v in gain)} "
+                f"level {'/'.join(f'{float(v):+.4f}' for v in off)}")
+
+def _keyframe_latent(vae, hand_img):
+    """The keyframe latent for this shot: an ENCODE of the previous shot's last frame.
+
+    This was briefly an optimisation -- pass the previous shot's own latent straight
+    through and skip a VAE round trip per boundary. It was wrong, and it degraded
+    every shot after the first.
+
+    A keyframe is ONE pixel frame, and H3's grid puts that at 5f -> TWO latent
+    frames. Slicing [:, :, -1:] off a finished shot hands over one. Worse, the video
+    VAE is causal: the last latent of a 72-frame sequence encodes its temporal
+    context, not a standalone opening frame, so even at the right count it does not
+    mean what a keyframe means. The spatial-size guard could not see either problem.
+
+    The round trip is real but it is one lossy step on a correctly formed anchor,
+    which beats a cheap malformed one."""
+    return vae.encode(hand_img)
+
+
+def _build_ref_images(vae, images, gen_w, gen_h, mode="match"):
+    """(tokenizer items, DiT blocks) for a list of reference IMAGE tensors.
+
+    The tokenizer labels each one `<Picture N>:` itself, in the order given here --
+    so the roster the prompt refers to is decided by input order, not by anything
+    written in the prompt."""
+    items, blocks = [], []
+    for img in images:
+        if img is None:
+            continue
+        h, w = int(img.shape[1]), int(img.shape[2])
+        tw, th = ref_image_canvas(w, h, gen_w, gen_h, mode)
+        resized = _resize(img[:1], tw, th, "disabled")
+        items.append({"type": "image", "data": resized})
+        blocks.append({"kind": "image", "latent_h": th // 16, "latent_w": tw // 16,
+                       "latent": vae.encode(resized)})
+    return items, blocks
+
+
+def build_conditioning(clip, vae, audio_vae, prompt, width, height, length,
+                       handoff=None, refs=None,
+                       ref_noise_aug=0.999, silent=False, ref_image_size="match",
+                       handoff_as_ref=False, speech_lead_seconds=0.0, speech_tail_frames=0):
+    """Encode prompt, identity references, keyframe, and audio constraints for a shot."""
+    latent, fc = _empty_av_latent(width, height, length, H3_FPS)
+    refs = [r for r in (refs or []) if r is not None]
+
+    hand_img = None
+    if handoff is not None:
+        hand_img = _resize(handoff[:1], width, height, "disabled")
+
+    # REFERENCES AND THE KEYFRAME RIDE TOGETHER. This is the arrangement the node
+    # had before I broke it, and the reason is in ComfyUI's own layout:
+    #
+    #   model_base.py:2183-2191  cond_video_latents = keyframe latents THEN ref latents
+    #   model.py PackedLayout    emits keyframe "cond" segments THEN ref "ref_img" ones
+    #
+    # The two orders agree, so both channels coexist. A shot takes its references AND
+    # a real keyframe: the keyframe ANCHORS the first frame, which is what continuity
+    # needs, while a reference only supplies identity. They are not alternatives.
+    #
+    # I had read "<Picture 1>" as MEANING the first frame on fl2va, and rearranged the
+    # roster around that. It does not. Which image is the first frame is decided by
+    # resolved_frame_index in minimax_keyframes, not by a label's number -- the labels
+    # are only how the images are shown to the VLM, and what they have to line up with
+    # is the <Picture N> tags in the prompt.
+    #
+    # So references come FIRST and keep slots 1..N, which is what a sheet line's
+    # `Name: <Picture 1>, ...` points at, and the handoff is appended AFTER them where
+    # it disturbs no numbering. It has to be in the list at all because
+    # tokenize_with_weights is either/or: passing minimax_ref_items makes it ignore
+    # `images` outright, so leaving the handoff out means the VLM is never shown where
+    # the shot left off and re-imagines the scenery -- same place, new room.
+    keyframe_ok = ref_noise_aug is None or float(ref_noise_aug) >= KEYFRAME_SAFE_AUG
+    # One aug covers every visual condition row, references AND the keyframe. Below
+    # KEYFRAME_SAFE_AUG the keyframe latent would be noised and labelled at the wrong
+    # timestep, so the handoff stops being an anchor and rides as an extra reference
+    # instead: weaker continuity, but nothing pretending to anchor while carrying noise.
+    # ...or because the caller asked for it. A shot that introduces somebody already
+    # in position wants the room this picture carries and NOT the first frame it
+    # would force, and that is a demotion the aug knows nothing about.
+    carry_as_ref = bool(hand_img is not None
+                        and (handoff_as_ref or (refs and not keyframe_ok)))
+
+    enc_refs = refs + ([hand_img] if carry_as_ref else [])
+    items, blocks = ([], [])
+    if enc_refs:
+        items, blocks = _build_ref_images(vae, enc_refs, width, height, ref_image_size)
+    if hand_img is not None and not carry_as_ref:
+        items = items + [{"type": "image", "data": hand_img}]
+
+    if items:
+        tokens = clip.tokenize(prompt, minimax_ref_items=items)
+    else:
+        tokens = clip.tokenize(prompt)
+    cond = clip.encode_from_tokens_scheduled(tokens)
+
+    vals = {}
+    if blocks:
+        vals["minimax_refs"] = blocks
+        # How CLEAN the references are shown. One aug covers every conditioning
+        # latent, keyframe included -- which is why softening references below
+        # KEYFRAME_SAFE_AUG would soften the anchor too.
+        if ref_noise_aug is not None:
+            vals["minimax_visual_cond_noise_aug"] = float(ref_noise_aug)
+
+    kfs = []
+    if hand_img is not None and not carry_as_ref:
+        kfs.append({"resolved_frame_index": 0,
+                    "latent": _keyframe_latent(vae, hand_img)})
+    # Audio keyframes are extra conditioning rows in H3's PackedLayout. Pin the
+    # generated target stream instead, so the joint model also sees a quiet mouth.
+    # A dialogue shot pins its opening (the lead) and, past the line's estimated end,
+    # its close (the tail); the span between is the model's.
+    if silent or float(speech_lead_seconds or 0.0) > 0.0 or int(speech_tail_frames or 0) > 0:
+        _SILENCE_STATUS["asked"] += 1
+        if audio_vae is None:
+            _SILENCE_STATUS["why"] = "no audio VAE is wired to the node"
+        else:
+            sil = _silent_audio_latent(audio_vae, fc, H3_FPS)
+            if sil is None:
+                _SILENCE_STATUS["why"] = ("the audio VAE would not encode a silent "
+                                          "second -- the wrong VAE is on the "
+                                          "audio_vae input")
+            else:
+                lead = None if silent else round(float(speech_lead_seconds) *
+                                                 AUDIO_LATENT_FPS)
+                tail = 0 if silent else int(speech_tail_frames or 0)
+                if _pin_audio_silence(latent, sil, lead, tail):
+                    _SILENCE_STATUS["applied"] += 1
+                else:
+                    _SILENCE_STATUS["why"] = "the silent latent did not match the shot"
+    if kfs:
+        vals["minimax_keyframes"] = kfs
+    if vals:
+        cond = node_helpers.conditioning_set_values(cond, vals)
+    return cond, latent, fc, carry_as_ref

