Strip legacy anchor style note

README.md

@@ -56,6 +56,13 @@
   - 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.
 
+- `Dumas H3 Prompt Curator`
+  - Inputs: `action_prompt`, `anatomy_guard`, `subject_count_guard`, optional `anchor`, optional `soundscape`, optional `ref_1` through `ref_9`
+  - Outputs: `prompt`, `ref_image_1` through `ref_image_9`, `reference_count`, `debug`
+  - 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.
+  - Adds curated reference context, anatomy guard text, optional subject-count guard text, anchor/style text, and `overall_soundscape:` 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`
   - Outputs: `seconds`, `frames`, `info`
@@ -76,6 +83,18 @@
   - 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`
@@ -83,6 +102,11 @@
   - 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`.
 
+- `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, then edit the text that flows into `Dumas H3 Prompt Curator`.
+
 - `Dumas JSON String to Object`
   - Input: `json_string`
   - Output: parsed `JSON`
@@ -239,7 +263,7 @@ 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, 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_latent_upscale.py

@@ -46,6 +46,7 @@ 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
 
 
@@ -76,6 +77,42 @@ 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
@@ -902,15 +939,22 @@ def upscale_latent_video(video, param):
     if mode == "off":
         return video, video.shape[-2], video.shape[-1]
     if mode == "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)
 
 

dumas_image_nodes.py

@@ -27,6 +27,25 @@ _H3_PLAN_IMAGE_BINDINGS_CAP = 128
 _H3_PLAN_IMAGE_SLOTS = 9
 _FOLDER_IMAGE_EXTS = (".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".tiff", ".tif")
 _ANCHOR_STYLE_H3_NOTE = ""
+_ANCHOR_STYLE_LEGACY_NOTE_RE = re.compile(
+    r"\s*Keep this anchor focused on persistent camera language, lighting, "
+    r"texture, environment treatment, and tone; do not name characters or "
+    r"describe one-off actions\.?",
+    re.I,
+)
+_H3_PROMPT_REF_SLOTS = 9
+_H3_PROMPT_MAX_CHARS = 7000
+_PICTURE_TAG_RE = re.compile(r"<\s*picture[\s_\-]*(\d+)\s*>", re.I)
+_REF_TAG_RE = re.compile(r"<\s*ref[\s_\-]*(\d+)\s*>", re.I)
+_ANATOMY_GUARD_TEXT = (
+    "Each person has one head, two arms, two hands with five fingers on each hand, "
+    "and two legs with two feet. Limbs stay attached to the correct body and move "
+    "only with the person they belong to."
+)
+_SUBJECT_COUNT_FALLBACK_TEXT = (
+    "Only include the people explicitly described in the action. Do not invent "
+    "extra people, doubles, duplicate bodies, background performers, or extra faces."
+)
 _ANCHOR_STYLE_PRESETS = OrderedDict(
     [
         (
@@ -346,6 +365,47 @@ _ANCHOR_STYLE_PRESETS = OrderedDict(
         ),
     ]
 )
+_SOUNDSCAPE_PRESETS = OrderedDict(
+    [
+        (
+            "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", ""),
+    ]
+)
+
+
+def _soundscape_options():
+    return list(_SOUNDSCAPE_PRESETS.keys())
+
+
+def _soundscape_description(soundscape_name):
+    return _SOUNDSCAPE_PRESETS.get(soundscape_name, "")
 _LOAD_IMAGES_FOLDER_DEFAULT_STATE = {
     "version": 1,
     "folder": "",
@@ -366,6 +426,11 @@ def _anchor_style_description(style_name):
     return _ANCHOR_STYLE_PRESETS.get(str(style_name or "").strip().lower(), "")
 
 
+def _clean_anchor_style_text(text):
+    cleaned = _ANCHOR_STYLE_LEGACY_NOTE_RE.sub("", str(text or ""))
+    return re.sub(r"[ \t]{2,}", " ", cleaned).strip()
+
+
 def _clean_input_token_value(value):
     cleaned = ""
     if value is not None:
@@ -920,6 +985,248 @@ def normalize_reference(value, picture_id=None, allow_image_fallback=True):
     )
 
