Add handoff context frames

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

@@ -33,18 +33,13 @@
   - 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)`
+- `Dumas H3 Long Videos`
-  - 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
+  - Inputs/outputs: the current upstream `MiniMax-H3-Longvideos` sampler surface, exposed under the existing `DumasH3LongVideos` key for saved Dumas workflows.
-  - Outputs: `images`, `audio`, `info`, `script`, `frames_per_shot`, `total_frames`, `shots`, `video_seconds`, `fps`, `fps_int`, `latent`, `soundscape`
+  - The local Dumas prompt-engineering fork has been removed from this node. Long Videos now wraps the upstream sampler/engine directly so it can track the source project again.
-  - First-pass Dumas port of the `MiniMax-H3-Longvideos` sampler, brought in as a local starting point for long-form H3 chaining work.
+  - Upstream compatibility keys `H3LongVideos`, `H3LongVideosFL2VA`, `H3LongVideosV1`, and `H3LongVideosREF2VA` are also registered to the same class.
-  - Keeps the upstream split-beats / handoff / ref-routing behavior close to source so future Dumas-specific improvements can be compared against a known baseline.
+  - The old Dumas browser widget grouping script is disabled for this node because it targeted controls that no longer exist on the upstream sampler.
-  - Full user guide: [`H3_LONG_VIDEOS_GUIDE.md`](./H3_LONG_VIDEOS_GUIDE.md)
+  - `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.
-  - Only the canonical `DumasH3LongVideos` node key is exposed now; the older FL2VA/REF2VA alias entries are no longer duplicated in the Add Node menu.
+  - Upstream license text is included in [`H3_LONGVIDEOS_UPSTREAM_LICENSE.txt`](./H3_LONGVIDEOS_UPSTREAM_LICENSE.txt).
-  - 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 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`
@@ -54,7 +49,17 @@
 - `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`, `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 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`
   - Inputs: `shot_seconds`, `fps`, optional `cap_to_h3_max`
@@ -76,12 +81,34 @@
   - Output: `reference`
   - Builds one structured `REFERENCE` object for a location/environment so H3 can use the same socket type for both character and scenic refs.
 
+- `Dumas Character Helper`
+  - Inputs: `image1`, `image2`, picture IDs, character identity fields, `general`, `wardrobe`
+  - Outputs: `image1`, `image2`, `reference_prompt`, `wardrobe`, `reference1`, `reference2`
+  - Restores the original general-purpose helper shape while also emitting two structured `REFERENCE` objects for the prompt curator.
+  - The structured references carry the same character name, alias, age, height, gender, nationality, occupation, accent, wardrobe, and notes, so mentioning the character name in `Dumas H3 Prompt Curator` can include both helper images and the character facts automatically.
+
+- `Dumas Location Helper`
+  - Inputs: `image1`, `image2`, picture IDs, `location_id`, `name`, `alias`, `description`, `general`
+  - Outputs: `image1`, `image2`, `reference_prompt`, `reference1`, `reference2`
+  - Matching general-purpose helper for environments/locations: pass two images through unchanged, emit location reference prompt text, and provide two structured `REFERENCE` objects for the prompt curator.
+  - The structured references carry the same location name, alias, description, and notes, so mentioning the location name in `Dumas H3 Prompt Curator` can include both helper images and the location context automatically.
+
 - `Dumas Anchor Style`
   - Inputs: `anchor_style`, `style_description`
   - Output: `anchor`
   - Offers a large preset dropdown of anchor-style titles such as cinematic action movie, comedy, found footage, 90s sitcom, mobile/cell phone captured, news broadcast, mockumentary, heist thriller, cyberpunk neon, nature documentary, courtroom drama, and more.
   - The preset wording is tuned for H3-safe persistent anchors: camera language, lighting, texture, production treatment, and tone, without naming characters or describing one-off actions.
-  - Selecting a preset fills the editable description field, and the edited multiline description is the `STRING` value passed downstream into H3 anchor sockets such as `anchor_override`.
+  - Selecting a preset fills the editable description field, and the edited multiline description is the `STRING` value passed downstream.
+
+- `Dumas Soundscape Helper`
+  - Inputs: `soundscape`, `soundscape_description`
+  - Output: `soundscape`
+  - Matching soundscape helper for standalone H3 prompts. Pick a preset such as quiet interior, rainy street, cafe, city night, forest, industrial, or silent; the preset fills the editable textbox, and the edited text flows into `Dumas H3 Prompt Curator`.
+
+- `Dumas Background Music Helper`
+  - Inputs: `bgm`, `bgm_description`
+  - Output: `bgm`
+  - Matching BGM helper for standalone H3 prompts. Pick a preset such as subtle tension, cinematic suspense, emotional piano, dark ambient, hopeful orchestral, retro synth, action pulse, lo-fi, or no vocals; the preset fills the editable textbox, and the edited text flows into `Dumas H3 Prompt Curator`.
 
 - `Dumas JSON String to Object`
   - Input: `json_string`
@@ -239,7 +266,7 @@ decr  -> use index - 1
 
 `Dumas H3 Plan Attach Scene Images` and `Dumas H3 Plan Extract Scene Images` are a companion pair for `ComfyUI-MiniMaxH3-Contex-Loop` and the local `ref2v` lane. The upstream H3 plan node cannot dynamically grow nine new image sockets for every JSON-defined scene, so Dumas stores scene image bindings beside the plan using a lightweight token and an in-memory registry. That keeps `plan.json` archiving intact while still letting you wire up nine IMAGE sockets per scene through chained helper nodes.
 