dumas_h3_longvideos_runtime.py

@@ -0,0 +1,739 @@
+# H3-LongVideos -- https://github.com/Smite79/MiniMax-H3-LongVideos
+# Copyright (c) 2026 Smite79. All rights reserved.
+# Redistribution, in whole or in part, requires written permission.
+# This notice may not be removed or altered. See LICENSE.
+"""Sampling, decoding, resizing, memory handling, and frame assembly."""
+
+import math
+
+import torch
+import nodes
+import comfy.utils
+import comfy.sample
+import comfy.samplers
+import comfy.nested_tensor
+import comfy.model_management as mm
+import latent_preview
+
+
+class FrameAccumulator:
+    """Build the final frame tensor once, retaining overflow only when necessary."""
+
+    def __init__(self, capacity, dtype, store_on_cpu):
+        self.capacity = int(capacity)
+        self.dtype = dtype
+        self.store_on_cpu = bool(store_on_cpu)
+        self.tensor = None
+        self.used = 0
+        self.overflow = []
+
+    def add(self, frames):
+        count = int(frames.shape[0])
+        if self.tensor is None and count:
+            device = torch.device("cpu") if self.store_on_cpu else frames.device
+            self.tensor = torch.empty(
+                (max(count, self.capacity),) + tuple(frames.shape[1:]),
+                dtype=self.dtype, device=device)
+        if (not self.overflow and self.tensor is not None
+                and self.used + count <= self.tensor.shape[0]):
+            self.tensor[self.used:self.used + count].copy_(frames)
+            self.used += count
+            return
+        self.overflow.append(frames.to("cpu", self.dtype, copy=True)
+                             if self.store_on_cpu else frames)
+
+    def release(self):
+        """Drop every tensor held, now, rather than whenever the collector gets to it.
+
+        On an interrupt the render unwinds through frames the collector tears down in
+        its own order, and a large video buffer freed after the models it was sized
+        against have already gone is a free the allocator cannot explain. Deliberately
+        does NOT empty the cache: that is another CUDA call, and if the context is
+        already in a sticky error state it is one more thing to abort inside."""
+        self.tensor = None
+        self.overflow = []
+        self.used = 0
+
+    def finish(self):
+        if not self.overflow:
+            if self.tensor is None:
+                return torch.cat(self.overflow, dim=0)
+            if self.used == self.tensor.shape[0]:
+                out = self.tensor
+            else:
+                # COMPACT, never a slice. A slice of a larger buffer keeps the WHOLE
+                # buffer's storage alive, which is the retention this class exists to
+                # prevent -- and test_the_chain_is_never_held_twice measures exactly
+                # that, demanding no unused bytes behind the returned tensor.
+                #
+                # There is slack because the capacity is now an upper bound: it can no
+                # longer assume trim_seam drops a frame at every seam, since a shot that
+                # opens on no keyframe keeps its first frame. Over-allocating by at most
+                # one frame per seam and compacting once is the bounded cost. The
+                # alternative -- an exact guess that can be too small -- drops into the
+                # overflow list, which with cleanup_between_shots off holds every shot's
+                # decoded frames live on the GPU until the end of the run.
+                out = torch.empty((self.used,) + tuple(self.tensor.shape[1:]),
+                                  dtype=self.dtype, device=self.tensor.device)
+                out.copy_(self.tensor[:self.used])
+            self.tensor = None
+            return out
+
+        extra = sum(int(piece.shape[0]) for piece in self.overflow)
+        reference = self.tensor if self.tensor is not None else self.overflow[0]
+        out = torch.empty((self.used + extra,) + tuple(reference.shape[1:]),
+                          dtype=self.dtype, device=reference.device)
+        if self.tensor is not None and self.used:
+            out[:self.used].copy_(self.tensor[:self.used])
+        at = self.used
+        while self.overflow:
+            piece = self.overflow.pop(0)
+            count = int(piece.shape[0])
+            out[at:at + count].copy_(piece)
+            at += count
+        self.tensor = None
+        return out
+
+
+H3_FPS = 24                    # H3 renders 24 fps, always
+
+
+AUDIO_LATENT_FPS = 40          # audio latent frames per second
+
+
+AUTO_TILE_T = 8                # temporal chunk for a tiled decode
+
+
+MAX_FRAMES = 362               # H3's own ceiling (~15s)
+
+
+CANVAS_MULTIPLE = 32
+
+
+REF_IMAGE_SHORT_EDGE = 2048
+
+
+def align_frame_count(n):
+    """Up to the next valid H3 frame count. The grid is 17k+5."""
+    n = max(5, int(n))
+    while n % 17 != 5:
+        n += 1
+    return min(n, MAX_FRAMES)
+
+
+def video_latent_t(fc):
+    return 2 if fc <= 5 else ((fc - 5) // 17) * 5 + 2
+
+
+def temporal_shape(length, fps=H3_FPS):
+    """(frame count, video latent frames, audio latent frames) for a shot.
+
+    `fps` is accepted but deliberately IGNORED: the audio latent has to line up
+    with 24 fps video or the shot's sound is stretched against its picture."""
+    fc = align_frame_count(length)
+    return fc, video_latent_t(fc), round(fc / H3_FPS * AUDIO_LATENT_FPS)
+
+
+def ref_image_canvas(w, h, gen_w, gen_h, mode="match"):
+    """Pure: the (width, height) a reference image is encoded at.
+
+    'match' scales it (DOWN only, aspect kept) to the generation's pixel area, so a
+    reference costs about as much as one frame of the shot. 'max' goes to the
+    reference pipeline's 2048 short edge for the best identity fidelity, which on a
+    long chain is several times slower because the rows are re-attended every step
+    of every shot. Never upscales: a small reference stays small."""
+    w, h = max(1, int(w)), max(1, int(h))
+    if mode == "max":
+        scale = min(1.0, REF_IMAGE_SHORT_EDGE / min(w, h))
+    else:
+        scale = min(1.0, math.sqrt((int(gen_w) * int(gen_h)) / float(w * h)))
+    snap = lambda v: max(CANVAS_MULTIPLE, round(v * scale / CANVAS_MULTIPLE) * CANVAS_MULTIPLE)
+    return snap(w), snap(h)
+
+
+def _resize(image, width, height, crop):
+    s = image[..., :3].movedim(-1, 1)
+    s = comfy.utils.common_upscale(s, width, height, "lanczos", crop)
+    return s.movedim(1, -1)
+
+
+def _empty_av_latent(width, height, length, fps, batch_size=1):
+    fc, lt, at = temporal_shape(length, fps)
+    video = torch.zeros([batch_size, 24, lt, height // 16, width // 16], device=mm.intermediate_device())
+    audio = torch.zeros([batch_size, 32, 2, at], device=mm.intermediate_device())
+    return {"samples": comfy.nested_tensor.NestedTensor((video, audio))}, fc
+
+
+def _auto_tile_t(n_latent_frames, requested=None):
+    """Temporal tile for a tiled decode. An explicit value wins.
+
+    The decode_tile_frames widget is gone, so this is where the value comes from
+    now. It has to come from somewhere: ComfyUI's decode_tiled_3d defaults tile_t
+    to 999, i.e. SPATIAL tiles only, and expanding the whole clip's time axis at
+    once is the single largest allocation in a run. A "tiled" decode that keeps the
+    full temporal extent barely lowers the peak, so the OOM retry that switches
+    tiling on was, without this, retrying with almost the same footprint."""
+    if requested:
+        return int(requested)
+    n = int(n_latent_frames or 0)
+    return AUTO_TILE_T if n > AUTO_TILE_T else None
+
+
+def _decode_video(vae, out_latent, tiled, free_first=None, tile_t=None, tile_xy=None,
+                  keep=()):
+    """Decode the video latent.
+
+    `free_first` is the diffusion model: sampling is finished, and the video VAE
+    needs the room for THIS decode -- the free runs immediately before it, not to
+    make room for the next shot. On a card where the DiT is most of the VRAM, the
+    decode does not fit until it goes.
+
+    `keep` is what must NOT be evicted on the way. It was `keep_loaded=[]`, which
+    unloaded every resident model -- including the video VAE, which ComfyUI then
+    reloaded three lines later to run the decode. An evict-and-reload of the thing
+    about to be used, once per shot, on every card. Peak VRAM is identical either
+    way, since the VAE has to be resident to decode; the round trip was pure cost.
+
+    memory_required is ASKED FOR HONESTLY, which it was not. It was 1e30, and
+    free_memory computes `memory_to_free = memory_required - get_free_memory(device)`
+    (model_management.py:887), so 1e30 means "unload everything not in keep_loaded",
+    every shot, in full -- skipping partially_unload entirely.
+
+    What that evicts is the DiT, three lines before the next shot needs it again. On
+    a machine whose RAM is already full of finished frames there is nowhere for it to
+    go but disk, so the reload is a read from the drive, once per shot. Reported as
+    thrashing that slows the preload, and it is exactly that: the same weights being
+    read back at every boundary.
+
+    The VAE knows what its own decode costs -- ComfyUI sizes it with
+    memory_used_decode and uses that number everywhere else. Asked for that instead,
+    a card with headroom frees NOTHING and the DiT simply stays. A card without
+    headroom frees what it needs and no more, which is what partially_unload is for.
+    1e30 remains the fallback for a VAE that cannot estimate itself."""
+    latent = out_latent["samples"]
+    if latent.is_nested:
+        latent = latent.unbind()[0]
+    if free_first is not None:
+        try:
+            mm.free_memory(_decode_headroom(vae, latent), mm.get_torch_device(),
+                           keep_loaded=_resident(keep or (vae,)))
+        except Exception:
+            pass
+    # A VAE THAT ALREADY TILES DOES NOT NEED TO BE ASKED TO, AND ASKING COSTS 3x.
+    #
+    # MiniMaxH3VideoVAE.decode_tiled is, in full:
+    #
+    #     def decode_tiled(self, z, **kwargs):
+    #         return self.decode(z)
+    #
+    # Every tile_t/overlap_t/tile_x/tile_y this function computes is discarded, so
+    # the tiling the widget promises is not happening here -- the model tiles
+    # internally either way (256px spatial, 17-frame temporal), which is why
+    # comfy/sd.py sets handles_tiling on it.
+    #
+    # What the detour costs is the OUTPUT BUFFER. comfy's VAE.decode preallocates
+    # ONE result at vae_output_dtype and hands it to the model as output_buffer=,
+    # and MiniMaxH3VideoVAE.decode_temporal writes finalized chunks straight into
+    # it. Going through decode_tiled instead reaches _decode_tiled_owned, which
+    # calls the model with output_buffer=None -- so decode_temporal allocates its
+    # own at torch.float32 -- and then makes an fp16 `copy=True` of that. Two
+    # buffers, the larger of them at double width:
+    #
+    #     tiled : fp32 2.60GB + fp16 copy 1.30GB = 3.90GB per shot
+    #     decode: one preallocated fp16          = 1.30GB per shot
+    #
+    # at 362 frames of 1056x608. Every shot, on the node's own default.
+    #
+    # So: when the VAE owns its tiling AND can be written into, the un-tiled call IS
+    # the tiled one, minus the copies. Anything else keeps the old path -- this is a
+    # detour around a detour, not a claim that tiling is useless.
+    _owns_tiling = bool(getattr(vae, "handles_tiling", False) and getattr(
+        getattr(vae, "first_stage_model", None), "comfy_has_chunked_io", False))
+    if tiled and _owns_tiling:
+        imgs = vae.decode(latent)
+    elif tiled:
+        # Temporal + spatial tiling. Without tile_t the VAE expands the WHOLE latent
+        # clip at once, which on a 243-frame 1344x768 shot is the single largest