 
+def _reference_text(value):
+    return " ".join(str(value or "").split()).strip()
+
+
+def _reference_sentence(value):
+    text = _reference_text(value)
+    if text and text[-1] not in ".!?":
+        text += "."
+    return text
+
+
+def _reference_name_keys(ref):
+    names = []
+    for key in ("name", "id"):
+        value = _reference_text(ref.get(key))
+        if value:
+            names.append(value)
+    for alias in ref.get("aliases") or []:
+        value = _reference_text(alias)
+        if value:
+            names.append(value)
+    seen = set()
+    out = []
+    for name in names:
+        key = name.lower()
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(name)
+    return out
+
+
+def _reference_image(ref):
+    if not isinstance(ref, dict):
+        return None
+    return ref.get("image")
+
+
+def _normalize_prompt_refs(raw_refs):
+    refs = []
+    for slot_number, raw in enumerate(raw_refs or (), 1):
+        if raw is None:
+            refs.append(None)
+            continue
+        try:
+            ref = normalize_reference(raw, picture_id=slot_number, allow_image_fallback=False)
+        except Exception:
+            refs.append(None)
+            continue
+        if _reference_image(ref) is None:
+            refs.append(None)
+        else:
+            refs.append(ref)
+    return refs
+
+
+def _explicit_reference_tags(text):
+    return sorted(
+        {
+            int(match.group(1))
+            for pattern in (_PICTURE_TAG_RE, _REF_TAG_RE)
+            for match in pattern.finditer(text or "")
+        }
+    )
+
+
+def _name_matches_reference(text, ref):
+    haystack = str(text or "")
+    for name in _reference_name_keys(ref):
+        if re.search(r"\b" + re.escape(name) + r"\b", haystack, re.I):
+            return True
+    return False
+
+
+def _selected_prompt_refs(action_prompt, refs):
+    selected = []
+    seen_slots = set()
+    for slot_number in _explicit_reference_tags(action_prompt):
+        if not (1 <= slot_number <= len(refs)):
+            continue
+        ref = refs[slot_number - 1]
+        if ref is None:
+            continue
+        selected.append((slot_number, ref))
+        seen_slots.add(slot_number)
+    for slot_number, ref in enumerate(refs, 1):
+        if slot_number in seen_slots or ref is None:
+            continue
+        if _name_matches_reference(action_prompt, ref):
+            selected.append((slot_number, ref))
+            seen_slots.add(slot_number)
+    return selected
+
+
+def _replace_reference_tags(text, picture_map):
+    def repl(match):
+        original = int(match.group(1))
+        compacted = picture_map.get(original)
+        if compacted is None:
+            return ""
+        return f"<Picture {compacted}>"
+
+    rewritten = _PICTURE_TAG_RE.sub(repl, str(text or ""))
+    rewritten = _REF_TAG_RE.sub(repl, rewritten)
+    return re.sub(r"[ \t]{2,}", " ", rewritten).strip()
+
+
+def _reference_fact_sentence(ref, label):
+    if ref.get("kind") != "character":
+        return ""
+    facts = dict(ref.get("facts") or {})
+    bits = []
+    aliases = [_reference_text(alias) for alias in (ref.get("aliases") or []) if _reference_text(alias)]
+    if aliases:
+        bits.append(f"also known as {aliases[0]}")
+    for key in ("gender", "nationality", "occupation"):
+        value = _reference_text(facts.get(key))
+        if value:
+            bits.append(value if key != "occupation" else f"works as {value}")
+    age = _parse_positive_int(facts.get("age"))
+    if age is not None:
+        bits.append(f"{age} years old")
+    feet = _reference_text(facts.get("height_feet"))
+    inches = _reference_text(facts.get("height_inches"))
+    if feet and inches:
+        bits.append(f"{feet} foot {inches} tall")
+    elif feet:
+        bits.append(f"{feet} foot tall")
+    accent = _reference_text(facts.get("accent"))
+    if accent:
+        bits.append(f"speaks with a {accent} accent")
+    if not bits:
+        return ""
+    return f"Character facts for {label}: " + ", ".join(bits) + "."
+
+
+def _reference_context(ref, compact_picture_number):
+    label_name = _reference_text(ref.get("name")) or _reference_text(ref.get("id")) or "this reference"
+    label = f"<Picture {compact_picture_number}> {label_name}"
+    parts = [_reference_sentence(_reference_summary(ref.get("kind"), label_name, compact_picture_number))]
