A film from a sentence

Highly experimental

Tuned and tried on a single real library (about 76,000 pictures, one family) plus a few synthetic test households. Two rounds of prompts written cold went from about 1 in 7 translated right to all of them, but those rounds were then used to fix it, so they no longer count as tests. On your library, expect some wrong translations and read the trace before trusting a film.

You type what you want to see ("the cars I drove", "at the park with kids", "the first picture of each person") and get a film of it. The part that reads the sentence is new. The part that makes the film is the same editor as every other memory (From library to film).

immich-memories generate --ask "at the park with kids" --dry-run   # the translation, the pool and its rules
immich-memories generate --ask "at the park with kids"             # then the film

What it needs:

• tier: full, meaning a configured reader (advanced.llm). Every step is built for a small local model: it was tuned on Gemma 4 E4B. See Add a reader.
• A prepared library. It reads the captions, faces and place names immich-memories prepare banked, plus the letters Immich's OCR found in your photos.
• The WordNet dictionary (11 MB), downloaded once by immich-memories models fetch and checked against a pinned digest. Nothing downloads it at run time.

Nothing leaves your network except the calls to your Immich and your reader. The flags are on the generate page. The web UI has the same thing as a box on the Memory page, with a preview before any film: The web UI.

How it works

The sentence becomes filters, the filters make a pool of pictures, and the editor chooses from the pool the same way it chooses from a month or a trip. The model reads your words. Code links them to what the library holds. No model looks at a picture to decide whether it belongs: the pool is built from captions and facts the library already has.

Swipe sideways, or focus the diagram and use the arrow keys, to see more.

1. Reading. The model splits the sentence into who, when, where and what. It can only pick phrases your sentence contains, and it does it three times in three orders. A word counts where two answers agree. A part you said nothing about sets no filter: say nothing about where and the film is from anywhere.
2. Who. Grammar and your people file, no model:
   • "I", "me" and "my" are you, used for your age and your homes, never as a face in the photo (you are usually the one holding the phone: "the cars I drove" has no face in it).
   • "We" and "our" are you and your partner.
   • A name or a role from your people file ("my son", a first name) means that face, as Immich recognised it.
   • A plural word for people ("friends", "kids") asks for company: a caption naming people, or naming children when the word means young people.
3. When. Years you write are pattern work. An age ("in our 20s") is read by the model as numbers, and the calendar is arithmetic from the birth date in your people file.
4. Where. The model votes one place per phrase among the options code builds: anywhere, a home, near home, away on a trip. A phrase that only repeats the subject ("at the park") stays the subject, not a place. One particular place at home has to be proven by GPS.
5. What. The head noun of each phrase ("bread" in "bread making", "park" in "at the park with kids"). When that head only frames an occasion, the modifier carries the subject instead: "rainy weekends" offers "rainy" and WordNet's "rain"; "snowy days" offers "snowy" and "snow". Generic heads are WordNet time periods plus day, walk, trip, moment and time. Their plural forms follow the same rule. A picture phrase such as "rainy pictures" also keeps its modifier; measured qualities such as "blurry pictures" still use the facts filter. Concrete heads such as "cat" in "black cat" keep their meaning. The trace names the modifier and the head it replaced. WordNet adds the other everyday names and the thing's own kinds and parts, and the model votes which one is the main subject. A thing has to be what the caption is about ("a loaf of bread on a rack" yes, "a toddler holding toast" no). A place, a scene or something people do counts anywhere in the caption, since captions put the people first ("kids playing in a park"). A word printed in the photos (a club's name on the jerseys, read by Immich's OCR) vouches for the whole episode it appears in. Only what follows "not", "no", "without" or "except" is left out.
6. What the library measures. Some words link straight to fields the library holds, no caption involved:
   • a country, region or city Immich names on your photos ("I love <a country>");
   • the kind of picture ("screenshots", "documents", "maps", "videos");
   • "blurry": below the editor's own sharpness line (the softest 10 % of your library);
   • how often a face appears ("people with more than 35 pictures");
   • "farthest from home": the trip whose middle photo is farthest from where you lived then;
   • "first" or "last" picture of each person. The first is when they show up for good, never before their birth date, so a scanned childhood photo or a wrong face match stays out.
7. The film. The pool goes whole to the editor, filmed like an album whose written subject is your sentence: stories, standing, the family-viewing check, duplicates and length all apply. Forwarded pictures are kept (a club's photos tend to arrive through a group chat). One occasion of one day ("the birth of my son") goes to the special-day film instead.

Reading the trace

Every run prints how it read you before anything else runs, one line per decision. A real dry run:

"At the park with kids"
READING  the model split your words 3 times; a word counts where 2 answers agree -> who: kids; where: the park; what: at the park | kids
WHO      your words "kids" -> children must be in the photos, no one in particular (a plural word for children; a caption naming children shows it)
WHEN     nothing said -> any time (no time words, years or people to date)
WHERE    your words "the park" -> anywhere (nothing beyond the subject's own nouns)
WHAT     your words "at the park | kids" -> subject words park (...)
POOL     library 76367 -> kind of picture 74466 -> subject 1197 -> company 457
VERDICT  possible: 457 pictures in the pool; ...
FILM     the engine films the pool as an album whose written subject is your words: 457 pictures (the pool is the film's whole reach)

POOL is the funnel: how many pictures were left after each filter. When a film is wrong, this line usually says where. A subject count of 3 means the captioner never writes your word. A count that barely moves means the filter did nothing.

VERDICT is one of three:

Verdict	Meaning	What happens
possible	12 pictures or more in the pool	the film is made
thin	fewer than 12	a short film is made, and the run says why
not possible	the pool is empty	no film; the trace names the filter that emptied it

A computed selection ("the first picture of each person") is never called thin, however short. The run does not pad a request it cannot show with something else.