+        # allocation in the run -- and on an unpruned checkpoint that is already
+        # streaming, it is what tips the card over. Decoding in temporal chunks
+        # trades a little speed for a much lower peak; None keeps ComfyUI's defaults.
+        args = {}
+        tile_t = _auto_tile_t(latent.shape[2] if latent.ndim >= 5 else 0, tile_t)
+        if tile_t:
+            args["tile_t"] = int(tile_t)
+            args["overlap_t"] = max(1, int(tile_t) // 8)
+        if tile_xy:
+            args["tile_x"] = int(tile_xy)
+            args["tile_y"] = int(tile_xy)
+        try:
+            imgs = vae.decode_tiled(latent, **args) if args else vae.decode_tiled(latent)
+        except TypeError:
+            imgs = vae.decode_tiled(latent)      # older signature without tile_t
+    else:
+        imgs = vae.decode(latent)
+    if len(imgs.shape) == 5:
+        imgs = imgs.reshape(-1, imgs.shape[-3], imgs.shape[-2], imgs.shape[-1])
+    return imgs
+
+
+def _decode_audio(audio_vae, out_latent):
+    latent = out_latent["samples"]
+    if latent.is_nested:
+        latent = latent.unbind()[-1]
+    audio = audio_vae.decode(latent).movedim(-1, 1)
+    std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
+    std[std < 1.0] = 1.0
+    audio = audio / std
+    sr = getattr(audio_vae, "audio_sample_rate_output", getattr(audio_vae, "audio_sample_rate", 44100))
+    return {"waveform": audio, "sample_rate": sr}
+
+
+def _is_oom(e):
+    return isinstance(e, torch.cuda.OutOfMemoryError) or "out of memory" in str(e).lower()
+
+
+def _deep_cleanup():
+    """Release cached VRAM between shots so a long chain does not accumulate and OOM.
+
+    It unloads NOTHING. soft_empty_cache(force) ignores `force` in current ComfyUI
+    (model_management.py:2050) -- the body only reaches empty_cache() and
+    ipc_collect() -- so this drops cached blocks, not models. The `True` is kept
+    only for older builds that read it; the older comment here claimed this took an
+    unload_all_models path, and it does not."""
+    try:
+        mm.soft_empty_cache(True)
+    except TypeError:
+        mm.soft_empty_cache()
+    try:
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+    except Exception:
+        pass
+
+
+DECODE_HEADROOM = 1.25          # over ComfyUI's own estimate, for working allocations
+
+
+SAMPLE_HEADROOM = 1.35          # likewise for sampling, which is the longer stretch
+
+
+def _decode_headroom(vae, latent):
+    """VRAM this decode actually needs, by the VAE's own estimate. 1e30 if unknown.
+
+    ComfyUI sizes every VAE with memory_used_decode and uses that number itself, so
+    it is the honest figure to hand free_memory. The alternative -- and what was here
+    -- is 1e30, which means "unload everything" and evicts the DiT before every
+    decode, three lines before the next shot reloads it.
+
+    1e30 on failure rather than 0: a bad estimate that frees too little turns a slow
+    render into an OOM, and a wrong guess should fall back to the behaviour that has
+    been running, not to no freeing at all."""
+    try:
+        dtype = getattr(vae, "vae_dtype", None) or latent.dtype
+        need = float(vae.memory_used_decode(tuple(latent.shape), dtype))
+        if need > 0:
+            return need * DECODE_HEADROOM
+    except Exception:
+        pass
+    return 1e30
+
+
+def _resident(models):
+    """The LoadedModel entries ComfyUI currently holds for `models`.
+
+    That is the form free_memory's keep_loaded wants: it compares against the
+    entries in current_loaded_models, not against the ModelPatcher objects a node
+    is holding. Anything not matched is simply not kept, so a model that is not
+    resident costs nothing here."""
+    out = []
+    for lm in list(getattr(mm, "current_loaded_models", [])):
+        for m in models or ():
+            if m is None:
+                continue
+            try:
+                if lm.model is m or getattr(lm, "model", None) is getattr(m, "model", None):
+                    if lm not in out:
+                        out.append(lm)
+            except Exception:
+                pass
+    return out
+
+
+def _image_out_dtype():
+    """The dtype ComfyUI itself hands between nodes on THIS install.
+
+    The join used to end in a hard-coded .float(), commented "back to what every
+    downstream node expects". That was true when it was written and is not a
+    constant: ComfyUI has --fp16-intermediates, and on an install running it the
+    VAE's own decode already returns fp16 -- VAE.vae_output_dtype() IS
+    model_management.intermediate_dtype() (comfy/sd.py) -- as do EmptyLatentImage
+    and the rest of nodes.py. So on that install the node was taking frames the
+    VAE handed it in fp16, widening them to fp32 nothing had asked for, and
+    handing them to nodes whose own convention is fp16.
+
+    It is the largest thing this node holds, so the widening is not free: the
+    2580-frame chain costed at the join is 9.3GB as fp16 and 18.5GB as fp32,
+    against 44.6GB of staged weights on a 62GB machine -- which is the difference
+    between the render finishing and the OOM killer taking the server. Reported as
+    exactly that, twice.
+
+    Asked, not assumed, and never widened: whatever ComfyUI says it wants between
+    nodes is what the chain is built in. An install with the flag off is told
+    float32 and gets float32, byte for byte what it got before. Older builds have
+    no intermediate_dtype at all, so the fallback is the old constant."""
+    try:
+        return mm.intermediate_dtype()
+    except Exception:
+        return torch.float32
+
+
+def _evict_all_but(keep_model, latent=None):
+    """Unload every model EXCEPT the diffusion model from the GPU.
+
+    This is the fix for VRAM ratcheting across a long chain. soft_empty_cache()
+    only drops the CUDA allocator's cached blocks -- it does NOT unload models, so
+    ComfyUI keeps the Qwen3-VL text encoder (~14.6GB) and both VAEs resident in
+    current_loaded_models alongside the DiT. Each shot re-encodes the prompt
+    (text encoder), encodes the handoff keyframe (video VAE), then samples (DiT),
+    so all three compete for the card.
+
+    ComfyUI does free ahead of each load -- load_models_gpu() calls free_memory()
+    for what it is about to need (model_management.py:975), so the weight path is
+    not purely reactive. What it cannot size for is a long chain's ACTIVATIONS on
+    a card where the DiT is most of the VRAM. Freeing explicitly, right after
+    conditioning is built and before sampling, keeps only what the sampler needs.
+
+    ASKED FOR HONESTLY, and this is the expensive one. free_memory computes
+    `memory_to_free = memory_required - get_free_memory(device)`, so 1e30 meant
+    "unload everything but the DiT" on every shot, unconditionally -- on a 48GB card
+    with room for all of it as readily as on a 16GB one. What it unloads is the
+    ~14.6GB text encoder and both VAEs, and the next shot re-encodes the prompt and
+    the handoff keyframe, so all three come straight back. On a machine whose RAM is
+    already full of finished frames they come back from DISK, once per shot, which is
+    the thrashing this was reported as.
+
+    The DiT can size its own activations -- memory_required(shape) is what ComfyUI
+    itself calls before a load -- so ask for that. A card with room frees nothing and
+    keeps the encoder resident; a card without frees exactly as much as it must.
+    1e30 stays the fallback, because a bad estimate that frees too little turns a
+    slow render into an OOM."""
+    need = 1e30
+    try:
+        if latent is not None:
+            shape = latent["samples"].shape if isinstance(latent, dict) else latent.shape
+            need = float(keep_model.model.memory_required(tuple(shape))) * SAMPLE_HEADROOM
+            if not (need > 0):
+                need = 1e30
+    except Exception:
+        need = 1e30
+    try:
+        mm.free_memory(need, mm.get_torch_device(),
+                       keep_loaded=_resident([keep_model]))
+    except Exception:
+        try:
+            mm.soft_empty_cache(True)
+        except Exception:
+            pass
+
+
+def _sample_on_sigmas(model, seed, cfg, sampler_name, positive, negative, latent, sigmas):
+    """common_ksampler, driven by an EXTERNAL sigma schedule.
+
+    common_ksampler derives its sigmas from (sampler_name, scheduler, steps, denoise)
+    and takes no schedule argument, so a schedule computed anywhere else cannot
+    reach it. Under PDD that is fatal rather than merely inconvenient: the heads
+    accept only their nine trained boundaries, and re-deriving the grid from
+    widgets means hitting it by coincidence and losing it again the moment a step
+    count changes.
+
+    Mirrors nodes.common_ksampler's noise / mask / callback handling exactly -- the
+    only substitution is comfy.sample.sample_custom for comfy.sample.sample."""
+    latent_image = latent["samples"]
+    latent_image = comfy.sample.fix_empty_latent_channels(
+        model, latent_image,
+        latent.get("downscale_ratio_spacial", None),
+        latent.get("downscale_ratio_temporal", None))
+    noise = comfy.sample.prepare_noise(latent_image, seed, latent.get("batch_index"))
+    # `steps` here only sizes the progress bar -- the schedule is `sigmas`, whose
+    # step count is one less than its length (the trailing 0.0 is an endpoint).
+    callback = latent_preview.prepare_callback(model, max(len(sigmas) - 1, 1))
+    samples = comfy.sample.sample_custom(
+        model, noise, cfg, comfy.samplers.sampler_object(sampler_name), sigmas,
+        positive, negative, latent_image,
+        noise_mask=latent.get("noise_mask"), callback=callback,
+        disable_pbar=not comfy.utils.PROGRESS_BAR_ENABLED, seed=seed)
+    out = latent.copy()
+    out.pop("downscale_ratio_spacial", None)
+    out.pop("downscale_ratio_temporal", None)
+    out["samples"] = samples
+    return out
+
+
+RESIZE_CHUNK = 32
+
+
+def _stream_chunks(total):
+    """A collector that writes upscaled chunks into ONE destination as they land.
+
+    Both chunk loops in _upscale_frames used `out.append(...)` then
+    `frames = torch.cat(out, dim=0)`. That is the shape the finished-chain join was
+    rebuilt to stop, at a LARGER size: the list holds the whole upscaled chain and
+    the cat allocates a second one, both live at the cat, and `out` is a local that
+    is never cleared -- so it survives the cat, survives the trailing resize, and is
+    still bound at the return. Meanwhile the CALLER's pre-upscale chain cannot be
+    dropped either, because `part = frames[s:s+batch]` is a view into it.
+
+    At 2580 frames of 1056x608 that is 9.26GB per copy per doubling: 37GB x2 at 2x,
+    and 148GB x2 with the RealESRGAN_x4plus that is sitting in models/upscale_models.
+    Preallocating from the first chunk and copying into it removes exactly one of
+    those two, and drops the list at the same time.
+
+    The destination is sized from the FIRST chunk, so the model's scale factor does
+    not have to be known in advance, and the frame count is the caller's own -- an
+    upscaler changes width and height, never the number of frames."""
+    state = {"dst": None, "at": 0}
+
+    def put(piece):
+        if state["dst"] is None:
+            state["dst"] = torch.empty((int(total),) + tuple(piece.shape[1:]),
+                                       dtype=piece.dtype, device=piece.device)
+        k = int(piece.shape[0])
+        end = min(state["at"] + k, state["dst"].shape[0])
+        if end > state["at"]:
+            state["dst"][state["at"]:end].copy_(piece[:end - state["at"]])
+        state["at"] = end
+
+    def done():
+        d, at = state["dst"], state["at"]
+        if d is None:
+            return None
+        return d if at == d.shape[0] else d[:at]
+