+    description = _reference_sentence(ref.get("description"))
+    wardrobe = _reference_sentence(ref.get("wardrobe"))
+    general = _reference_sentence(ref.get("general"))
+    facts = _reference_fact_sentence(ref, label)
+    if ref.get("kind") == "location":
+        if description:
+            parts.append(f"Location context for {label}: {description}")
+        if general:
+            parts.append(f"Location notes for {label}: {general}")
+    else:
+        if facts:
+            parts.append(facts)
+        if description:
+            parts.append(f"Persistent appearance for {label}: {description}")
+        if wardrobe:
+            parts.append(f"Persistent wardrobe/style for {label}: {wardrobe}")
+        if general:
+            parts.append(f"Character notes for {label}: {general}")
+    return " ".join(part for part in parts if part).strip()
+
+
+def _subject_count_guard_text(selected_refs):
+    character_labels = []
+    seen_characters = set()
+    for picture_number, (_slot, ref) in enumerate(selected_refs or (), 1):
+        if ref.get("kind") != "character":
+            continue
+        name = _reference_text(ref.get("name")) or _reference_text(ref.get("id"))
+        key = (_reference_text(ref.get("id")) or name or f"picture-{picture_number}").lower()
+        if key in seen_characters:
+            continue
+        seen_characters.add(key)
+        label = f"<Picture {picture_number}>"
+        if name:
+            label = f"{label} {name}"
+        character_labels.append(label)
+    if not character_labels:
+        return _SUBJECT_COUNT_FALLBACK_TEXT
+    if len(character_labels) == 1:
+        return (
+            f"The shot contains exactly one named character: {character_labels[0]}. "
+            "Do not create any extra people, doubles, duplicate bodies, background "
+            "performers, or extra faces."
+        )
+    return (
+        f"The shot contains exactly {len(character_labels)} named characters: "
+        + ", ".join(character_labels)
+        + ". Do not create any extra people, doubles, duplicate bodies, background "
+          "performers, or extra faces."
+    )
+
+
+def _append_prompt_section(parts, label, text):
+    clean = _reference_text(text)
+    if clean:
+        parts.append(f"{label}: {clean}")
+
+
+def curate_h3_prompt(
+    action_prompt,
+    anchor="",
+    soundscape="",
+    refs=(),
+    anatomy_guard="auto",
+    subject_count_guard="auto",
+):
+    normalized_refs = _normalize_prompt_refs(refs)
+    selected = _selected_prompt_refs(action_prompt, normalized_refs)
+    picture_map = {slot_number: index for index, (slot_number, _ref) in enumerate(selected, 1)}
+    action = _replace_reference_tags(action_prompt, picture_map)
+
+    prompt_parts = []
+    _append_prompt_section(prompt_parts, "Scene anchor", anchor)
+    if selected:
+        contexts = [_reference_context(ref, picture_number) for picture_number, (_slot, ref) in enumerate(selected, 1)]
+        _append_prompt_section(prompt_parts, "Reference context", " ".join(contexts))
+    _append_prompt_section(prompt_parts, "Action", action)
+    if anatomy_guard == "on" or (anatomy_guard == "auto" and any(ref.get("kind") == "character" for _slot, ref in selected)):
+        _append_prompt_section(prompt_parts, "Anatomy guard", _ANATOMY_GUARD_TEXT)
+    if subject_count_guard == "on" or (
+        subject_count_guard == "auto" and any(ref.get("kind") == "character" for _slot, ref in selected)
+    ):
+        _append_prompt_section(prompt_parts, "Subject count guard", _subject_count_guard_text(selected))
+    _append_prompt_section(prompt_parts, "overall_soundscape", soundscape)
+
+    prompt = "\n\n".join(prompt_parts).strip()
+    if len(prompt) > _H3_PROMPT_MAX_CHARS:
+        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)))
+    debug = (
+        f"Selected {len(selected)} reference(s): "
+        + ", ".join(
+            f"input {slot}-><Picture {index}> {_reference_text(ref.get('name')) or ref.get('id')}"
+            for index, (slot, ref) in enumerate(selected, 1)
+        )
+        if selected
+        else "Selected 0 references."
+    )
+    return (prompt, *images[:_H3_PROMPT_REF_SLOTS], len(selected), debug)
+
+
 def _parse_positive_int(value):
     text = str(value or "").strip()
     if not text:
@@ -1042,6 +1349,65 @@ def _build_character_wardrobe_text(wardrobe, character_id, name, alias):
     return text
 
