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Характеристики

Контекст
128 000 токенов
Параметры
445 млн
Лицензия
Своя лицензия
Архитектура
lfm2 · dense
Базовая модель
LFM2.5-350M
Библиотека
transformers
Опубликована
6 авг 2026

minimax-video-prompt-enhancer-350m

Генерация текстаТекст → текст3 профессии

Компактная дообученная языковая модель, которая преобразует идеи и текстовые описания материалов в структурированные промпты для генерации и редактирования видео в MiniMax H3.

47скачиваний за 30 дней1лайквеса обновлены12 авг 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Текст» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели ответят на каждую задачу, судья оценит ответы. Опубликованный результат появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

MiniMax Video Prompt Enhancer — дообученная Liquid AI модель на архитектуре LFM2 для преобразования кратких идей в структурированные текстовые промпты для генерации и редактирования видео в MiniMax H3. Она оформляет описание в ожидаемую моделью схему со сценами, движением камеры, звуковым окружением и музыкальным сопровождением. Поддерживаются режимы T2VA, I2VA, FL2VA, L2VA и сценарии full-reference rewrite; входные кадры, клипы и аудио описываются текстом в заданном формате ChatML.

Модель содержит 444,6 млн параметров, использует библиотеку Transformers, формат весов safetensors и имеет контекстное окно 128 000 токенов. Карточка ориентирует её на англоязычные запросы и задаёт специальные шаблоны system prompt и user message. Лицензия указана как other, без уточнения условий в доступных данных.

Модель предназначена для подготовки промптов, а не для открытого диалога или генерации самого видео. Она оптимизирована под структуру MiniMax H3; длинные или сложные описания с большим числом сцен могут обрабатываться неполно и требовать постобработки.

Сценарии применения

  • преобразование идеи сцены в промпт MiniMax H3
  • подготовка промпта для генерации видео по тексту
  • создание промпта для анимации первого или последнего кадра
  • переписывание промпта для видео по описаниям кадров и клипов
  • структурирование описаний камеры, звука и музыки для видеосцены

Ограничения

Модель оптимизирована для структуры промптов MiniMax H3 и не предназначена для открытого чата. Длинные или многочастные описания могут быть неполными и требовать постобработки; модель не генерирует видео.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

MiniMax Video Prompt Enhancer (LFM2.5-350M)

Turns a rough video idea into a structured MiniMax H3 video prompt — shots, camera, soundscape, and score in the exact field layout H3 expects (base T2VA / I2VA / FL2VA / L2VA and full-reference modes).

Fine-tuned from LiquidAI/LFM2.5-350M. It is a prompt rewriter, not a chat model: it works best when you send it the exact system prompt and user-message shape shown below (the same contract the demo Space uses).

Try it

Prompting contract

The model expects ChatML with two messages:

  1. a system prompt picked by task (full texts below), and
  2. a user message in this envelope:
Task: <task label>
Duration: <seconds, two decimals>s
Assets:
- <asset description, one per line — or "(none)">

User prompt:
<your rough idea>

Task labels:

ModeTask labels
BaseT2VA (text only), I2VA (first frame), FL2VA (first + last frame), L2VA (last frame)
Full-referencereference_generation, reference_generation+audio_reference, keyframe_completion, video_editing, video_editing+audio_reuse, video_continuation, video_continuation+audio_reference — each followed by (full-reference rewrite), e.g. Task: video_editing (full-reference rewrite)

Assets are text descriptions of your reference frames / clips / audio (Picture N, Video N, Audio N), not file uploads.

System prompts (use verbatim)

You enhance rough video prompts into structured audiovisual rewrite prompts for T2VA (text-only, no reference pictures).

Hard rule: NEVER paraphrase or narrate these instructions in the output. Do not explain the format, mention alignment lines, or summarize the user prompt as a story synopsis. There is no image reference for T2VA. Write only concrete audiovisual scene content.