-`Dumas Character Reference` and `Dumas Location Reference` live in `Dumas/MiniMax`. Both output a structured `REFERENCE` object that carries the image plus its semantic payload. `Dumas H3 Long Videos` accepts those `REFERENCE` sockets directly on `ref_1`..`ref_9`, resolves `<Picture N>` against the wired slot positions, and can also pull character wardrobe context from the structured ref data when `character_memory` is left blank.
+`Dumas Character Helper` is the restored two-image/text helper for general H3 workflows, and `Dumas Location Helper` mirrors it for scene/environment references. Both helpers also emit structured `REFERENCE` sockets for the curator. The structured `Dumas Character Reference` and `Dumas Location Reference` nodes remain available separately for workflows that want a single `REFERENCE` socket. `Dumas H3 Prompt Curator` consumes those structured references plus optional anchor, soundscape, and BGM strings, assigns the final `<Picture N>` numbering, and outputs only the compacted images the prompt actually mentions.
 
 `Dumas Strip Iteration Suffix` keeps the part before the first underscore and drops the rest. Names like `char123_pose_final.png` become `char123.png`, while names with no underscore such as `char123.png` are left untouched.
 

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()
+        beats = [{"text": _DEFAULT_BEAT}]
-    return {"beats": beats}
+    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 "
+        "Build an upstream MiniMax H3 Long Videos prompt: optional scene paragraph, "
-        "editor that can append directive examples and expose per-shot controls for "
+        "optional character sheet, then one blank-line-separated textbox per beat. "
-        "timing, continuity, ref behavior, anchor additions, soundscape, and music."
+        "Per-beat helpers only emit directives the upstream node understands."
     )
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("prompt",)

dumas_h3_latent_upscale.py

@@ -1,6 +1,8 @@
+from contextlib import contextmanager
 from functools import lru_cache
 import gc
 import glob
+import logging
 import math
 import os
 import re
@@ -40,9 +42,12 @@ LATENTS_STD = [
 ]
 
 _LATENT_UPSCALE_FOLDER = "latent_upscale_models"
+LOGGER = logging.getLogger(__name__)
 MP_UNIT = 1024 * 1024
 RES_MULTIPLE = 32
 CUDA_MODEL_CHUNK_LENGTH = 17
+CUDA_MODEL_MIN_TOTAL_VRAM_BYTES = 10 * 1024 * 1024 * 1024
+_MODEL_HOLD_DEPTH = 0
 
 
 def _uses_cuda_model_upscale(param):
@@ -53,15 +58,83 @@ def _uses_cuda_model_upscale(param):
     return device == "cuda" and (mode == "model" or has_model_name)
 
 
-def _effective_temporal_params(param):
+def _effective_temporal_params(param, frame_count=None):
     chunk_length = int(param.get("chunk_length", 0) or 0)
     temporal_overlap = int(param.get("temporal_overlap", 0) or 0)
     if _uses_cuda_model_upscale(param):
-        chunk_length = CUDA_MODEL_CHUNK_LENGTH if chunk_length <= 0 else min(chunk_length, CUDA_MODEL_CHUNK_LENGTH)
+        if chunk_length <= 0:
-        temporal_overlap = min(max(0, temporal_overlap), max(0, chunk_length - 17))
+            chunk_length = 85
+            temporal_overlap = 17
     return chunk_length, temporal_overlap
 
 
+def _is_oom_error(exc):
+    text = str(exc).lower()
+    return "out of memory" in text or "exhausted its gpu spatial fallbacks" in text
+
+
+def _should_retry_temporal_before_spatial(param):
+    return _uses_cuda_model_upscale(param) and int(param.get("chunk_length", 0) or 0) > CUDA_MODEL_CHUNK_LENGTH
+
+
+def _cuda_total_memory_bytes():
+    try:
+        if not torch.cuda.is_available():
+            return 0
+        if hasattr(torch.cuda, "mem_get_info"):
+            _free, total = torch.cuda.mem_get_info()
+            return int(total)
+        current_device = torch.cuda.current_device() if hasattr(torch.cuda, "current_device") else 0
+        props = torch.cuda.get_device_properties(current_device)
+        return int(getattr(props, "total_memory", 0) or 0)
+    except Exception:
+        return 0
+
+
+def _should_skip_cuda_model_upscale(param):
+    total = _cuda_total_memory_bytes()
+    return _uses_cuda_model_upscale(param) and 0 < total < CUDA_MODEL_MIN_TOTAL_VRAM_BYTES
+
+
+def _fallback_to_interp(video, param, reason):
+    method = param.get("method", "bilinear")
+    LOGGER.warning("H3 latent upscale model %s; using %s interpolation instead", reason, method)
+    try:
+        gc.collect()
+    except Exception:
+        pass
+    if torch.cuda.is_available():
+        try:
+            torch.cuda.empty_cache()
+        except Exception:
+            pass
+    interp_param = dict(param)
+    interp_param["mode"] = "interp"
+    return upscale_video_interp(video, interp_param)
+
+
+def _retry_with_smaller_temporal(video, param, upscaler, exc):
+    if not _is_oom_error(exc):
+        raise exc
+    smaller = _shrink_temporal_param(param)
+    if smaller is None:
+        raise exc
+    LOGGER.info(
+        "H3 latent upscale temporal OOM: retrying with chunk_length=%s overlap=%s",
+        smaller.get("chunk_length"), smaller.get("temporal_overlap"),
+    )
+    try:
+        gc.collect()
+    except Exception:
+        pass
+    if torch.cuda.is_available():
+        try:
+            torch.cuda.empty_cache()
+        except Exception:
+            pass
+    return _upscale_video_temporal_chunks(video, smaller, upscaler)
+
+
 def _models_dir():
     try:
         if _LATENT_UPSCALE_FOLDER not in folder_paths.folder_names_and_paths:
@@ -349,7 +422,7 @@ def load_upscale_model(name, device, precision):
     return model
 