 
+def _label_for_location(name, location_id):
+    return _normalize_free_text(name) or _normalize_free_text(location_id) or "the location"
+
+
+def _build_location_helper_text(
+    primary_picture_id,
+    secondary_picture_id,
+    location_id,
+    name,
+    alias,
+    description,
+    general,
+):
+    primary_picture = int(primary_picture_id)
+    secondary_picture = int(secondary_picture_id)
+    location_name = _normalize_free_text(name)
+    location_id = _normalize_free_text(location_id)
+    alias = _normalize_free_text(alias)
+    description = _ensure_sentence(description)
+    general = _ensure_sentence(general)
+    location_label = _label_for_location(location_name, location_id)
+
+    if location_name:
+        first_line = (
+            f"<Picture {primary_picture}> and <Picture {secondary_picture}> reference "
+            f"the same location called {location_name}."
+        )
+    elif location_id:
+        first_line = (
+            f"<Picture {primary_picture}> and <Picture {secondary_picture}> reference "
+            f'the same location with ID "{location_id}".'
+        )
+    else:
+        first_line = (
+            f"<Picture {primary_picture}> and <Picture {secondary_picture}> reference "
+            "the same location."
+        )
+
+    lines = [
+        first_line,
+        f"<Picture {primary_picture}> is the primary wide/environment reference for {location_label}.",
+        f"<Picture {secondary_picture}> is the secondary detail/angle reference for {location_label}.",
+    ]
+
+    facts = []
+    if alias:
+        facts.append(f"is also known as {alias}")
+    if description:
+        facts.append(description)
+
+    if facts:
+        lines.append(f"{location_label} {', '.join(facts)}")
+
+    if general:
+        lines.append(general)
+
+    return "\n".join(lines)
+
+
 class DumasImageCompareNode:
     DESCRIPTION = (
         "Dumas Image Compare shows the difference between two images directly on "
@@ -1604,6 +1970,322 @@ class DumasH3PlanExtractSceneImagesNode:
         return (passthrough_plan, *images, _connected_image_count(images))
 