+    return put, done
+
+
+def _resize_short_edge(frames, target, method="lanczos", chunk=0):
+    """Resize a [B,H,W,C] frame batch so its short edge == target (keeping aspect,
+    snapped to /32). Plain high-quality resize -- enlarges, doesn't add detail.
+
+    IN CHUNKS, BECAUSE LANCZOS IS FOUR FULL-LENGTH COPIES. The whole chain went
+    into one common_upscale call, and comfy.utils.lanczos is three successive list
+    comprehensions over every frame at once:
+
+        images = [Image.fromarray(...) for image in samples]        # N at source size
+        images = [image.resize(...) for image in images]            # N at target size
+        images = [torch.from_numpy(np.array(im).astype(np.float32)/255.) ...]
+        result = torch.stack(images)
+        return result.to(samples.device, samples.dtype)
+
+    A comprehension builds the new list completely before rebinding the name, so at
+    each rebind BOTH are live; then torch.stack allocates a full copy while its list
+    still exists, and .to() allocates the result while the stack still exists. Note
+    the astype(np.float32): the input is fp16 but the two largest transients are at
+    DOUBLE its width. At 2580 frames to a 1080 short edge that peaked around 147GB
+    to produce a 29GB result, and it fires on a DOWNSCALE too.
+
+    Chunked, the peak is the result plus one chunk's worth of that machinery. It is
+    bit-identical: PIL resizes each frame independently, so per-chunk and per-chain
+    give the same pixels. The early return for an already-correct size is kept, so
+    the common no-op case still allocates nothing."""
+    b, h, w, c = frames.shape
+    if min(h, w) == target:
+        return frames
+    if h <= w:
+        nh = target; nw = max(32, int(round(target * w / h / 32) * 32))
+    else:
+        nw = target; nh = max(32, int(round(target * h / w / 32) * 32))
+    step = max(1, int(chunk) or RESIZE_CHUNK)
+    out = torch.empty((b, nh, nw, c), dtype=frames.dtype, device=frames.device)
+    for i in range(0, b, step):
+        part = comfy.utils.common_upscale(
+            frames[i:i + step].movedim(-1, 1), nw, nh, method, "disabled")
+        out[i:i + step].copy_(part.movedim(1, -1))
+        del part
+    return out
+
+
+def _upscale_frames(frames, mode, model_name, target_short_edge, batch=4):
+    """Optional post-pass upscale of the finished frames (on CPU).
+      mode 'model'   : run a ComfyUI upscale model (Real-ESRGAN/UltraSharp class)
+                       via the registered loader+apply nodes, chunked with cleanup
+                       so 2000+ frames don't OOM; then fit to target short edge.
+      mode 'rtx'     : NVIDIA RTX Video Super Resolution (Tensor Cores; fastest,
+                       best quality for video -- needs Nvidia_RTX_Nodes_ComfyUI).
+      mode 'lanczos' : plain high-quality resize to the target short edge.
+    Any failure falls back to lanczos (or the raw frames), so it never breaks a
+    render. Returns (frames, note). NOTE: this SHARPENS/ENLARGES; it does not
+    reconstruct video detail the way a second-model (LTX 2.3) pass does."""
+    if mode == "off" or frames is None or getattr(frames, "shape", [0])[0] == 0:
+        return frames, ""
+    note = ""
+    if mode == "rtx":
+        # NVIDIA RTX Video Super Resolution (Comfy-Org/Nvidia_RTX_Nodes_ComfyUI).
+        # Runs on RTX Tensor Cores -- far faster than ESRGAN-class models and
+        # generally cleaner on video, though like them it enhances/enlarges rather
+        # than reconstructing detail (an LTX 2.3 re-generation does that).
+        try:
+            rtx = (_find_node(["rtx", "video", "super"]) or _find_node(["rtxvideosuperresolution"])
+                   or _find_node(["rtx", "upscale"]))
+            if rtx is None:
+                raise RuntimeError("RTX node not installed (Nvidia_RTX_Nodes_ComfyUI)")
+            scale = 2
+            if target_short_edge and int(target_short_edge) > 0:
+                cur = min(frames.shape[1], frames.shape[2])
+                if cur > 0:
+                    scale = max(1, min(4, int(round(int(target_short_edge) / cur))))
+            _put, _done = _stream_chunks(frames.shape[0])
+            n = frames.shape[0]
+            step = max(1, int(batch))
+            for st in range(0, n, step):
+                part = frames[st:st + step]
+                res = None
+                for kw in ({"image": part, "scale": scale}, {"images": part, "scale": scale},
+                           {"image": part, "scale_factor": scale}, {"image": part}):
+                    try:
+                        res = _invoke_node(rtx, **kw); break
+                    except TypeError:
+                        continue
+                if res is None:
+                    raise RuntimeError("RTX node signature not recognized")
+                _put(res.detach().to("cpu"))
+                del res, part
+                _deep_cleanup()
+            frames = _done()
+            note = f"RTX Video Super Resolution x{scale}"
+            if target_short_edge and int(target_short_edge) > 0:
+                frames = _resize_short_edge(frames, int(target_short_edge))
+                note += f"; fit to {int(target_short_edge)}px short edge"
+            return frames, note
+        except Exception as e:
+            mode = "model"
+            note = f"RTX upscale unavailable ({e}); fell back to model/lanczos"
+    if mode == "model" and model_name and model_name != "none":
+        try:
+            loader = _find_node(["upscale", "model", "load"]) or _find_node(["loadupscalemodel"])
+            applier = _find_node(["imageupscale", "model"]) or _find_node(["upscaleimageusingmodel"])
+            if loader is None or applier is None:
+                raise RuntimeError("upscale-model nodes not found")
+            up_model = _invoke_node(loader, model_name=model_name)
+            _put, _done = _stream_chunks(frames.shape[0])
+            n = frames.shape[0]
+            for s in range(0, n, max(1, int(batch))):
+                part = frames[s:s + max(1, int(batch))]
+                res = _invoke_node(applier, upscale_model=up_model, image=part)
+                _put(res.detach().to("cpu"))
+                del res, part
+                _deep_cleanup()
+            frames = _done()
+            note = f"upscaled with {model_name}"
+        except Exception as e:
+            mode = "lanczos"
+            note = f"model upscale unavailable ({e}); used lanczos"
+    if target_short_edge and int(target_short_edge) > 0:
+        try:
+            frames = _resize_short_edge(frames, int(target_short_edge))
+            note = (note + "; " if note else "") + f"fit to {int(target_short_edge)}px short edge"
+        except Exception as e:
+            note = (note + "; " if note else "") + f"resize failed ({e})"
+    elif mode == "lanczos" and not note:
+        note = "lanczos selected but no target set -> unchanged"
+    return frames, note
+
+
+def _find_node(substrings):
+    """Find a registered node whose key contains all of `substrings` (lowercased)."""
+    maps = getattr(nodes, "NODE_CLASS_MAPPINGS", {}) or {}
+    for k, v in maps.items():
+        kl = k.lower()
+        if all(s in kl for s in substrings):
+            return v
+    return None
+
+
+def _invoke_node(cls, **kwargs):
+    """Call a registered ComfyUI node (V1 FUNCTION or V3 execute) with kwargs and
+    return its first output. Used to reuse ComfyUI's own upscale-model loader/apply
+    so we don't reimplement spandrel loading or tiled scaling."""
+    inst = cls()
+    fn = None
+    if getattr(cls, "FUNCTION", None) and hasattr(inst, cls.FUNCTION):
+        fn = getattr(inst, cls.FUNCTION)
+    else:
+        for cand in ("execute", "upscale", "load_model", "load"):
+            if hasattr(inst, cand):
+                fn = getattr(inst, cand); break
+    if fn is None:
+        raise RuntimeError("no callable entrypoint")
+    out = fn(**kwargs)
+    out = getattr(out, "result", out)
+    return out[0] if isinstance(out, (tuple, list)) else out
+
+
+# --- THE GRADE THE CHAIN ADDS TO ITSELF -------------------------------------
+# Every shot boundary decodes a shot, hands its LAST frame over, and re-encodes that as
+# the next shot's keyframe. The distill reproduces the keyframe faithfully enough to
+# inherit whatever is already in it and SYNTHESISES frame 0 rather than copying it, so
+# its own bias lands on top: S_next = a*S + b, a near 1, b above 0. Linear at best,
+# geometric at worst, invisible shot to shot. And the VAE hard-clips every decode to
+# 0..1, which makes the expansion a RATCHET -- headroom spent is not recoverable, so it
+# shows as crushed blacks and blown highlights rather than merely as more contrast.
+#
+# These two are the measurement and the correction. Both work per colour channel,
+# because the clip is per channel: the VAE un-whitens with ImageNet stds before it
+# clamps, so the 0..1 rails sit at different distances in each channel and the blue
+# floor and red ceiling bite first. A single luma number would miss the colour half.
+LEVEL_POOL = 64                # cells per axis the level statistics are measured on
+
+
+def frame_levels(img):
+    """(mean, std) per colour channel for one frame, as 3-vectors, or (None, None).
+
+    Area-pooled to LEVEL_POOL first, so a pre-upscale frame and an upscaled one can be
+    compared: pooling measures the PICTURE's levels rather than its resolution. Measured
+    across a 2x resize, std agrees to 0.28% on picture-like content -- and to only 15%
+    on pure noise, because pooling cannot preserve variance that lives entirely at the
+    pixel scale. Real frames are the former, and whatever residual there is cancels
+    anyway: the caller measures the same pipeline difference separately and subtracts it.
+
+    float32 throughout, deliberately: these frames are fp16 under
+    --fp16-intermediates, and an fp16 mean accumulated over a 1344x768 frame biases
+    badly enough to matter at the sizes being corrected here."""
+    x = img
+    if x.dim() == 4:
+        x = x[0]
+    if x.dim() != 3 or int(x.shape[-1]) < 3:
+        return None, None
+    if int(x.shape[0]) < 2 or int(x.shape[1]) < 2:
+        return None, None
+    x = x[..., :3].float().permute(2, 0, 1).unsqueeze(0)
+    p = torch.nn.functional.adaptive_avg_pool2d(x, LEVEL_POOL)[0].reshape(3, -1)
+    return p.mean(dim=1), p.std(dim=1)
+
+
+# The per-shot motion envelope lived here, measured so the built footsteps could be
+# timed off the picture. Nothing is built any more -- see the note at the top of
+# audio.py -- so there is nothing left to time, and a measurement with no reader is a
+# measurement that rots. Removed with the synthesiser it served.
+
+
+def apply_levels(img, gain, offset):
+    """Rescale a frame's contrast and level about its OWN per-channel mean.
+
+    The pivot is the frame's own mean and never a target. That is the whole reason this
+    can run on any scene: a beat that walks into a darker room keeps its darkness,
+    because nothing here knows or cares what the level is -- only how much the last
+    boundary expanded it. Anchoring to shot 1 instead would cancel every deliberate
+    lighting change in the film, which is the opposite failure.
+
+    Clamped into 0..1 because the next thing that happens to this frame is an 8-bit
+    quantisation (comfy.utils.common_upscale goes through a uint8 PIL round trip even
+    at the same size), so there is no headroom outside the range to borrow from."""
+    x = img.float()
+    c = min(3, int(x.shape[-1]))
+    m = x[..., :c].reshape(-1, c).mean(dim=0)
+    g = gain[:c].to(device=x.device, dtype=x.dtype)
+    o = offset[:c].to(device=x.device, dtype=x.dtype)
+    y = x.clone()
+    y[..., :c] = ((x[..., :c] - m) * g + m + o).clamp(0.0, 1.0)
+    return y.to(img.dtype)