Which rules would drop pictures

The pool is what your words mean. The editor then refuses some pool pictures on rules that protect every film: a screenshot, a second file of a picture it already has, a picture held for review. A dry run asks those rules about the pool before anything renders, and prints them as RULES right after POOL:

RULES    451 of 457 pictures pass the rules checked before cutting
         another file of the same picture: 3 (the full-size file of the same picture plays instead) id-6c1f0e2a9b4d7e31, id-...
         screens and documents: 1 (a screenshot, a screen or a document is never a source) id-...
         held for review: 2 (a detector or you marked it never_auto; only your clearance lifts it) id-...
         decided while cutting: who sees it (...); look-alikes (...); capture spacing (...)
         not applied to a film you asked for: provenance (...); standing (...)

These are the run's own checks, called on the same banked facts, so the preview and the film agree. It reads the pool's pictures from Immich first, like the film run does: one search call per 1,000 pictures between the pool's first and last date, never one call per picture. Each picture counts once, under the first rule that drops it. The ids are hashed the way report hashes them: the terminal never prints a real one.

What it checks, in the run's order:

Rule	Drops
hidden in Immich, a film this app made, long video	archived, locked or deleted pictures, uploaded memories, recordings over the source cap
another file of the same picture	the smaller copy (a shared album's downscale, a chat's re-send)
screens and documents	screenshots, screens, documents, by the document head, the screen head, the caption or a phone's screen size
held for review	a never_auto flag on the picture or its Live Photo clip, until you clear it
close-up of a face part, medical care, sheet of identical portraits	pictures that need an explanation
video frames miss the subject	a video whose sampled frames mostly miss the subject, unless starred

Three rules depend on the shots around a picture or on a model reading, so they are only known while cutting and are named without a count: who sees it (the reader checks each chosen shot's caption for your sharing level; a detector hold keeps a picture out of shareable films), look-alikes (a shot repeating one already in the cut) and capture spacing (two shots of one moment within five minutes). Two rules do not apply to a film you asked for, and the block says so: provenance (forwarded and saved pictures stay) and standing (a pool picture stands on your subject whatever its score).

A picture prepare has not read yet has nothing for the rules to read, so it is not counted as passing: the first line then reads "17 of 18 prepared pictures pass ...; 12 not prepared yet". The run reads those pictures before applying the rules. There is no switch to turn a rule off. Held pictures come back one at a time, when you clear them on the pool page.

With --dry-run the command stops after the trace and the rule preview. Translating one sentence took 7 to 46 seconds on a Mac with a local Gemma E4B; the model's answers are banked in the store, so asking the same question twice does not ask the model twice.

Where it is good, where it is less good

From the prompts tried so far (redacted: the real ones are private):

Asked for	Result
things the captions name: the cars I drove, bread making, a pet over the years	good
scenes with people in them: at the park with kids, races, partying in our 20s, closed eyes	good
what the library measures: I love <a country>, blurry pictures, first or last picture of each person	good
a club's rides, with the name on the jerseys	good, thin: the OCR finds the club
one known occasion: the birth of a child	good, filmed as the special day
a concept the captioner rarely writes: brunches, breastfeeding	thin
an occasion nothing marks: an unconventional wedding	not possible
sport app screenshots	not possible: the screenshots are found, their captions rarely say "sport"
one picture per person	a few people are cut to fit the film's length

Captions name things well and people doing things well. Concepts the captioner does not write come out thin or not possible.

Limits

• One library. Every rule was found on one family's pictures and a few synthetic households. Your words, habits and cameras differ, so will the results.
• Captions are the basis. "Brunch" was in 3 captions out of 76,000. If the captioner never writes the word, no rule finds it.
• Occasions without a date. A wedding the captions never call a wedding can only be found by its date, and there is no place to tell it that date yet (birth dates in the people file work).
• Two senses. "Races" found running races AND motor-racing track days. Say which one you mean.
• WordNet is from 2006. It has no "screenshot" or "app", and some first senses are odd ("dashboard" is a carriage's mud panel).
• The model still judges some parts: the main subject, the place vote, the kind of film. Three votes soften a bad answer; they do not remove it.
• Company comes from captions only. "With kids" needs a caption naming children; the ages of the faces are not used yet.
• The editor still decides the film. It gets the pool and your sentence, not your special cases: a list of one picture per person is cut to the film's length, and blurry pictures still lose to sharp ones.
• English first. Other languages go through the model and may lose meaning.

When a result is bad

Mark it on the run, then paste the report into an issue:

immich-memories report <run-id> --wrong <asset-id> --wrong <asset-id> --missing "the red bike"

Each photo marked wrong gets the filter that let it into the pool and whether the editor picked it. The missing words are checked against the run: did the reading keep them, were they offered or picked as the subject, and does any caption in the pool say them.

In the web UI, open the film's run and use Copy report, or Download report for the ZIP with the whole log. Same report, same redaction. A film made from the web UI is cut first, then rendered as a run of its own: the report of either run carries your sentence.

The report carries the sentence, the trace, the translation as data (the same JSON --ask-trace writes), the funnel, the rule preview, the editor's picks and the run's logs. Before anything is copied:

• IDs become hashes that only match inside that one report;
• the names of people the sentence linked become their roles ("the owner's son"), and a birth date the trace dated from becomes [private];
• place names become "area A", and words read by OCR become "text-1", in your sentence too;
• captions stay out unless you pass --include-flagged-captions, and then only those of the photos you marked. Pictures never.

The rest of the report and its privacy rules: report and Privacy.