 
+class DumasCharacterHelperNode:
+    DESCRIPTION = (
+        "Build a general character reference prompt and wardrobe sheet from two "
+        "IMAGE sockets plus simple identity fields, while passing both images "
+        "through unchanged."
+    )
+    RETURN_TYPES = ("IMAGE", "IMAGE", "STRING", "STRING", _REFERENCE_TYPE, _REFERENCE_TYPE)
+    RETURN_NAMES = ("image1", "image2", "reference_prompt", "wardrobe", "reference1", "reference2")
+    FUNCTION = "build_character_text"
+    CATEGORY = "Dumas/MiniMax"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image1": ("IMAGE", {"tooltip": "Primary image to pass through and describe."}),
+                "image2": ("IMAGE", {"tooltip": "Secondary image to pass through and describe."}),
+                "image1_picture_id": (
+                    ["1", "2", "3", "4", "5", "6", "7", "8", "9"],
+                    {
+                        "default": "1",
+                        "tooltip": "Picture number to mention for image1 in the reference prompt.",
+                    },
+                ),
+                "image2_picture_id": (
+                    ["1", "2", "3", "4", "5", "6", "7", "8", "9"],
+                    {
+                        "default": "2",
+                        "tooltip": "Picture number to mention for image2 in the reference prompt.",
+                    },
+                ),
+                "character_id": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional character ID string to include in the output text.",
+                    },
+                ),
+                "name": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Character name used in the main reference sentences.",
+                    },
+                ),
+                "alias": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional alternate name, codename, or nickname.",
+                    },
+                ),
+                "gender": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional gender field for non-visual character facts.",
+                    },
+                ),
+                "age": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional numeric age. Invalid values are omitted.",
+                    },
+                ),
+                "nationality": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional nationality, origin, or cultural background.",
+                    },
+                ),
+                "occupation": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional job, role, or function that is not visually obvious.",
+                    },
+                ),
+                "height_feet": (
+                    ["", "3", "4", "5", "6", "7", "8"],
+                    {
+                        "default": "",
+                        "tooltip": "Optional feet component for the character's height.",
+                    },
+                ),
+                "height_inches": (
+                    ["", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
+                    {
+                        "default": "",
+                        "tooltip": "Optional inches component for the character's height.",
+                    },
+                ),
+                "accent": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional short accent description.",
+                    },
+                ),
+                "general": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Optional non-clothing details appended as the last sentence of the reference prompt.",
+                    },
+                ),
+                "wardrobe": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Optional wardrobe/channel text. Plain clothing lists are auto-wrapped as 'Name = ...' when a name, alias, or character ID is present.",
+                    },
+                ),
+            }
+        }
+
+    def build_character_text(
+        self,
+        image1,
+        image2,
+        image1_picture_id,
+        image2_picture_id,
+        character_id,
+        name,
+        alias,
+        gender,
+        age,
+        nationality,
+        occupation,
+        height_feet,
+        height_inches,
+        accent,
+        general,
+        wardrobe,
+    ):
+        text = _build_character_helper_text(
+            image1_picture_id,
+            image2_picture_id,
+            character_id,
+            name,
+            alias,
+            gender,
+            age,
+            nationality,
+            occupation,
+            height_feet,
+            height_inches,
+            accent,
+            general,
+        )
+        wardrobe_text = _build_character_wardrobe_text(
+            wardrobe,
+            character_id,
+            name,
+            alias,
+        )
+        facts = {
+            "gender": _normalize_free_text(gender),
+            "age": str(_parse_positive_int(age) or ""),
+            "nationality": _normalize_free_text(nationality),
+            "occupation": _normalize_free_text(occupation),
+            "height_feet": str(height_feet or "").strip(),
+            "height_inches": str(height_inches or "").strip(),
+            "accent": _normalize_free_text(accent),
+        }
+        common = {
+            "kind": "character",
+            "explicit_id": character_id,
+            "name": name,
+            "aliases": alias,
+            "description": general,
+            "wardrobe": wardrobe,
+            "general": general,
+            "facts": facts,
+        }
+        reference1 = make_reference(
+            image=image1,
+            summary="Primary full-body character reference.",
+            **common,
+        )
+        reference2 = make_reference(
+            image=image2,
+            summary="Secondary facial character reference.",
+            **common,
+        )
+        return (image1, image2, text, wardrobe_text, reference1, reference2)
+
+
+class DumasLocationHelperNode:
+    DESCRIPTION = (
+        "Build a general location reference prompt from two IMAGE sockets plus "
+        "simple environment fields, while passing both images through unchanged."
+    )
+    RETURN_TYPES = ("IMAGE", "IMAGE", "STRING", _REFERENCE_TYPE, _REFERENCE_TYPE)
+    RETURN_NAMES = ("image1", "image2", "reference_prompt", "reference1", "reference2")
+    FUNCTION = "build_location_text"
+    CATEGORY = "Dumas/MiniMax"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image1": ("IMAGE", {"tooltip": "Primary location image to pass through and describe."}),
+                "image2": ("IMAGE", {"tooltip": "Secondary location image to pass through and describe."}),
+                "image1_picture_id": (
+                    ["1", "2", "3", "4", "5", "6", "7", "8", "9"],
+                    {
+                        "default": "1",
+                        "tooltip": "Picture number to mention for image1 in the reference prompt.",
+                    },
+                ),
+                "image2_picture_id": (
+                    ["1", "2", "3", "4", "5", "6", "7", "8", "9"],
+                    {
+                        "default": "2",
+                        "tooltip": "Picture number to mention for image2 in the reference prompt.",
+                    },
+                ),
+                "location_id": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional location ID string to include in the output text.",
+                    },
+                ),
+                "name": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Location name used in the main reference sentences.",
+                    },
+                ),
+                "alias": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Optional alternate name, label, or area name.",
+                    },
+                ),
+                "description": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Persistent environment, layout, and atmosphere description.",
+                    },
+                ),
+                "general": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Optional extra notes appended as the last sentence of the reference prompt.",
+                    },
+                ),
+            }
+        }
+
+    def build_location_text(
+        self,
+        image1,
+        image2,
+        image1_picture_id,
+        image2_picture_id,
+        location_id,
+        name,
+        alias,
+        description,
+        general,
+    ):
+        text = _build_location_helper_text(
+            image1_picture_id,
+            image2_picture_id,
+            location_id,
+            name,
+            alias,
+            description,
+            general,
+        )
+        common = {
+            "kind": "location",
+            "explicit_id": location_id,
+            "name": name,
+            "aliases": alias,
+            "description": description,
+            "general": general,
+            "facts": {},
+        }
+        reference1 = make_reference(
+            image=image1,
+            summary="Primary location reference.",
+            **common,
+        )
+        reference2 = make_reference(
+            image=image2,
+            summary="Secondary location reference.",
+            **common,
+        )
+        return (image1, image2, text, reference1, reference2)
+
+
 class DumasCharacterReferenceNode:
     DESCRIPTION = (
         "Build one structured REFERENCE object for a character so H3 can carry "
@@ -1836,6 +2518,166 @@ class DumasLocationReferenceNode:
         )
 