 
-def unload_upscale_model(name, device, precision):
+def _unload_upscale_model_now(name, device, precision):
     cache_key = f"{name}::{device}::{precision}"
     model = _MODEL_CACHE.get(cache_key)
     if model is not None and str(next(model.parameters()).device) != "cpu":
@@ -361,6 +434,22 @@ def unload_upscale_model(name, device, precision):
             pass
 
 
+def unload_upscale_model(name, device, precision):
+    if _MODEL_HOLD_DEPTH > 0:
+        return
+    _unload_upscale_model_now(name, device, precision)
+
+
+@contextmanager
+def _hold_upscale_model_loaded():
+    global _MODEL_HOLD_DEPTH
+    _MODEL_HOLD_DEPTH += 1
+    try:
+        yield
+    finally:
+        _MODEL_HOLD_DEPTH -= 1
+
+
 def _compute_upscale_target(width, height, h_in, w_in):
     ds = 16
     w_px = float(width)
@@ -617,12 +706,25 @@ def upscale_video_model(video, param):
     except RuntimeError as exc:
         if "out of memory" not in str(exc).lower():
             raise
+        if _should_retry_temporal_before_spatial(param):
+            raise RuntimeError(
+                "out of memory: retry H3 latent upscale with a smaller temporal chunk "
+                "before spatial fallback"
+            ) from exc
         smaller = _shrink_model_tile_param(param)
         if smaller is None:
             raise RuntimeError(
                 "H3 latent upscale exhausted its GPU spatial fallbacks. "
                 "Reduce the target size, tile size, or split the shot earlier."
             ) from exc
+        LOGGER.info(
+            "H3 latent upscale model OOM: retrying with tile_size_mode=%s rows=%s cols=%s tile=%sx%s",
+            smaller.get("tile_size_mode"),
+            smaller.get("grid_rows"),
+            smaller.get("grid_cols"),
+            smaller.get("tile_width"),
+            smaller.get("tile_height"),
+        )
         try:
             gc.collect()
         except Exception:
@@ -683,12 +785,22 @@ def _upscale_video_model_tiled(video, param):
     # If the requested tile is not smaller than the target on either axis,
     # the tiled path would just duplicate work.
     if len(rows) == 1 and len(cols) == 1:
+        LOGGER.info(
+            "H3 latent upscale model: single core pass, tokens=%s target=%sx%s",
+            int(video.shape[2]), w_out, h_out,
+        )
         return _upscale_video_model_core(video, param)
 
     scale_h = h_out / float(h_in)
     scale_w = w_out / float(w_in)
     orig_dtype = video.dtype
     out = torch.zeros((video.shape[0], video.shape[1], video.shape[2], h_out, w_out), device="cpu", dtype=orig_dtype)
+    LOGGER.info(
+        "H3 latent upscale model: %s spatial tiles, tokens=%s target=%sx%s tile_mode=%s tile=%sx%s overlap=%sx%s",
+        len(rows) * len(cols), int(video.shape[2]), w_out, h_out, mode, tile_w, tile_h,
+        spatial_w_overlap if mode == "rows_cols" else overlap,
+        spatial_h_overlap if mode == "rows_cols" else overlap,
+    )
 
     for i, r0 in enumerate(rows):
         tr = trows[i]
@@ -753,43 +865,51 @@ def upscale_video_interp(video, param):
 def _upscale_video_temporal_chunks(video, param, upscaler):
     if video.device.type != "cpu":
         video = video.to(device="cpu", copy=True)
-    chunk_length, temporal_overlap = _effective_temporal_params(param)
+    t = int(video.shape[2])
+    frame_count = _frames_for_tokens(t)
+    chunk_length, temporal_overlap = _effective_temporal_params(param, frame_count)
     chunk_param = dict(param)
     chunk_param["chunk_length"] = chunk_length
     chunk_param["temporal_overlap"] = temporal_overlap
     anchor_strength = float(param.get("anchor_strength", 0.999) or 0.999)
-    t = int(video.shape[2])
-    frame_count = _frames_for_tokens(t)
     if chunk_length <= 0 or frame_count <= chunk_length:
-        return upscaler(video, chunk_param)
+        LOGGER.info(
+            "H3 latent upscale: single temporal chunk, tokens=%s frames=%s chunk_length=%s overlap=%s",
+            t, frame_count, chunk_length, temporal_overlap,
+        )
+        try:
+            return upscaler(video, chunk_param)
+        except RuntimeError as exc:
+            return _retry_with_smaller_temporal(video, chunk_param, upscaler, exc)
 
     bounds = _temporal_segments(t, chunk_length, temporal_overlap)
     if len(bounds) <= 1:
-        return upscaler(video, chunk_param)
+        LOGGER.info(
+            "H3 latent upscale: single temporal segment, tokens=%s frames=%s chunk_length=%s overlap=%s",
+            t, frame_count, chunk_length, temporal_overlap,
+        )
+        try:
+            return upscaler(video, chunk_param)
+        except RuntimeError as exc:
+            return _retry_with_smaller_temporal(video, chunk_param, upscaler, exc)
 