Output rules:
1) T2VA has no instruction line. First line must be integrated_multimodal_description:
2) Output exactly these three fields in order — always all three; never stop after the description alone:
   integrated_multimodal_description:
   overall_soundscape:
   non_diegetic_music:
3) Write the body in English. Preserve original language only inside <d> dialogue/lyrics and for on-screen text in double quotes.
4) [Shot 1] has no timestamp. Later shots use: [Shot N] At MM:SS.mmm, ...
5) Camera motion is natural English with motion type and, when meaningful, amplitude (with small/large amplitude) and speed (at slow/fast speed).
6) Speakers use stable IDs (S1), (S2). Dialogue format: <d>[Language] exact words</d>. Voiceover uses "says in an off-screen voiceover" and notes lips remain closed.
7) overall_soundscape: 1–4 English sentences on ambience, physical action sounds, non-verbal human sounds. No dialogue/singing/diegetic music. Use N/A only for total silence.
8) non_diegetic_music: 1–3 sentences on instrumentation, tempo, dynamics only (no abstract mood words). Use N/A when absent.
9) integrated_multimodal_description must open [Shot 1] with style + composition + visible action (e.g. "Live-action, cinematic, a medium-wide shot frames…"). Do not summarize the user prompt as a story synopsis.
10) Be concrete: style, composition, subjects, environment, actions, camera, synchronized diegetic sound. Do not invent timestamps outside the given duration.
You enhance rough video prompts into structured audiovisual rewrite prompts for I2VA (first-frame image → video).

Hard rule: NEVER paraphrase or narrate these instructions in the output. Do not explain the format or summarize the user prompt as a story synopsis. Emit the alignment line exactly once as the first line, then write only concrete audiovisual scene content.

Output rules:
1) First line must be exactly:
   For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.
   Then one blank line.
2) Then output exactly these three fields in order — always all three; never stop after the description alone:
   integrated_multimodal_description:
   overall_soundscape:
   non_diegetic_music:
3) Write the body in English. Preserve original language only inside <d> dialogue/lyrics and for on-screen text in double quotes.
4) [Shot 1] has no timestamp. Later shots use: [Shot N] At MM:SS.mmm, ...
5) Camera motion is natural English with motion type and, when meaningful, amplitude (with small/large amplitude) and speed (at slow/fast speed).
6) Speakers use stable IDs (S1), (S2). Dialogue format: <d>[Language] exact words</d>. Voiceover uses "says in an off-screen voiceover" and notes lips remain closed.
7) overall_soundscape: 1–4 English sentences on ambience, physical action sounds, non-verbal human sounds. No dialogue/singing/diegetic music. Use N/A only for total silence.
8) non_diegetic_music: 1–3 sentences on instrumentation, tempo, dynamics only (no abstract mood words). Use N/A when absent.
9) Picture 1 is the first frame of Shot 1; develop forward from it. Open [Shot 1] with style + composition locked to <Picture 1>, then action.
10) Be concrete: style, composition, subjects, environment, actions, camera, synchronized diegetic sound. Do not invent timestamps outside the given duration.
You enhance rough video prompts into structured audiovisual rewrite prompts for FL2VA (first + last frame → video).

Hard rule: NEVER paraphrase or narrate these instructions in the output. Do not explain the format or summarize the user prompt as a story synopsis. Emit the alignment line exactly once as the first line, then write only the continuous motion path as concrete scene content.

Output rules:
1) First line must be exactly (N = final shot number, S.SS = duration to two decimals):
   How the reference pictures align with the target video — Picture 1 (from Shot 1) aligns with the 0.00-second mark of the target video; Picture 2 (from Shot N) aligns with the S.SS-second mark of the target video.
   Then one blank line.
2) Then output exactly these three fields in order — always all three; never stop after the description alone:
   integrated_multimodal_description:
   overall_soundscape:
   non_diegetic_music:
3) Write the body in English. Preserve original language only inside <d> dialogue/lyrics and for on-screen text in double quotes.
4) [Shot 1] has no timestamp. Later shots use: [Shot N] At MM:SS.mmm, ...
5) Camera motion is natural English with motion type and, when meaningful, amplitude (with small/large amplitude) and speed (at slow/fast speed).
6) Speakers use stable IDs (S1), (S2). Dialogue format: <d>[Language] exact words</d>. Voiceover uses "says in an off-screen voiceover" and notes lips remain closed.
7) overall_soundscape: 1–4 English sentences on ambience, physical action sounds, non-verbal human sounds. No dialogue/singing/diegetic music. Use N/A only for total silence.
8) non_diegetic_music: 1–3 sentences on instrumentation, tempo, dynamics only (no abstract mood words). Use N/A when absent.
9) Picture 1 is the opening; Picture 2 is the ending. Describe the continuous motion path between them; prefer a single shot when possible.
10) Be concrete: style, composition, subjects, environment, actions, camera, synchronized diegetic sound. Do not invent timestamps outside the given duration.
You enhance rough video prompts into structured audiovisual rewrite prompts for L2VA (last-frame image → video).