 
+class DumasSoundscapeHelperNode:
+    DESCRIPTION = (
+        "Choose a soundscape preset, auto-fill its editable description, and pass "
+        "the final soundscape text downstream for MiniMax H3 prompts."
+    )
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("soundscape",)
+    FUNCTION = "build_soundscape"
+    CATEGORY = "Dumas/MiniMax"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        default_soundscape = "quiet interior"
+        return {
+            "required": {
+                "soundscape": (
+                    _soundscape_options(),
+                    {
+                        "default": default_soundscape,
+                        "tooltip": "Preset title used to seed the editable soundscape description.",
+                    },
+                ),
+                "soundscape_description": (
+                    "STRING",
+                    {
+                        "default": _soundscape_description(default_soundscape),
+                        "multiline": True,
+                        "tooltip": (
+                            "Editable environmental audio description. Whatever text is here "
+                            "is what the node outputs to the soundscape socket."
+                        ),
+                    },
+                ),
+            }
+        }
+
+    def build_soundscape(self, soundscape, soundscape_description):
+        text = str(soundscape_description or "").strip()
+        if not text:
+            text = _soundscape_description(soundscape)
+        return (text,)
+
+
+class DumasH3PromptCuratorNode:
+    DESCRIPTION = (
+        "Curate one MiniMax H3 prompt from an action textbox, anchor text, "
+        "soundscape text, and up to nine structured references. References are "
+        "compacted so only mentioned names, aliases, or explicit <Picture N>/<refN> "
+        "tags are sent onward."
+    )
+    RETURN_TYPES = ("STRING",) + ("IMAGE",) * _H3_PROMPT_REF_SLOTS + ("INT", "STRING")
+    RETURN_NAMES = (
+        "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",
+    )
+    FUNCTION = "curate_prompt"
+    CATEGORY = "Dumas/MiniMax"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        optional = {
+            "anchor": (
+                "STRING",
+                {
+                    "forceInput": True,
+                    "tooltip": "Optional anchor/style text, usually from Dumas Anchor Style.",
+                },
+            ),
+            "soundscape": (
+                "STRING",
+                {
+                    "forceInput": True,
+                    "tooltip": "Optional soundscape text, usually from Dumas Soundscape Helper.",
+                },
+            ),
+        }
+        for slot in range(1, _H3_PROMPT_REF_SLOTS + 1):
+            optional[f"ref_{slot}"] = (
+                _REFERENCE_TYPE,
+                {
+                    "tooltip": (
+                        f"Optional structured reference {slot}. The curator only outputs "
+                        "it if the action prompt mentions its name/alias or an explicit "
+                        f"<Picture {slot}>/<ref{slot}> tag."
+                    )
+                },
+            )
+        return {
+            "required": {
+                "action_prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": (
+                            "Write the final shot action here using character/location names. "
+                            "Mention a reference by name, alias, <Picture N>, or <refN> to use it."
+                        ),
+                    },
+                ),
+                "anatomy_guard": (
+                    ["auto", "on", "off"],
+                    {
+                        "default": "on",
+                        "tooltip": (
+                            "Add the anatomy guard. Auto adds it when a character reference is used."
+                        ),
+                    },
+                ),
+                "subject_count_guard": (
+                    ["auto", "on", "off"],
+                    {
+                        "default": "auto",
+                        "tooltip": (
+                            "Add a guard against extra people, duplicate bodies, or extra faces. "
+                            "Auto adds it when a character reference is used."
+                        ),
+                    },
+                ),
+            },
+            "optional": optional,
+        }
+
+    def curate_prompt(
+        self,
+        action_prompt,
+        anatomy_guard,
+        subject_count_guard,
+        anchor="",
+        soundscape="",
+        ref_1=None,
+        ref_2=None,
+        ref_3=None,
+        ref_4=None,
+        ref_5=None,
+        ref_6=None,
+        ref_7=None,
+        ref_8=None,
+        ref_9=None,
+    ):
+        return curate_h3_prompt(
+            action_prompt,
+            anchor=anchor,
+            soundscape=soundscape,
+            refs=(ref_1, ref_2, ref_3, ref_4, ref_5, ref_6, ref_7, ref_8, ref_9),
+            anatomy_guard=anatomy_guard,
+            subject_count_guard=subject_count_guard,
+        )
+
+
 class DumasAnchorStyleNode:
     DESCRIPTION = (
         "Choose an anchor-style preset, auto-fill its full description, and pass "
@@ -1873,9 +2715,9 @@ class DumasAnchorStyleNode:
         }
 