dumas_h3_longvideos_shot_plan.py

@@ -0,0 +1,109 @@
+# H3-LongVideos -- https://github.com/Smite79/MiniMax-H3-LongVideos
+# Copyright (c) 2026 Smite79. All rights reserved.
+# Redistribution, in whole or in part, requires written permission.
+# This notice may not be removed or altered. See LICENSE.
+"""Per-shot records passed from prompt planning to rendering."""
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Shot:
+    prompt: str
+    cast: list[str]
+    speech: bool
+    sounded: bool
+    voiced_only: bool
+    events: list[str]
+    frame_count: int = 0
+    refs: list[object] = field(default_factory=list)
+    line_seconds: float = 0.0     # the planner's estimate of the spoken line, words / WORDS_PER_SEC
+
+
+@dataclass
+class ShotPlan:
+    shots: list[Shot] = field(default_factory=list)
+
+    @property
+    def prompts(self):
+        return [shot.prompt for shot in self.shots]
+
+    def add(self, prompt, cast, speech, sounded, voiced_only, events):
+        shot = Shot(prompt, list(cast or ()), bool(speech), bool(sounded),
+                    bool(voiced_only), list(events or ()))
+        self.shots.append(shot)
+
+    def set_frame_counts(self, counts):
+        counts = [int(n) for n in counts]
+        if len(counts) != len(self.shots) or any(n <= 0 for n in counts):
+            raise ValueError("frame counts must be positive and match the planned shots")
+        for shot, count in zip(self.shots, counts):
+            shot.frame_count = count
+
+    def validate(self):
+        if any(shot.frame_count <= 0 for shot in self.shots):
+            raise ValueError("each shot needs a positive frame count before rendering")
+        return self
+
+    def __len__(self):
+        return len(self.shots)
+
+
+@dataclass
+class PreparedVideo:
+    """Resolved inputs consumed by the render stage; model/tensor handles are shared."""
+    _placed_shots: object
+    _first_is_plate: object
+    _returns: object
+    _soft_landing: object
+    _tagged_names: object
+    ambient_audio: object
+    ambient_level: float
+    apply_model_sampling: bool
+    audio_vae: object
+    auto_sound: bool
+    bared_shots: object
+    cfg: float
+    cleanup_between_shots: bool
+    clip: object
+    first_frame: object
+    foley_level: float
+    h: int
+    latent_upscale: str
+    latent_upscale_scale: float
+    megapixels: float
+    model: object
+    moved_shots: object
+    negative: object
+    notes: list[str]
+    plan: ShotPlan
+    ref_noise_aug: float | None
+    restart_after_removal: bool
+    revealed_shots: object
+    sampler_name: str
+    scheduler: str
+    seed: int
+    shift_audio: float
+    shift_video: float
+    sigmas: object
+    silence_nonspeech: bool
+    speech_lead_seconds: float
+    speech_tail_seconds: float
+    hold_levels: float
+    handoff_frames: int
+    staging_shots: object
+    steps: int
+    stripped_shots: object
+    cut_shots: object
+    tiled_decode: bool
+    trim_seam: bool
+    upscale: str
+    upscale_batch: int
+    upscale_model: str
+    upscale_target_short_edge: int
+    vae: object
+    w: int
+    shot_rooms: object = None         # {0-based shot: (room it opens in, room it ends in)}
+    hardware_changed: object = None   # 1-based shots that put hardware on or take it off
+    shot_frames: object = None        # {0-based shot: (who its frames show, who is still there at its end)}
+    reentry_shots: object = None      # {0-based shot: who walks in while the keyframe still has them}

dumas_image_nodes.py

@@ -1253,11 +1253,13 @@ def curate_h3_prompt(
     soundscape_text = _reference_text(soundscape)
     bgm_text = _reference_text(bgm)
     reference_description = ""
+    individual_reference_descriptions = []
     if selected:
-        reference_description = " ".join(
+        individual_reference_descriptions = [
             _reference_context(ref, picture_number)
             for picture_number, (_slot, ref) in enumerate(selected, 1)
-        )
+        ]
+        reference_description = " ".join(individual_reference_descriptions)
 
     prompt_parts = []
     _append_prompt_section(prompt_parts, "Scene anchor", anchor_text)
@@ -1277,6 +1279,9 @@ def curate_h3_prompt(
         prompt = prompt[: _H3_PROMPT_MAX_CHARS - 3].rstrip() + "..."
     images = [_reference_image(ref) for _slot, ref in selected]
     images.extend([None] * (_H3_PROMPT_REF_SLOTS - len(images)))
+    individual_reference_descriptions.extend(
+        [""] * (_H3_PROMPT_REF_SLOTS - len(individual_reference_descriptions))
+    )
     debug = (
         f"Selected {len(selected)} reference(s): "
         + ", ".join(
@@ -1295,6 +1300,7 @@ def curate_h3_prompt(
         soundscape_text,
         bgm_text,
         *images[:_H3_PROMPT_REF_SLOTS],
+        *individual_reference_descriptions[:_H3_PROMPT_REF_SLOTS],
     )
 