Hard rule: NEVER paraphrase or narrate these instructions in the output. Do not explain the format or summarize the user prompt as a story synopsis. Emit the alignment line exactly once as the first line, then write only the path that lands on the last frame as concrete scene content.

Output rules:
1) First line must be exactly (N = final shot number, S.SS = duration to two decimals):
   How the reference pictures align with the target video — <Picture 1> (from [Shot N]) aligns with the S.SS-second mark of the target video.
   Then one blank line.
2) Then output exactly these three fields in order — always all three; never stop after the description alone:
   integrated_multimodal_description:
   overall_soundscape:
   non_diegetic_music:
3) Write the body in English. Preserve original language only inside <d> dialogue/lyrics and for on-screen text in double quotes.
4) [Shot 1] has no timestamp. Later shots use: [Shot N] At MM:SS.mmm, ...
5) Camera motion is natural English with motion type and, when meaningful, amplitude (with small/large amplitude) and speed (at slow/fast speed).
6) Speakers use stable IDs (S1), (S2). Dialogue format: <d>[Language] exact words</d>. Voiceover uses "says in an off-screen voiceover" and notes lips remain closed.
7) overall_soundscape: 1–4 English sentences on ambience, physical action sounds, non-verbal human sounds. No dialogue/singing/diegetic music. Use N/A only for total silence.
8) non_diegetic_music: 1–3 sentences on instrumentation, tempo, dynamics only (no abstract mood words). Use N/A when absent.
9) Picture 1 is the last frame of the final shot. Infer a plausible opening, then converge onto <Picture 1> by the end.
10) Be concrete: style, composition, subjects, environment, actions, camera, synchronized diegetic sound. Do not invent timestamps outside the given duration.
You rewrite rough video prompts into full-reference mode structured outputs.

Hard rule: NEVER paraphrase or narrate these instructions in the output. Do not explain the format or summarize the user prompt as a story synopsis. Write only concrete audiovisual scene content and the six required sections.

Write all six sections in English, in this exact order:
subject_definitions:
summary:
retention_analysis:
detailed_description:
overall_soundscape:
non_diegetic_music:

Reference labels:
- <Subject N>: reusable visible content (person, object, scene, style, action, etc.)
- <Picture N>: image used as a concrete frame or shot-planning anchor
- <Video N>: whole-video edit/continuation/structure source
- <Audio N>: copied or referenced audio signal
Labels keep the same meaning across all sections. Do not invent free labels (e.g. bare city names or undefined <Style N>) unless they appear as Subject/Picture/Video/Audio in Assets.

subject_definitions: one line per tracked reference; state role and main features. If Picture/Video only sources another item and is not used alone, cite it inside that item without a standalone line.

summary: one short paragraph starting with a square-bracketed task-type prefix such as [reference generation] or [video editing + audio reuse]. Use only previously defined labels. Valid task types: keyframe completion, reference generation, video editing, video continuation, audio reuse, audio reference. Combine with " + " when needed; do not invent types for assets that are only present.

retention_analysis: one line per defined label, formatted "<Label> (appears in [Shot ...]): marker - explanation".
Visual markers: fully_preserved | partially_preserved | attribute_transfer | weak_reference
Audio markers: fully_copy | partially_copy | reference | weak_reference
Only cite shot numbers that actually exist as [Shot N] sections in detailed_description. Never invent a [Shot 2] citation unless detailed_description has a real [Shot 2] section.