     def build_anchor(self, anchor_style, style_description):
-        text = str(style_description or "").strip()
+        text = _clean_anchor_style_text(style_description)
         if not text:
-            text = _anchor_style_description(anchor_style)
+            text = _clean_anchor_style_text(_anchor_style_description(anchor_style))
         return (text,)
 
 
@@ -1887,9 +2729,12 @@ NODE_CLASS_MAPPINGS = {
     "DumasH3PlanExtractSceneImages": DumasH3PlanExtractSceneImagesNode,
     "DumasCharacterReference": DumasCharacterReferenceNode,
     "DumasLocationReference": DumasLocationReferenceNode,
+    "DumasSoundscapeHelper": DumasSoundscapeHelperNode,
+    "DumasH3PromptCurator": DumasH3PromptCuratorNode,
     "DumasAnchorStyle": DumasAnchorStyleNode,
-    "DumasCharacterHelper": DumasCharacterReferenceNode,
-    "DumasH3CharacterHelper": DumasCharacterReferenceNode,
+    "DumasCharacterHelper": DumasCharacterHelperNode,
+    "DumasLocationHelper": DumasLocationHelperNode,
+    "DumasH3CharacterHelper": DumasCharacterHelperNode,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1900,7 +2745,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "DumasH3PlanExtractSceneImages": "Dumas H3 Plan Extract Scene Images",
     "DumasCharacterReference": "Dumas Character Reference",
     "DumasLocationReference": "Dumas Location Reference",
+    "DumasSoundscapeHelper": "Dumas Soundscape Helper",
+    "DumasH3PromptCurator": "Dumas H3 Prompt Curator",
     "DumasAnchorStyle": "Dumas Anchor Style",
-    "DumasCharacterHelper": "Dumas Character Reference",
-    "DumasH3CharacterHelper": "Dumas Character Reference",
+    "DumasCharacterHelper": "Dumas Character Helper",
+    "DumasLocationHelper": "Dumas Location Helper",
+    "DumasH3CharacterHelper": "Dumas Character Helper",
 }

tests/test_dumas_h3_longvideos.py

@@ -1414,6 +1414,103 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
             else:
                 latent.torch.device = original_device
 