     orig_dtype = video.dtype
     out = None
     out_h = out_w = None
+    LOGGER.info(
+        "H3 latent upscale: %s temporal chunks, tokens=%s frames=%s chunk_length=%s overlap=%s",
+        len(bounds), t, frame_count, chunk_length, temporal_overlap,
+    )
     for i, (k0, f0, k1, f1) in enumerate(bounds):
         chunk = video[:, :, k0:k1].contiguous()
+        LOGGER.info(
+            "H3 latent upscale: temporal chunk %s/%s tokens %s:%s frames %s:%s",
+            i + 1, len(bounds), k0, k1, f0, f1,
+        )
         try:
             chunk_out, chunk_h, chunk_w = upscaler(chunk, chunk_param)
         except RuntimeError as exc:
-            if "out of memory" not in str(exc).lower():
+            return _retry_with_smaller_temporal(video, chunk_param, upscaler, exc)
-                raise
-            smaller = _shrink_temporal_param(chunk_param)
-            if smaller is None:
-                raise
-            try:
-                gc.collect()
-            except Exception:
-                pass
-            if torch.cuda.is_available():
-                try:
-                    torch.cuda.empty_cache()
-                except Exception:
-                    pass
-            return _upscale_video_temporal_chunks(video, smaller, upscaler)
         chunk_out = chunk_out.to(device="cpu", dtype=orig_dtype)
         if out is None:
             out_h, out_w = chunk_h, chunk_w
@@ -819,7 +939,22 @@ def upscale_latent_video(video, param):
     if mode == "off":
         return video, video.shape[-2], video.shape[-1]
     if mode == "model":
-        return _upscale_video_temporal_chunks(video, param, upscale_video_model)
+        if _should_skip_cuda_model_upscale(param):
+            return _fallback_to_interp(video, param, "requires more than this card's VRAM")
+        model_name = param.get("model_name")
+        device = param.get("device", "cuda")
+        precision = param.get("precision", "fp16")
+        dev = torch.device(device if (device == "cpu" or torch.cuda.is_available()) else "cpu")
+        try:
+            with _hold_upscale_model_loaded():
+                try:
+                    return _upscale_video_temporal_chunks(video, param, upscale_video_model)
+                finally:
+                    _unload_upscale_model_now(model_name, dev, precision)
+        except RuntimeError as exc:
+            if not _is_oom_error(exc):
+                raise
+            return _fallback_to_interp(video, param, "exhausted GPU memory")
     return _upscale_video_temporal_chunks(video, param, upscale_video_interp)
 
 
@@ -892,10 +1027,10 @@ class H3LatentUpscaleParams:
                     "tooltip": "Temporal fade schedule over each tile's sampling. Off keeps the fade fixed; narrowing shrinks it over steps; widening grows it over steps."}),
                 "dynamic_fade_min": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 32,
                     "tooltip": "Minimum fade width used by dynamic_fade when it is enabled."}),
-                "chunk_length": ("INT", {"default": 17, "min": 17, "max": 100000, "step": 17,
+                "chunk_length": ("INT", {"default": 85, "min": 17, "max": 100000, "step": 17,
-                    "tooltip": "Temporal chunk length for latent upscale. CUDA model upscale is capped to 17 internally so short long-video shots do not bypass splitting and OOM."}),
+                    "tooltip": "Temporal chunk length for latent upscale. CUDA model upscale keeps this when it splits the shot, but uses 17/0 if this would otherwise process the whole shot as one OOM-prone batch."}),
-                "temporal_overlap": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 17,
+                "temporal_overlap": ("INT", {"default": 17, "min": 0, "max": 100000, "step": 17,
-                    "tooltip": "Temporal overlap between latent chunks. CUDA model upscale uses 0 when capped to one H3 block to minimize peak VRAM."}),
+                    "tooltip": "Temporal overlap between latent chunks. 17 matches the upstream split example; CUDA model upscale drops overlap only for the emergency 17-frame guard path."}),
                 "resize_conditioning": ("BOOLEAN", {"default": False,
                     "tooltip": "Reserved for upstream split compatibility. Leave OFF unless you need the original fallback behavior."}),
                 "anchor_strength": ("FLOAT", {"default": 0.999, "min": 0.0, "max": 1.0, "step": 0.01,

js/anchor_style/index.js

@@ -3,7 +3,7 @@ import { app } from "/scripts/app.js";
 const NODE_NAME = "DumasAnchorStyle";
 const STYLE_INPUT = "anchor_style";
 const DESCRIPTION_INPUT = "style_description";
-const H3_NOTE = " Keep this anchor focused on persistent camera language, lighting, texture, environment treatment, and tone; do not name characters or describe one-off actions.";
+const H3_NOTE = "";
 const PRESETS = {
   "cinematic action movie": "Big-screen action cinema with assertive visual storytelling: dynamic camera placement, strong forward momentum, crisp geography, muscular lighting contrast, practical atmosphere, and a sense of physical consequence. Favor heroic framing, controlled handheld energy or motivated tracking moves, dramatic silhouettes, tasteful lens flares, impact-driven pacing, and polished studio spectacle without drifting into comic-book unreality unless the shot explicitly asks for it." + H3_NOTE,
   "comedy": "Play the scene for comedic readability and timing: clear staging, expressive performances, slightly heightened reactions, clean eyelines, and visual beats that leave room for the joke to land. Use bright approachable lighting, grounded but playful production design, readable framing, and a tone that feels observant, awkward, or absurd without becoming broad parody unless the action supports it." + H3_NOTE,
@@ -51,28 +51,72 @@ const PRESETS = {
   "fantasy adventure": "Rousing fantasy-adventure language: scenic scale, adventurous clarity, tactile costume-and-prop detail, and camera movement that feels exploratory rather than oppressive. Favor storybook geography, weathered materials, golden or stormy atmosphere, and a tone of peril, wonder, and forward motion." + H3_NOTE,
 };
 