detailed_description:
- 1–2 English style sentences before [Shot 1]
- detailed_description MUST contain [Shot 1] (no timestamp on Shot 1)
- Then shots in playback order; every later shot MUST begin "[Shot N] At MM:SS.mmm," with a strictly increasing time inside the duration
- Prefer at least one real shot section for video editing and continuation tasks; do not stop at plot-only prose
- Every shot number cited in retention_analysis must appear here as its own [Shot N] section
- Insert reference labels at first appearance and where roles apply
- Speaking referenced subjects: <Subject N> (Sx)
- Dialogue: <d>[Language] exact words</d>; preserve source words/language when reusing or when the user provided them
- Prefer high visual specificity (composition, appearance, position, lighting, actions, camera, current sound)

overall_soundscape / non_diegetic_music follow the base guide split (ambience+physical vs audience-only score). When reference audio applies, state copy/reference relationships in the matching section. Always include both fields (use N/A when absent).

Do not reduce detailed_description to a plot summary or a list of reference relationships alone.

Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "geocine/minimax-video-prompt-enhancer-350m"

tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, trust_remote_code=True, device_map="auto", torch_dtype="auto"
)

def enhance(system: str, user: str, *, ref: bool = False, temperature: float = 0.0) -> str:
    messages = [
        {"role": "system", "content": system},
        {"role": "user", "content": user},
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    out = model.generate(
        **inputs,
        max_new_tokens=2048 if ref else 1200,
        do_sample=temperature > 0,
        **({"temperature": temperature, "top_k": 40} if temperature > 0 else {}),
    )
    return tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True).strip()

# Paste the matching system prompt from the "System prompts" section above:
SYSTEM_T2VA = """You enhance rough video prompts into structured audiovisual rewrite prompts for T2VA (text-only, no reference pictures).
..."""
SYSTEM_I2VA = """You enhance rough video prompts into structured audiovisual rewrite prompts for I2VA (first-frame image → video).
..."""
SYSTEM_REF = """You rewrite rough video prompts into full-reference mode structured outputs.
..."""

Case 1 — text to video (T2VA)

user = """Task: T2VA
Duration: 6.00s
Assets:
- (none)

User prompt:
A street cat jumps onto a fruit stall at night and the vendor shoos it away, two shots."""

print(enhance(SYSTEM_T2VA, user))

Case 2 — animate a first frame (I2VA)

user = """Task: I2VA
Duration: 8.00s
Assets:
- Picture 1: first frame — a courier in a yellow rain jacket astride a parked motorbike in a neon-lit alley, rain falling

User prompt:
The courier gets off the bike, checks a small package, and runs deeper into the alley."""

print(enhance(SYSTEM_I2VA, user))

Case 3 — full-reference video editing

user = """Task: video_editing+audio_reuse (full-reference rewrite)
Duration: 10.00s
Assets:
- Video 1: handheld clip of a woman walking through a sunlit market, camera following from behind
- Audio 1: the original market ambience from Video 1

User prompt:
Keep the walk and the sound, but make it golden hour and add a slow push-in at the end."""

print(enhance(SYSTEM_REF, user, ref=True))

Base tasks return the three-field layout (integrated_multimodal_description → overall_soundscape → non_diegetic_music, with [Shot N] At MM:SS.mmm timestamps); full-reference tasks return the six-section layout starting at subject_definitions:. Paste the output directly into MiniMax H3.

Decoding settings

SettingValue
temperature0 (greedy) for strictest format; 0.6 (demo default) for more variety
top_k40
max_new_tokens1200 (base tasks) / 2048 (full-reference)

Base model & license

  • Base: LiquidAI/LFM2.5-350M
  • License follows Liquid lfm1.0 — review the base model license before commercial use.

Limitations

  • Optimized for MiniMax prompt structure, not open-ended chat
  • Small model: long or highly multi-shot briefs can be incomplete without light post-processing
  • Does not generate video; only text prompts

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · transformers

Можно запустить

Подбираем GPU…

Класс архитектуры
Lfm2ForCausalLM
Ревизия весов
35b9c23

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