+    def test_model_upscale_oom_falls_back_to_interp(self):
+        latent = importlib.import_module("dumas_h3_latent_upscale")
+        calls = []
+
+        original_temporal = latent._upscale_video_temporal_chunks
+        original_interp = latent.upscale_video_interp
+        original_unload_now = latent._unload_upscale_model_now
+        original_cuda = latent.torch.cuda
+        original_device = getattr(latent.torch, "device", None)
+        try:
+            latent.torch.cuda = types.SimpleNamespace(
+                is_available=lambda: True,
+                empty_cache=lambda: calls.append(("empty_cache",)),
+            )
+            latent.torch.device = lambda value: value
+
+            def temporal(_video, _param, _upscaler):
+                calls.append(("temporal", latent._MODEL_HOLD_DEPTH))
+                raise RuntimeError("H3 latent upscale exhausted its GPU spatial fallbacks")
+
+            def interp(video, param):
+                calls.append(("interp", video, param.get("mode"), param.get("method")))
+                return "interp_video", 8, 16
+
+            def unload_now(name, device, precision):
+                calls.append(("unload", name, device, precision, latent._MODEL_HOLD_DEPTH))
+
+            latent._upscale_video_temporal_chunks = temporal
+            latent.upscale_video_interp = interp
+            latent._unload_upscale_model_now = unload_now
+
+            result = latent.upscale_latent_video("source", {
+                "mode": "model",
+                "model_name": "upscale.safetensors",
+                "method": "bilinear",
+                "device": "cuda",
+                "precision": "fp16",
+            })
+
+            self.assertEqual(result, ("interp_video", 8, 16))
+            self.assertEqual(calls[0], ("temporal", 1))
+            self.assertEqual(calls[1], ("unload", "upscale.safetensors", "cuda", "fp16", 1))
+            self.assertIn(("empty_cache",), calls)
+            self.assertEqual(calls[-1], ("interp", "source", "interp", "bilinear"))
+            self.assertEqual(latent._MODEL_HOLD_DEPTH, 0)
+        finally:
+            latent._upscale_video_temporal_chunks = original_temporal
+            latent.upscale_video_interp = original_interp
+            latent._unload_upscale_model_now = original_unload_now
+            latent.torch.cuda = original_cuda
+            if original_device is None:
+                delattr(latent.torch, "device")
+            else:
+                latent.torch.device = original_device
+
+    def test_model_upscale_skips_learned_model_on_8gb_cuda(self):
+        latent = importlib.import_module("dumas_h3_latent_upscale")
+        calls = []
+
+        original_temporal = latent._upscale_video_temporal_chunks
+        original_interp = latent.upscale_video_interp
+        original_cuda = latent.torch.cuda
+        try:
+            latent.torch.cuda = types.SimpleNamespace(
+                is_available=lambda: True,
+                mem_get_info=lambda: (1 * 1024 * 1024 * 1024, 8 * 1024 * 1024 * 1024),
+                empty_cache=lambda: calls.append(("empty_cache",)),
+            )
+
+            def temporal(_video, _param, _upscaler):
+                calls.append(("temporal",))
+                raise AssertionError("learned model path should be skipped on 8GB CUDA")
+
+            def interp(video, param):
+                calls.append(("interp", video, param.get("mode"), param.get("method")))
+                return "interp_video", 8, 16
+
+            latent._upscale_video_temporal_chunks = temporal
+            latent.upscale_video_interp = interp
+
+            result = latent.upscale_latent_video("source", {
+                "mode": "model",
+                "model_name": "upscale.safetensors",
+                "method": "bilinear",
+                "device": "cuda",
+                "precision": "fp16",
+            })
+
+            self.assertEqual(result, ("interp_video", 8, 16))
+            self.assertNotIn(("temporal",), calls)
+            self.assertIn(("empty_cache",), calls)
+            self.assertEqual(calls[-1], ("interp", "source", "interp", "bilinear"))
+        finally:
+            latent._upscale_video_temporal_chunks = original_temporal
+            latent.upscale_video_interp = original_interp
+            latent.torch.cuda = original_cuda
+
     def test_upscale_video_model_raises_when_gpu_cannot_shrink(self):
         latent = importlib.import_module("dumas_h3_latent_upscale")
 

tests/test_dumas_image_nodes.py

@@ -356,6 +356,277 @@ 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_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",
+            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("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])
+
+    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["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["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), 12)
+        self.assertEqual(node.RETURN_NAMES[1:10], tuple(f"ref_image_{i}" for i in range(1, 10)))
+
     def test_normalize_reference_upgrades_generic_summary_with_socket_picture_id(self):
         image = FakeTensorBatch()
 
@@ -417,6 +688,19 @@ 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."