 
@@ -2687,6 +2693,7 @@ class DumasH3PromptCuratorNode:
         + ("IMAGE",) * _H3_PROMPT_REF_SLOTS
         + ("INT", "STRING", "STRING", "STRING", "STRING")
         + ("IMAGE",) * _H3_PROMPT_REF_SLOTS
+        + ("STRING",) * _H3_PROMPT_REF_SLOTS
     )
     RETURN_NAMES = (
         "prompt",
@@ -2713,6 +2720,15 @@ class DumasH3PromptCuratorNode:
         "original_ref_7",
         "original_ref_8",
         "original_ref_9",
+        "compiled_ref_description_1",
+        "compiled_ref_description_2",
+        "compiled_ref_description_3",
+        "compiled_ref_description_4",
+        "compiled_ref_description_5",
+        "compiled_ref_description_6",
+        "compiled_ref_description_7",
+        "compiled_ref_description_8",
+        "compiled_ref_description_9",
     )
     FUNCTION = "curate_prompt"
     CATEGORY = "Dumas/MiniMax"

js/h3_beat_prompt/index.js

@@ -8,32 +8,15 @@ const DEFAULT_W = 520;
 const DEFAULT_H = 340;
 const DEFAULT_BEAT = "Describe this beat.";
 const STATE_PROPERTY = "dumas_h3_beat_prompt_state";
-const CONTINUITY_OPTIONS = ["", "soft carry", "hard cut", "keyframe carry", "handoff ref"];
-const REF_MODE_OPTIONS = ["", "auto ref2v", "where tagged", "first shot", "every shot", "every shot + handoff ref"];
 const MANAGED_DIRECTIVES = {
-  seconds: ["seconds", "duration"],
-  continuity: ["continuity"],
-  ref_mode: ["ref_mode"],
-  ref_noise_aug: ["ref_noise_aug"],
-  anchor_add: ["anchor_add"],
-  overall_soundscape: ["overall_soundscape", "soundscape"],
-  non_diegetic_music: ["non_diegetic_music", "music"],
+  remove: ["remove", "removed", "off"],
+  add: ["add", "wear", "wearing"],
 };
 const DIRECTIVE_EXAMPLES = [
-  ["wardrobe set", "wardrobe: Maya = grey shorts, red jacket"],
-  ["wardrobe add", "wardrobe: Maya += red jacket"],
-  ["wardrobe remove", "wardrobe: Maya -= red jacket"],
-  ["seconds", "seconds: 8"],
-  ["exit", "exit: Maya"],
-  ["enter", "enter: Jon"],
-  ["continuity", "continuity: hard cut"],
-  ["ref_mode", "ref_mode: every shot"],
-  ["ref_noise_aug", "ref_noise_aug: 0.92"],
-  ["anchor_add", "anchor_add: harsh sodium-vapor spill, wet pavement, long-lens compression"],
-  ["overall_soundscape", "overall_soundscape: soft rain, distant traffic"],
-  ["non_diegetic_music", "non_diegetic_music: tense analog synth pulse"],
-  ["soundscape", "soundscape: fluorescent room tone, faint HVAC hum"],
-  ["music", "music: low ominous cello and sparse percussion"],
+  ["remove", "remove: red jacket"],
+  ["off", "off: steel collar"],
+  ["add", "add: white shirt underneath"],
+  ["wearing", "wearing: black coat"],
 ];
 
 function injectCSS() {
@@ -183,7 +166,7 @@ function injectCSS() {
 }
 
 function defaultState() {
-  return { beats: [{ text: DEFAULT_BEAT }] };
+  return { scene: "", character_sheet: "", beats: [{ text: DEFAULT_BEAT }] };
 }
 
 function normalizeState(value) {
@@ -200,7 +183,11 @@ function normalizeState(value) {
   const normalized = beats.map((beat) => ({
     text: typeof beat?.text === "string" ? beat.text : String(beat?.text || ""),
   }));
-  return normalized.length ? { beats: normalized } : defaultState();
+  return {
+    scene: typeof parsed.scene === "string" ? parsed.scene : String(parsed.scene || ""),
+    character_sheet: typeof parsed.character_sheet === "string" ? parsed.character_sheet : String(parsed.character_sheet || ""),
+    beats: normalized.length ? normalized : [{ text: DEFAULT_BEAT }],
+  };
 }
 
 function readState(node) {
@@ -321,6 +308,51 @@ function renderUI(node) {
   node._dh3bpRenderedState = JSON.stringify(state);
   ui.list.innerHTML = "";
 
+  const buildTopTextarea = ({ labelText, placeholder, value, onInput }) => {
+    const card = document.createElement("div");
+    card.className = "dh3bp-beat";
+
+    const label = document.createElement("div");
+    label.className = "dh3bp-label";
+    label.textContent = labelText;
+
+    const textarea = document.createElement("textarea");
+    textarea.className = "dh3bp-text";
+    textarea.placeholder = placeholder;
+    textarea.value = value || "";
+    textarea.addEventListener("input", () => {
+      onInput(textarea.value);
+      updateTextareaHeight(textarea);
+    });
+    textarea.addEventListener("keydown", stopCanvasKeyboard);
+
+    card.append(label, textarea);
+    updateTextareaHeight(textarea);
+    return card;
+  };
+
+  ui.list.appendChild(buildTopTextarea({
+    labelText: "Scene paragraph",
+    placeholder: "Optional. Persistent location, lighting, camera, tone. Leave empty if you wire the Long Videos anchor input.",
+    value: state.scene,
+    onInput: (value) => {
+      const next = readState(node);
+      next.scene = value;
+      writeState(node, next);
+    },
+  }));
+
+  ui.list.appendChild(buildTopTextarea({
+    labelText: "Character sheet",
+    placeholder: "Optional. One character per line, e.g. Maya: 27, she, silver hair, red jacket, the woman in <Picture 1>.",
+    value: state.character_sheet,
+    onInput: (value) => {
+      const next = readState(node);
+      next.character_sheet = value;
+      writeState(node, next);
+    },
+  }));
+
   state.beats.forEach((beat, index) => {
     const card = document.createElement("div");
     card.className = "dh3bp-beat";
@@ -379,85 +411,27 @@ function renderUI(node) {
       return wrap;
     };
 
-    const secondsInput = document.createElement("input");
-    secondsInput.className = "dh3bp-input";
-    secondsInput.type = "text";
-    secondsInput.placeholder = "8";
-    secondsInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.seconds);
-    secondsInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "seconds", MANAGED_DIRECTIVES.seconds, secondsInput.value));
+    const removeInput = document.createElement("input");
+    removeInput.className = "dh3bp-input";
+    removeInput.type = "text";
+    removeInput.placeholder = "red jacket";
+    removeInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.remove);
+    removeInput.addEventListener("input", () => {
+      applyTextUpdate(setDirectiveValue(textarea.value, "remove", MANAGED_DIRECTIVES.remove, removeInput.value));
     });
 
-    const continuitySelect = document.createElement("select");
-    continuitySelect.className = "dh3bp-select";
-    CONTINUITY_OPTIONS.forEach((value) => {
-      const option = document.createElement("option");
-      option.value = value;
-      option.textContent = value || "Default";
-      continuitySelect.appendChild(option);
-    });
-    continuitySelect.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.continuity);
-    continuitySelect.addEventListener("change", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "continuity", MANAGED_DIRECTIVES.continuity, continuitySelect.value));
-    });
-
-    const refModeSelect = document.createElement("select");
-    refModeSelect.className = "dh3bp-select";
-    REF_MODE_OPTIONS.forEach((value) => {
-      const option = document.createElement("option");
-      option.value = value;
-      option.textContent = value || "Global";
-      refModeSelect.appendChild(option);
-    });
-    refModeSelect.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.ref_mode);
-    refModeSelect.addEventListener("change", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "ref_mode", MANAGED_DIRECTIVES.ref_mode, refModeSelect.value));
-    });
-
-    const refNoiseInput = document.createElement("input");
-    refNoiseInput.className = "dh3bp-input";
-    refNoiseInput.type = "text";
-    refNoiseInput.placeholder = "0.95";
-    refNoiseInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.ref_noise_aug);
-    refNoiseInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "ref_noise_aug", MANAGED_DIRECTIVES.ref_noise_aug, refNoiseInput.value));
-    });
-
-    const anchorInput = document.createElement("input");
-    anchorInput.className = "dh3bp-input";
-    anchorInput.type = "text";
-    anchorInput.placeholder = "extra per-shot style treatment";
-    anchorInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.anchor_add);
-    anchorInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "anchor_add", MANAGED_DIRECTIVES.anchor_add, anchorInput.value));
-    });
-
-    const soundscapeInput = document.createElement("input");
-    soundscapeInput.className = "dh3bp-input";
-    soundscapeInput.type = "text";
-    soundscapeInput.placeholder = "faint traffic, loose sign rattle";
-    soundscapeInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.overall_soundscape);
-    soundscapeInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "overall_soundscape", MANAGED_DIRECTIVES.overall_soundscape, soundscapeInput.value));
-    });
-
-    const musicInput = document.createElement("input");
-    musicInput.className = "dh3bp-input";
-    musicInput.type = "text";
-    musicInput.placeholder = "low pulsing synth tension";
-    musicInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.non_diegetic_music);
-    musicInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "non_diegetic_music", MANAGED_DIRECTIVES.non_diegetic_music, musicInput.value));
+    const addInput = document.createElement("input");
+    addInput.className = "dh3bp-input";
+    addInput.type = "text";
+    addInput.placeholder = "white shirt underneath";
+    addInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.add);
+    addInput.addEventListener("input", () => {
+      applyTextUpdate(setDirectiveValue(textarea.value, "add", MANAGED_DIRECTIVES.add, addInput.value));
     });
 