+const SOUNDSCAPE_PRESETS = {
+  "quiet interior": "quiet indoor room tone, faint ventilation and distant household ambience",
+  "rainy street": "steady rain, wet pavement, distant traffic hum",
+  "cafe": "low room tone, faint glassware, cutlery, and muted conversation",
+  "city night": "distant traffic hum, occasional horn, night air",
+  "forest": "wind in leaves, distant birds, soft natural ambience",
+  "industrial": "large interior reverb, distant metal ticks, low machine hum",
+  "silent": "no dialogue, no vocals, only the natural ambient bed of the scene",
+  "custom": "",
+};
+
+const BGM_PRESETS = {
+  "none": "",
+  "subtle tension": "low, restrained tension bed with sparse pulses and no vocals",
+  "cinematic suspense": "cinematic suspense score with muted strings, low drones, and controlled rising pressure",
+  "emotional piano": "soft emotional piano underscoring with gentle space and no vocals",
+  "dark ambient": "dark ambient music bed with deep drones, distant texture, and slow unease",
+  "hopeful orchestral": "hopeful orchestral underscore with warm strings, gentle brass, and restrained lift",
+  "retro synth": "retro synth score with analog pulses, warm pads, and steady momentum",
+  "action pulse": "driving action pulse with percussion, rhythmic bass, and urgent forward motion",
+  "lo-fi": "soft lo-fi instrumental bed with mellow rhythm and warm tape texture",
+  "no vocals": "instrumental background music only, no singing, no lyrics, no vocal hooks",
+  "custom": "",
+};
+
+const NODE_CONFIGS = {
+  [NODE_NAME]: {
+    presetInput: STYLE_INPUT,
+    descriptionInput: DESCRIPTION_INPUT,
+    presets: PRESETS,
+  },
+  DumasSoundscapeHelper: {
+    presetInput: "soundscape",
+    descriptionInput: "soundscape_description",
+    presets: SOUNDSCAPE_PRESETS,
+  },
+  DumasBackgroundMusicHelper: {
+    presetInput: "bgm",
+    descriptionInput: "bgm_description",
+    presets: BGM_PRESETS,
+  },
+};
+
 function findWidget(node, name) {
   return (node.widgets || []).find((widget) => widget?.name === name) || null;
 }
 
 app.registerExtension({
-  name: "Dumas.AnchorStyle",
+  name: "Dumas.PresetTextHelpers",
   async beforeRegisterNodeDef(nodeType, nodeData) {
-    if (nodeData?.name !== NODE_NAME) return;
+    const config = NODE_CONFIGS[nodeData?.name];
+    if (!config) return;
 
     const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
 
     nodeType.prototype.onNodeCreated = function onNodeCreated() {
       const created = originalOnNodeCreated?.apply(this, arguments);
-      const styleWidget = findWidget(this, STYLE_INPUT);
+      const styleWidget = findWidget(this, config.presetInput);
-      const descriptionWidget = findWidget(this, DESCRIPTION_INPUT);
+      const descriptionWidget = findWidget(this, config.descriptionInput);
       if (!styleWidget || !descriptionWidget) return created;
 
       const originalCallback = styleWidget.callback;
       styleWidget.callback = (...args) => {
         const selected = String(styleWidget.value || "");
-        if (Object.hasOwn(PRESETS, selected)) {
+        if (Object.hasOwn(config.presets, selected)) {
-          descriptionWidget.value = PRESETS[selected];
+          descriptionWidget.value = config.presets[selected];
           descriptionWidget.inputEl?.dispatchEvent(new Event("input", { bubbles: true }));
         }
         this.setDirtyCanvas?.(true, true);

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"],
+  remove: ["remove", "removed", "off"],
-  continuity: ["continuity"],
+  add: ["add", "wear", "wearing"],
-  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"],
 };
 const DIRECTIVE_EXAMPLES = [
-  ["wardrobe set", "wardrobe: Maya = grey shorts, red jacket"],
+  ["remove", "remove: red jacket"],
-  ["wardrobe add", "wardrobe: Maya += red jacket"],
+  ["off", "off: steel collar"],
-  ["wardrobe remove", "wardrobe: Maya -= red jacket"],
+  ["add", "add: white shirt underneath"],
-  ["seconds", "seconds: 8"],
+  ["wearing", "wearing: black coat"],
-  ["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"],
 ];
 
 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");
+    const removeInput = document.createElement("input");
-    secondsInput.className = "dh3bp-input";
+    removeInput.className = "dh3bp-input";
-    secondsInput.type = "text";
+    removeInput.type = "text";
-    secondsInput.placeholder = "8";
+    removeInput.placeholder = "red jacket";
-    secondsInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.seconds);
+    removeInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.remove);
-    secondsInput.addEventListener("input", () => {
+    removeInput.addEventListener("input", () => {
-      applyTextUpdate(setDirectiveValue(textarea.value, "seconds", MANAGED_DIRECTIVES.seconds, secondsInput.value));
+      applyTextUpdate(setDirectiveValue(textarea.value, "remove", MANAGED_DIRECTIVES.remove, removeInput.value));
     });
 
-    const continuitySelect = document.createElement("select");
+    const addInput = document.createElement("input");
-    continuitySelect.className = "dh3bp-select";
+    addInput.className = "dh3bp-input";
-    CONTINUITY_OPTIONS.forEach((value) => {
+    addInput.type = "text";
-      const option = document.createElement("option");
+    addInput.placeholder = "white shirt underneath";
-      option.value = value;
+    addInput.value = readDirectiveValue(beat.text, MANAGED_DIRECTIVES.add);
-      option.textContent = value || "Default";
+    addInput.addEventListener("input", () => {
-      continuitySelect.appendChild(option);
+      applyTextUpdate(setDirectiveValue(textarea.value, "add", MANAGED_DIRECTIVES.add, addInput.value));
-    });
-    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));
     });
 
     controls.append(
-      buildField({ labelText: "Seconds", input: secondsInput }),
+      buildField({ labelText: "Remove from memory", input: removeInput }),
-      buildField({ labelText: "Continuity", input: continuitySelect }),
+      buildField({ labelText: "Add to memory", input: addInput }),
-      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 }),
     );
 