     controls.append(
-      buildField({ labelText: "Seconds", input: secondsInput }),
-      buildField({ labelText: "Continuity", input: continuitySelect }),
-      buildField({ labelText: "Ref Mode", input: refModeSelect }),
-      buildField({ labelText: "Ref Noise Aug", input: refNoiseInput }),
-      buildField({ labelText: "Anchor Add", className: "dh3bp-control-wide", input: anchorInput }),
-      buildField({ labelText: "Shot Soundscape", className: "dh3bp-control-wide", input: soundscapeInput }),
-      buildField({ labelText: "Shot Music", className: "dh3bp-control-wide", input: musicInput }),
+      buildField({ labelText: "Remove from memory", input: removeInput }),
+      buildField({ labelText: "Add to memory", input: addInput }),
     );
 
     const directives = document.createElement("div");
@@ -501,7 +475,7 @@ function setupNode(node) {
   title.textContent = "Beat Prompt Builder";
   const subtitle = document.createElement("div");
   subtitle.className = "dh3bp-subtitle";
-  subtitle.textContent = "One textbox per H3 beat, plus per-shot controls for timing, ref behavior, continuity, anchor adds, and audio directives.";
+  subtitle.textContent = "Upstream Long Videos format: optional scene, optional character sheet, then one blank-line-separated beat per shot.";
   titleWrap.append(title, subtitle);
 
   const addButton = document.createElement("button");

js/h3_longvideos/index.js

@@ -1,375 +1,9 @@
 import { app } from "/scripts/app.js";
-import { applyAdaptiveCanvasOnly } from "../shared/nodes2.mjs";
-
-const COMFY_CLASS = "DumasH3LongVideos";
-const STATE_PROPERTY = "dumas_h3_longvideos_section_state";
-const DOM_WIDGET_NAME = "dumas_h3_longvideos_sections";
-const MIN_WIDTH = 520;
-const MIN_HEIGHT = 280;
-
-const GROUPS = [
-  {
-    id: "prompt",
-    label: "Prompt",
-    defaultCollapsed: false,
-    widgets: ["prompt", "resolution", "megapixels", "beat_split", "anchor_override", "shot_seconds", "plan_only", "fps"],
-  },
-  {
-    id: "refs",
-    label: "Refs",
-    defaultCollapsed: true,
-    widgets: ["ref_mode", "ref_image_size", "ref_noise_aug", "character_memory", "trim_seam", "vary_seed_per_shot", "handoff_offset"],
-  },
-  {
-    id: "sampling",
-    label: "Sampling",
-    defaultCollapsed: true,
-    widgets: [
-      "steps", "cfg", "sampler_name", "scheduler", "seed",
-      "apply_model_sampling", "shift_video", "shift_audio",
-      "vram_headroom_gb", "allow_res_backoff",
-      "decode_tile_frames", "decode_tile_size",
-    ],
-  },
-  {
-    id: "audio",
-    label: "Audio",
-    defaultCollapsed: true,
-    widgets: [
-      "global_soundscape", "non_diegetic_music", "auto_soundscape",
-      "auto_silence_nonspeech", "allow_nonspeech_vocals",
-      "mute_nonspeech_audio", "mute_fade_ms",
-    ],
-  },
-  {
-    id: "scene",
-    label: "Scene Logic",
-    defaultCollapsed: true,
-    widgets: [
-      "auto_wardrobe", "auto_props", "prevent_nudity", "exposed_terms",
-      "anatomy_guard", "subject_count_guard", "lock_restraints",
-      "contact_guard", "motion_guard", "solidity_guard",
-    ],
-  },
-  {
-    id: "finish",
-    label: "Upscale",
-    defaultCollapsed: true,
-    widgets: [
-      "upscale", "upscale_model", "upscale_target_short_edge", "upscale_batch",
-    ],
-  },
-  {
-    id: "overlay",
-    label: "Overlays",
-    defaultCollapsed: true,
-    widgets: [
-      "watermark_text", "watermark_position", "watermark_size", "watermark_opacity", "watermark_margin",
-      "intro_text", "intro_position", "intro_seconds", "intro_fade", "intro_size",
-      "overlay_font", "overlay_stroke",
-    ],
-  },
-];
-
-function injectCSS() {
-  if (document.getElementById("dumas-h3lv-sections-css")) return;
-  const style = document.createElement("style");
-  style.id = "dumas-h3lv-sections-css";
-  style.textContent = `
-    .dh3lv-sections {
-      box-sizing: border-box;
-      width: 100%;
-      padding: 8px 10px 6px;
-      color: #e6e7eb;
-      font: 12px/1.35 "Segoe UI", sans-serif;
-      pointer-events: auto;
-      background: linear-gradient(180deg, rgba(33, 36, 42, 0.96), rgba(22, 24, 29, 0.96));
-      border-bottom: 1px solid rgba(255, 255, 255, 0.06);
-    }
-    .dh3lv-sections-head {
-      display: flex;
-      align-items: center;
-      justify-content: space-between;
-      gap: 8px;
-      margin-bottom: 8px;
-    }
-    .dh3lv-sections-title {
-      font-size: 11px;
-      text-transform: uppercase;
-      letter-spacing: 0.08em;
-      color: #9da5b1;
-    }
-    .dh3lv-sections-actions {
-      display: flex;
-      gap: 6px;
-    }
-    .dh3lv-sections-list {
-      display: flex;
-      flex-wrap: wrap;
-      gap: 6px;
-    }
-    .dh3lv-chip,
-    .dh3lv-action {
-      appearance: none;
-      border: 1px solid #464d59;
-      background: #262c35;
-      color: #d7dce3;
-      border-radius: 999px;
-      padding: 5px 9px;
-      cursor: pointer;
-      font: inherit;
-      line-height: 1.1;
-    }
-    .dh3lv-chip[data-open="true"] {
-      background: #d96f2b;
-      border-color: #f09358;
-      color: #fff7f0;
-    }
-    .dh3lv-chip:hover,
-    .dh3lv-action:hover {
-      filter: brightness(1.06);
-    }
-    .dh3lv-count {
-      opacity: 0.78;
-      margin-left: 4px;
-      font-size: 11px;
-    }
-  `;
-  document.head.appendChild(style);
-}
-
-function defaultState() {
-  const state = {};
-  for (const group of GROUPS) state[group.id] = !group.defaultCollapsed;
-  return state;
-}
-
-function parseState(value) {
-  let parsed = value;
-  if (typeof parsed === "string") {
-    try {
-      parsed = JSON.parse(parsed);
-    } catch (_error) {
-      parsed = null;
-    }
-  }
-  const base = defaultState();
-  if (!parsed || typeof parsed !== "object") return base;
-  for (const group of GROUPS) {
-    if (typeof parsed[group.id] === "boolean") base[group.id] = parsed[group.id];
-  }
-  return base;
-}
-
-function readState(node) {
-  return parseState(node.properties?.[STATE_PROPERTY] || node._dh3lvSectionState || "");
-}
-
-function writeState(node, state) {
-  const normalized = parseState(state);
-  const serialized = JSON.stringify(normalized);
-  node._dh3lvSectionState = serialized;
-  node.properties = node.properties || {};
-  node.properties[STATE_PROPERTY] = serialized;
-}
-
-function findWidget(node, name) {
-  return (node.widgets || []).find((widget) => widget?.name === name) || null;
-}
-
-function isInteractiveTarget(target) {
-  return !!target?.closest?.("button, input, textarea, select, label");
-}
-
-function stopCanvasEvent(event) {
-  if (isInteractiveTarget(event.target)) event.stopPropagation();
-}
-
-function stopCanvasKeyboard(event) {
-  if (isInteractiveTarget(event.target)) event.stopImmediatePropagation();
-}
-
-function setWidgetHidden(widget, hidden) {
-  if (!widget) return;
-  if (!widget._dh3lvOriginal) {
-    widget._dh3lvOriginal = {
-      type: widget.type,
-      computeSize: widget.computeSize,
-      hidden: widget.hidden,
-    };
-  }
-  if (hidden) {
-    widget.type = "hidden";
-    widget.hidden = true;
-    widget.computeSize = () => [0, -4];
-    return;
-  }
-  widget.type = widget._dh3lvOriginal.type;
-  widget.hidden = !!widget._dh3lvOriginal.hidden;
-  widget.computeSize = widget._dh3lvOriginal.computeSize;
-}
-
-function applyVisibility(node) {
-  const state = readState(node);
-  for (const group of GROUPS) {
-    for (const name of group.widgets) {
-      const widget = findWidget(node, name);
-      if (!widget || widget.name === DOM_WIDGET_NAME) continue;
-      setWidgetHidden(widget, !state[group.id]);
-    }
-  }
-}
-
-function resizeNode(node) {
-  requestAnimationFrame(() => {
-    const size = node.computeSize?.();
-    if (Array.isArray(size)) {
-      node.size[0] = Math.max(MIN_WIDTH, size[0] || 0, node.size?.[0] || 0);
-      node.size[1] = Math.max(MIN_HEIGHT, size[1] || 0);
-    }
-    node.setDirtyCanvas?.(true, true);
-  });
-}
-
-function renderToolbar(node) {
-  const ui = node._dh3lvUI;
-  if (!ui) return;
-  const state = readState(node);
-  ui.list.innerHTML = "";
-  for (const group of GROUPS) {
-    const button = document.createElement("button");
-    button.type = "button";
-    button.className = "dh3lv-chip";
-    button.dataset.open = state[group.id] ? "true" : "false";
-    button.textContent = state[group.id] ? `Hide ${group.label}` : `Show ${group.label}`;
-
-    const count = document.createElement("span");
-    count.className = "dh3lv-count";
-    count.textContent = String(group.widgets.filter((name) => findWidget(node, name)).length);
-    button.appendChild(count);
-
-    button.addEventListener("click", () => {
-      const next = readState(node);
-      next[group.id] = !next[group.id];
-      writeState(node, next);
-      applyVisibility(node);
-      renderToolbar(node);
-      resizeNode(node);
-    });
-    ui.list.appendChild(button);
-  }
-}
-
-function setAll(node, open) {
-  const next = {};
-  for (const group of GROUPS) next[group.id] = !!open;
-  writeState(node, next);
-  applyVisibility(node);
-  renderToolbar(node);
-  resizeNode(node);
-}
-
-function setupNode(node) {
-  if (node._dh3lvUI) return;
-  injectCSS();
-  writeState(node, readState(node));
-
-  const root = document.createElement("div");
-  root.className = "dh3lv-sections";
-
-  const head = document.createElement("div");
-  head.className = "dh3lv-sections-head";
-
-  const title = document.createElement("div");
-  title.className = "dh3lv-sections-title";
-  title.textContent = "Sections";
-
-  const actions = document.createElement("div");
-  actions.className = "dh3lv-sections-actions";
-
-  const expandAll = document.createElement("button");
-  expandAll.type = "button";
-  expandAll.className = "dh3lv-action";
-  expandAll.textContent = "Expand All";
-  expandAll.addEventListener("click", () => setAll(node, true));
-
-  const collapseAll = document.createElement("button");
-  collapseAll.type = "button";
-  collapseAll.className = "dh3lv-action";
-  collapseAll.textContent = "Collapse Extras";
-  collapseAll.addEventListener("click", () => {
-    const next = defaultState();
-    writeState(node, next);
-    applyVisibility(node);
-    renderToolbar(node);
-    resizeNode(node);
-  });
-
-  actions.append(expandAll, collapseAll);
-  head.append(title, actions);
-
-  const list = document.createElement("div");
-  list.className = "dh3lv-sections-list";
-  root.append(head, list);
-  root.addEventListener("pointerdown", stopCanvasEvent);
-  root.addEventListener("mousedown", stopCanvasEvent);
-  root.addEventListener("click", stopCanvasEvent);
-  root.addEventListener("dblclick", stopCanvasEvent);
-  root.addEventListener("keydown", stopCanvasKeyboard, true);
-
-  node._dh3lvUI = { root, list };
-  const widget = node.addDOMWidget(DOM_WIDGET_NAME, "custom", root, {
-    getValue: () => null,
-    setValue: () => {},
-    serialize: false,
-    getMinHeight: () => 52,
-    hideOnZoom: false,
-  });
-  applyAdaptiveCanvasOnly(widget);
-  const widgets = node.widgets || [];
-  const index = widgets.indexOf(widget);
-  if (index > 0) {
-    widgets.splice(index, 1);
-    widgets.unshift(widget);
-  }
-
-  applyVisibility(node);
-  renderToolbar(node);
-  resizeNode(node);
-}
 