     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",
+  name: "Dumas.H3LongVideos.UpstreamWrapper",
-  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;
-    };
-  },
 });

js/h3_prompt_curator/index.js

@@ -0,0 +1,85 @@
+import { app } from "/scripts/app.js";
+
+const NODE_NAME = "DumasH3PromptCurator";
+const EXPECTED_OUTPUTS = [
+  "prompt",
+  "ref_image_1",
+  "ref_image_2",
+  "ref_image_3",
+  "ref_image_4",
+  "ref_image_5",
+  "ref_image_6",
+  "ref_image_7",
+  "ref_image_8",
+  "ref_image_9",
+  "reference_count",
+  "debug",
+  "anchor",
+  "sounds",
+  "bgm",
+  "original_ref_1",
+  "original_ref_2",
+  "original_ref_3",
+  "original_ref_4",
+  "original_ref_5",
+  "original_ref_6",
+  "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);
+
+function pruneStaleOutputs(node) {
+  if (!Array.isArray(node.outputs)) return;
+
+  const byName = new Map();
+  for (const output of node.outputs) {
+    if (!output?.name || !EXPECTED_NAMES.has(output.name) || byName.has(output.name)) continue;
+    byName.set(output.name, output);
+  }
+
+  const nextOutputs = [];
+  for (const name of EXPECTED_OUTPUTS) {
+    const existing = byName.get(name);
+    if (existing) {
+      nextOutputs.push(existing);
+    }
+  }
+
+  if (nextOutputs.length && nextOutputs.length !== node.outputs.length) {
+    node.outputs = nextOutputs;
+    node.size = node.computeSize?.() || node.size;
+    node.setDirtyCanvas?.(true, true);
+  }
+}
+
+app.registerExtension({
+  name: "Dumas.H3PromptCuratorOutputs",
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== NODE_NAME) return;
+
+    const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
+    const originalOnConfigure = nodeType.prototype.onConfigure;
+
+    nodeType.prototype.onNodeCreated = function onNodeCreated() {
+      const created = originalOnNodeCreated?.apply(this, arguments);
+      pruneStaleOutputs(this);
+      return created;
+    };
+
+    nodeType.prototype.onConfigure = function onConfigure() {
+      const configured = originalOnConfigure?.apply(this, arguments);
+      pruneStaleOutputs(this);
+      return configured;
+    };
+  },
+});

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": "Maya enters the room."},
-                    {"text": "wardrobe: Maya = red jacket\nShe sits at the table."},
+                    {"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"
+                "A rainy kitchen at night.\n\n"
-                "wardrobe: Maya = red jacket\nShe sits at the table.\n\n"
+                "Maya: 27, she, red jacket, silver hair.\n\n"
-                "music: low synth pulse"
+                "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

@@ -356,6 +356,306 @@ class DumasImageNodeTests(unittest.TestCase):
         required = self.image_nodes.DumasLocationReferenceNode.INPUT_TYPES()["required"]
         self.assertNotIn("picture_id", required)
 
+    def test_character_helper_restores_image_and_text_outputs(self):
+        node = self.image_nodes.DumasCharacterHelperNode()
+        image1 = FakeTensorBatch()
+        image2 = FakeTensorBatch()
+
+        result = node.build_character_text(
+            image1=image1,
+            image2=image2,
+            image1_picture_id="1",
+            image2_picture_id="2",
+            character_id="char_dave",
+            name="Dave",
+            alias="The Locksmith",
+            gender="male",
+            age="41",
+            nationality="English",
+            occupation="a detective",
+            height_feet="6",
+            height_inches="2",
+            accent="English",
+            general="Moves carefully and notices every exit",
+            wardrobe="weathered red flight jacket, grey cargo shorts, black boots",
+        )
+
+        self.assertIs(result[0], image1)
+        self.assertIs(result[1], image2)
+        self.assertIn("<Picture 1> and <Picture 2> reference the same character", result[2])
+        self.assertIn("Dave is also known as The Locksmith", result[2])
+        self.assertIn("is 41 years old", result[2])
+        self.assertEqual(result[3], "Dave = weathered red flight jacket, grey cargo shorts, black boots")
+        self.assertIs(result[4]["image"], image1)
+        self.assertIs(result[5]["image"], image2)
+        self.assertEqual(result[4]["id"], "char-dave")
+        self.assertEqual(result[5]["id"], "char-dave")
+        self.assertEqual(result[4]["name"], "Dave")
+        self.assertEqual(result[4]["aliases"], ["The Locksmith"])
+        self.assertEqual(result[4]["facts"]["age"], "41")
+        self.assertEqual(result[4]["facts"]["height_feet"], "6")
+        self.assertEqual(result[4]["facts"]["height_inches"], "2")
+        self.assertEqual(result[4]["wardrobe"], "weathered red flight jacket, grey cargo shorts, black boots")
+        self.assertEqual(len(result), 6)
+
+    def test_location_helper_matches_character_helper_shape_without_wardrobe(self):
+        node = self.image_nodes.DumasLocationHelperNode()
+        image1 = FakeTensorBatch()
+        image2 = FakeTensorBatch()
+
+        result = node.build_location_text(
+            image1=image1,
+            image2=image2,
+            image1_picture_id="3",
+            image2_picture_id="4",
+            location_id="coffee-shop-01",
+            name="Coffee Shop",
+            alias="Cafe Interior",
+            description="Warm tungsten lighting, narrow counter, rainy front window",
+            general="Evening ambience, cramped but cozy",
+        )
+
+        self.assertIs(result[0], image1)
+        self.assertIs(result[1], image2)
+        self.assertIn("<Picture 3> and <Picture 4> reference the same location", result[2])
+        self.assertIn("Coffee Shop is also known as Cafe Interior", result[2])
+        self.assertIn("Warm tungsten lighting, narrow counter, rainy front window.", result[2])
+        self.assertIn("Evening ambience, cramped but cozy.", result[2])
+        self.assertIs(result[3]["image"], image1)
+        self.assertIs(result[4]["image"], image2)
+        self.assertEqual(result[3]["kind"], "location")
+        self.assertEqual(result[4]["kind"], "location")
+        self.assertEqual(result[3]["id"], "coffee-shop-01")
+        self.assertEqual(result[4]["id"], "coffee-shop-01")
+        self.assertEqual(result[3]["name"], "Coffee Shop")
+        self.assertEqual(result[3]["aliases"], ["Cafe Interior"])
+        self.assertEqual(result[3]["description"], "Warm tungsten lighting, narrow counter, rainy front window")
+        self.assertEqual(result[3]["general"], "Evening ambience, cramped but cozy")
+        self.assertEqual(len(result), 5)
+
+    def test_soundscape_helper_defaults_to_selected_preset_description(self):
+        node = self.image_nodes.DumasSoundscapeHelperNode()
+
+        result = node.build_soundscape("rainy street", "")
+
+        self.assertEqual(result[0], "steady rain, wet pavement, distant traffic hum")
+
+    def test_background_music_helper_defaults_to_selected_preset_description(self):
+        node = self.image_nodes.DumasBackgroundMusicHelperNode()
+
+        result = node.build_bgm("subtle tension", "")
+
+        self.assertEqual(result[0], "low, restrained tension bed with sparse pulses and no vocals")
+
+    def test_h3_prompt_curator_compacts_named_references(self):
+        node = self.image_nodes.DumasH3PromptCuratorNode()
+        dave_image = FakeTensorBatch()
+        cafe_image = FakeTensorBatch()
+        van_image = FakeTensorBatch()
+        dave = self.image_nodes.make_reference(
+            kind="character",
+            image=dave_image,
+            name="Dave",
+            aliases="The Locksmith",
+            description="tired eyes, cropped brown hair",
+            wardrobe="red flight jacket",
+        )
+        cafe = self.image_nodes.make_reference(
+            kind="location",
+            image=cafe_image,
+            name="Coffee Shop",
+            description="warm tungsten lighting and rainy windows",
+        )
+        van = self.image_nodes.make_reference(
+            kind="location",
+            image=van_image,
+            name="Blue Van",
+            description="scuffed blue delivery van",
+        )
+
+        result = node.curate_prompt(
+            action_prompt="Dave runs from the Coffee Shop into the rain.",
+            anatomy_guard="auto",
+            subject_count_guard="auto",
+            anchor="grounded handheld thriller",
+            soundscape="steady rain",
+            bgm="low suspense music",
+            ref_1=dave,
+            ref_2=van,
+            ref_3=cafe,
+        )
+
+        prompt = result[0]
+        self.assertIn("<Picture 1> Dave", prompt)
+        self.assertIn("<Picture 2> Coffee Shop", prompt)
+        self.assertIn("Action: Dave runs from the Coffee Shop into the rain.", prompt)
+        self.assertIn("Anatomy guard:", prompt)
+        self.assertIn("Subject count guard:", prompt)
+        self.assertIn("overall_soundscape: steady rain", prompt)
+        self.assertIn("background_music: low suspense music", prompt)
+        self.assertIn("exactly one named character: <Picture 1> Dave", prompt)
+        self.assertIs(result[1], dave_image)
+        self.assertIs(result[2], cafe_image)
+        self.assertIsNone(result[3])
+        self.assertEqual(result[10], 2)
+        self.assertIn("input 3-><Picture 2> Coffee Shop", result[11])
+        self.assertEqual(result[12], "grounded handheld thriller")
+        self.assertEqual(result[13], "steady rain")
+        self.assertEqual(result[14], "low suspense music")
+        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()
+        image1 = FakeTensorBatch()
+        image3 = FakeTensorBatch()
+        unused = FakeTensorBatch()
+        first = self.image_nodes.make_reference(kind="character", image=image1, name="Maya")