+// The Dumas Long Videos node now wraps the upstream MiniMax-H3-Longvideos
+// sampler directly. The old local frontend grouped Dumas-specific widgets that
+// no longer exist on the upstream node, so this extension intentionally does
+// nothing.
 app.registerExtension({
-  name: "Dumas.H3LongVideosSections",
-  async beforeRegisterNodeDef(nodeType, nodeData) {
-    if (nodeData?.name !== COMFY_CLASS) return;
-
-    const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
-    nodeType.prototype.onNodeCreated = function onNodeCreated() {
-      const result = originalOnNodeCreated?.apply(this, arguments);
-      setupNode(this);
-      return result;
-    };
-
-    const originalConfigure = nodeType.prototype.onConfigure;
-    nodeType.prototype.onConfigure = function onConfigure() {
-      const result = originalConfigure?.apply(this, arguments);
-      setupNode(this);
-      writeState(this, readState(this));
-      applyVisibility(this);
-      renderToolbar(this);
-      resizeNode(this);
-      return result;
-    };
-
-    const originalSerialize = nodeType.prototype.onSerialize;
-    nodeType.prototype.onSerialize = function onSerialize(o) {
-      writeState(this, readState(this));
-      const result = originalSerialize?.apply(this, arguments);
-      if (o && this.properties?.[STATE_PROPERTY]) {
-        o.properties = o.properties || {};
-        o.properties[STATE_PROPERTY] = this.properties[STATE_PROPERTY];
-      }
-      return result;
-    };
-  },
+  name: "Dumas.H3LongVideos.UpstreamWrapper",
 });

js/h3_prompt_curator/index.js

@@ -26,6 +26,15 @@ const EXPECTED_OUTPUTS = [
   "original_ref_7",
   "original_ref_8",
   "original_ref_9",
+  "compiled_ref_description_1",
+  "compiled_ref_description_2",
+  "compiled_ref_description_3",
+  "compiled_ref_description_4",
+  "compiled_ref_description_5",
+  "compiled_ref_description_6",
+  "compiled_ref_description_7",
+  "compiled_ref_description_8",
+  "compiled_ref_description_9",
 ];
 const EXPECTED_NAMES = new Set(EXPECTED_OUTPUTS);
 

tests/test_dumas_h3_beat_prompt.py

@@ -11,35 +11,62 @@ class DumasH3BeatPromptTests(unittest.TestCase):
         state = self.module._parse_beat_prompt_state("not json")
         self.assertEqual(
             state,
-            {"beats": [{"text": "Describe this beat."}]},
+            {
+                "scene": "",
+                "character_sheet": "",
+                "beats": [{"text": "Describe this beat."}],
+            },
         )
 
-    def test_assemble_prompt_joins_beats_with_blank_lines(self):
+    def test_assemble_prompt_outputs_upstream_sections(self):
         prompt = self.module._assemble_beat_prompt(
             {
+                "scene": "A rainy kitchen at night.",
+                "character_sheet": "Maya: 27, she, red jacket, silver hair.",
                 "beats": [
-                    {"text": "A woman enters the room."},
-                    {"text": "wardrobe: Maya = red jacket\nShe sits at the table."},
+                    {"text": "Maya enters the room."},
+                    {"text": "remove: red jacket\nadd: white shirt underneath\nShe sits at the table."},
                     {"text": "  "},
-                    {"text": "music: low synth pulse"},
                 ]
             }
         )
         self.assertEqual(
             prompt,
             (
-                "A woman enters the room.\n\n"
-                "wardrobe: Maya = red jacket\nShe sits at the table.\n\n"
-                "music: low synth pulse"
+                "A rainy kitchen at night.\n\n"
+                "Maya: 27, she, red jacket, silver hair.\n\n"
+                "Maya enters the room.\n\n"
+                "remove: red jacket\nadd: white shirt underneath\nShe sits at the table."
             ),
         )
 
+    def test_assemble_prompt_strips_old_dumas_directives(self):
+        prompt = self.module._assemble_beat_prompt(
+            {
+                "beats": [
+                    {
+                        "text": (
+                            "seconds: 8\n"
+                            "continuity: hard cut\n"
+                            "ref_mode: every shot\n"
+                            "soundscape: soft rain\n"
+                            "music: low synth\n"
+                            "Maya opens the cupboard.\n"
+                            "remove: red jacket"
+                        )
+                    },
+                ]
+            }
+        )
+
+        self.assertEqual(prompt, "Maya opens the cupboard.\nremove: red jacket")
+
     def test_node_build_prompt_uses_hidden_state(self):
         node = self.module.DumasH3BeatPromptNode()
         result = node.build_prompt(
-            '{"beats":[{"text":"Beat one"},{"text":"Beat two"}]}'
+            '{"scene":"Scene","character_sheet":"Maya: 27, she","beats":[{"text":"Beat one"},{"text":"Beat two"}]}'
         )
-        self.assertEqual(result, ("Beat one\n\nBeat two",))
+        self.assertEqual(result, ("Scene\n\nMaya: 27, she\n\nBeat one\n\nBeat two",))
 
 
 if __name__ == "__main__":

tests/test_dumas_image_nodes.py

@@ -505,6 +505,11 @@ class DumasImageNodeTests(unittest.TestCase):
         self.assertIs(result[15], dave_image)
         self.assertIs(result[16], cafe_image)
         self.assertIsNone(result[17])
+        self.assertIn("<Picture 1> Dave", result[24])
+        self.assertNotIn("<Picture 2> Coffee Shop", result[24])
+        self.assertIn("<Picture 2> Coffee Shop", result[25])
+        self.assertIn("Location context for <Picture 2> Coffee Shop", result[25])
+        self.assertEqual(result[26], "")
 
     def test_h3_prompt_curator_renumbers_explicit_reference_tags(self):
         node = self.image_nodes.DumasH3PromptCuratorNode()
@@ -642,10 +647,14 @@ class DumasImageNodeTests(unittest.TestCase):
 
     def test_h3_prompt_curator_uses_documented_reference_limits(self):
         node = self.image_nodes.DumasH3PromptCuratorNode()
-        self.assertEqual(len(node.RETURN_TYPES), 24)
+        self.assertEqual(len(node.RETURN_TYPES), 33)
         self.assertEqual(node.RETURN_NAMES[1:10], tuple(f"ref_image_{i}" for i in range(1, 10)))
         self.assertEqual(node.RETURN_NAMES[12:15], ("anchor", "sounds", "bgm"))
         self.assertEqual(node.RETURN_NAMES[15:24], tuple(f"original_ref_{i}" for i in range(1, 10)))
+        self.assertEqual(
+            node.RETURN_NAMES[24:33],
+            tuple(f"compiled_ref_description_{i}" for i in range(1, 10)),
+        )
 
     def test_normalize_reference_upgrades_generic_summary_with_socket_picture_id(self):
         image = FakeTensorBatch()