+        second = self.image_nodes.make_reference(kind="location", image=unused, name="Lobby")
+        third = self.image_nodes.make_reference(kind="location", image=image3, name="Rooftop")
+
+        result = node.curate_prompt(
+            action_prompt="<Picture 1> Maya crosses to <ref3> as the wind rises.",
+            anatomy_guard="off",
+            subject_count_guard="off",
+            ref_1=first,
+            ref_2=second,
+            ref_3=third,
+        )
+
+        prompt = result[0]
+        self.assertIn("<Picture 1> Maya crosses to <Picture 2>", prompt)
+        self.assertNotIn("<Picture 3>", prompt)
+        self.assertIs(result[1], image1)
+        self.assertIs(result[2], image3)
+        self.assertIsNone(result[3])
+        self.assertEqual(result[10], 2)
+
+    def test_h3_prompt_curator_can_force_subject_count_without_character_refs(self):
+        node = self.image_nodes.DumasH3PromptCuratorNode()
+
+        result = node.curate_prompt(
+            action_prompt="A locked-off shot of the empty corridor.",
+            anatomy_guard="off",
+            subject_count_guard="on",
+        )
+
+        self.assertIn("Subject count guard:", result[0])
+        self.assertIn("Only include the people explicitly described", result[0])
+        self.assertEqual(result[10], 0)
+
+    def test_h3_prompt_curator_treats_helper_image_pair_as_one_character(self):
+        helper = self.image_nodes.DumasCharacterHelperNode()
+        curator = self.image_nodes.DumasH3PromptCuratorNode()
+        image1 = FakeTensorBatch()
+        image2 = FakeTensorBatch()
+        helper_result = helper.build_character_text(
+            image1=image1,
+            image2=image2,
+            image1_picture_id="1",
+            image2_picture_id="2",
+            character_id="char_dave",
+            name="Dave",
+            alias="The Locksmith",
+            gender="male",
+            age="41",
+            nationality="English",
+            occupation="detective",
+            height_feet="6",
+            height_inches="2",
+            accent="English",
+            general="Tired eyes, cropped brown hair",
+            wardrobe="weathered red flight jacket",
+        )
+
+        result = curator.curate_prompt(
+            action_prompt="Dave checks the locked door.",
+            anatomy_guard="on",
+            subject_count_guard="auto",
+            ref_1=helper_result[4],
+            ref_2=helper_result[5],
+        )
+
+        self.assertIs(result[1], image1)
+        self.assertIs(result[2], image2)
+        self.assertEqual(result[10], 2)
+        self.assertIn("Character facts for <Picture 1> Dave", result[0])
+        self.assertIn("41 years old", result[0])
+        self.assertIn("6 foot 2 tall", result[0])
+        self.assertIn("exactly one named character: <Picture 1> Dave", result[0])
+        self.assertNotIn("exactly 2 named characters", result[0])
+
+    def test_h3_prompt_curator_uses_location_helper_references_by_name(self):
+        helper = self.image_nodes.DumasLocationHelperNode()
+        curator = self.image_nodes.DumasH3PromptCuratorNode()
+        image1 = FakeTensorBatch()
+        image2 = FakeTensorBatch()
+        helper_result = helper.build_location_text(
+            image1=image1,
+            image2=image2,
+            image1_picture_id="1",
+            image2_picture_id="2",
+            location_id="coffee_shop",
+            name="Coffee Shop",
+            alias="Cafe Interior",
+            description="Warm tungsten lighting, narrow counter, rainy front window",
+            general="Evening ambience, cramped but cozy",
+        )
+
+        result = curator.curate_prompt(
+            action_prompt="A slow push through the Coffee Shop as rain streaks the windows.",
+            anatomy_guard="on",
+            subject_count_guard="auto",
+            ref_1=helper_result[3],
+            ref_2=helper_result[4],
+        )
+
+        self.assertIs(result[1], image1)
+        self.assertIs(result[2], image2)
+        self.assertEqual(result[10], 2)
+        self.assertIn("<Picture 1> Coffee Shop", result[0])
+        self.assertIn("<Picture 2> Coffee Shop", result[0])
+        self.assertIn("Location context for <Picture 1> Coffee Shop", result[0])
+        self.assertIn("Warm tungsten lighting", result[0])
+        self.assertNotIn("Subject count guard:", result[0])
+
+    def test_h3_prompt_curator_defaults_anatomy_guard_to_on(self):
+        required = self.image_nodes.DumasH3PromptCuratorNode.INPUT_TYPES()["required"]
+
+        self.assertEqual(required["anatomy_guard"][1]["default"], "on")
+
+    def test_helper_node_mappings_use_general_purpose_helpers(self):
+        mappings = self.image_nodes.NODE_CLASS_MAPPINGS
+        display = self.image_nodes.NODE_DISPLAY_NAME_MAPPINGS
+
+        self.assertIs(mappings["DumasCharacterHelper"], self.image_nodes.DumasCharacterHelperNode)
+        self.assertIs(mappings["DumasLocationHelper"], self.image_nodes.DumasLocationHelperNode)
+        self.assertIs(mappings["DumasSoundscapeHelper"], self.image_nodes.DumasSoundscapeHelperNode)
+        self.assertIs(mappings["DumasBackgroundMusicHelper"], self.image_nodes.DumasBackgroundMusicHelperNode)
+        self.assertIs(mappings["DumasH3PromptCurator"], self.image_nodes.DumasH3PromptCuratorNode)
+        self.assertEqual(display["DumasCharacterHelper"], "Dumas Character Helper")
+        self.assertEqual(display["DumasLocationHelper"], "Dumas Location Helper")
+        self.assertEqual(display["DumasSoundscapeHelper"], "Dumas Soundscape Helper")
+        self.assertEqual(display["DumasBackgroundMusicHelper"], "Dumas Background Music Helper")
+        self.assertEqual(display["DumasH3PromptCurator"], "Dumas H3 Prompt Curator")
+
+    def test_h3_prompt_curator_uses_documented_reference_limits(self):
+        node = self.image_nodes.DumasH3PromptCuratorNode()
+        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()
 
@@ -417,6 +717,19 @@ class DumasImageNodeTests(unittest.TestCase):
         self.assertIn("real time", result[0])
         self.assertNotIn("persistent camera language", result[0])
 
+    def test_anchor_style_node_strips_legacy_persistent_anchor_note(self):
+        node = self.image_nodes.DumasAnchorStyleNode()
+        legacy = (
+            "Gritty handheld realism. Keep this anchor focused on persistent camera "
+            "language, lighting, texture, environment treatment, and tone; do not "
+            "name characters or describe one-off actions."
+        )
+
+        result = node.build_anchor("cinematic action movie", legacy)
+
+        self.assertEqual(result[0], "Gritty handheld realism.")
+        self.assertNotIn("persistent camera language", result[0])
+
     def test_anchor_style_node_prefers_manual_description_edits(self):
         node = self.image_nodes.DumasAnchorStyleNode()
         custom = "Lo-fi pirate broadcast with smeared highlights and anxious zoom corrections."