unsloth/studio/backend/core/inference/video_gallery.py
Daniel Han 186f381bc6
Studio: diffusion UX polish and stronger auto policies (images + video) (#6885)
* Auto policies: deferred dense compile, video compile default, step cache and precision auto

Image dense loads with speed unset no longer sit at plain off: the load stays
bit-identical eager, and the 3rd generation in a session engages the default
compile profile plus the cuDNN attention upgrade mid-session (a one-off image
never pays the warmup, repeated use amortises it). Video dense loads resolve
straight to the default profile since a clip denoise amortises the compile
within a single run, and never to max.

Video also gains the image backend's tri-state auto policies: unset step cache
now decides from the default schedule and re-checks the actual step count per
generation, and unset precision (transformer_quant) hands the decision to the
hardware ladder instead of staying off. Memory badge reason now says plainly
that everything fits when no offload is planned.

* Rename Dtype to Precision, add the video Precision control, step cache Auto option

The images Advanced panel's Dtype row is now Precision (same control, clearer
name), and the video Advanced panel gains the matching Precision select wired
to the load route's existing transformer_quant field, gated to full-pipeline
loads the way the image control gates to GGUF. Step cache selects on both
pages gain an explicit Auto option as the default (the previous Off default
silently behaved as auto and never let anyone pin off), and the Speed and
Attention tooltips now state the deferred dense compile and the SageAttention
black-frame caveat.

* Model catalog: canonical diffusion model groups with device-aware routing

One canonical name per image/video model, its published artifacts (GGUF, FP8,
bnb-4bit, official BF16) as data, and pure routing helpers: suffix-stripped
canonical keys (owner-preserving; cross-owner merges only via explicit
aliases), group/artifact lookups, a flat back-compat options shim, load-spec
resolution replacing the pages' lookup tables, search matching over old ids
and format tokens, the GGUF fit ladder extracted from the variant expander,
and pickDefaultArtifact/pickDefaultQuant deciding what a bare group click
loads (downloaded first, then the best quality that fits 70 percent of VRAM,
GGUF as the safe fallback). Checked by npm run catalog:check, following the
i18n:check pattern.

* Picker: one canonical row per diffusion model with a format second level

The Images and Video pickers now render the curated catalog as one row per
model in Recommended: clicking loads the best artifact for the device (the
routed GGUF quant, a prequant FP8/bnb-4bit that fits, or the official BF16),
and a chevron opens the per-format list, with the GGUF row nesting the usual
quant expander. Live HF listing rows that belong to a group are deduplicated,
search collapses member repos into their group (old ids and format tokens
still match), and the On Device sections group cached member repos under the
same canonical name with the per-repo rows inside. Curated groups render from
the catalog rather than the HF listing, which finally surfaces LTX-2.3 in the
video Recommended list (its hub pipeline_tag is image-to-video, so the
text-to-video listing always missed it) and exposes the HunyuanVideo 720p
repack next to 480p.

Backend: /cached-models now tags trusted video-family repos text-to-video
instead of blanket text-to-image, and the pickers admit catalog-known
non-unsloth repos On Device, so cached Lightricks/Wan/Hunyuan pipelines
finally appear in the Video picker. Chat pickers pass no catalog and are
unchanged.

* Download formats, tab icons, plain-language train tips, 3-loop autoplay

The image Download button becomes a menu: PNG saves the original bytes with
the embedded recipe, JPEG and WebP re-encode client-side from the fetched
blob (JPEG flattened onto white). The video Download button gains MP4
(original, keeps audio), WebM and GIF; the latter two transcode server-side
from the stored MP4 via PyAV (VP9 realtime profile for WebM, ~12 fps adaptive
palette for GIF) behind a new gallery export route that 501s with a readable
message when a codec is missing.

Generated clips no longer loop forever: the player replays a clip three times
per selection, then pauses with controls up; a new generation or a refresh
gets its own three plays. The Create/Train tabs reuse the sidebar's New Chat
and Train icons (TestTubeOutlineIcon moved to a shared lib module), and every
Train tab helper text is now one plain sentence.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Keep the Create/Train tab icon and label on one line

TabsTrigger renders its children inside a plain inline span and the
Tailwind preflight gives svg display:block, so the HugeiconsIcon forced
the label onto a second line. Wrap icon plus label in their own
inline flex row inside each trigger.

* Strip -int8 and -nvfp4 prequant suffixes in the model catalog key

canonicalKeyFor already lowercases before matching, so -GGUF/-FP8 in any
case were covered; -int8 and -nvfp4 were not in the suffix table, so
such repos rendered as standalone rows in Recommended and On Device
instead of standardizing into their base-name group and routing through
pickDefaultArtifact. Added both suffixes plus case-insensitivity and
routing assertions to the catalog check.

* Standardize non-catalog picker rows to their base model name

The curated catalog already collapses its own groups, but hub listing
rows and cached repos outside the catalog (ERNIE-Image, FLUX.2-klein,
Qwen-Image-Edit-2509, FLUX.2-dev) still rendered raw ids with -GGUF /
-FP8 style suffixes in Recommended and On Device.

- model-catalog.ts: new stripArtifactSuffixesForDisplay, a
  case-preserving twin of canonicalKeyFor's stripping that keeps the
  owner prefix and original casing for display.
- pickers.tsx: recommended hub rows and the downloaded GGUF/model rows
  pass their labels through it when a catalog is present, so only the
  diffusion pickers change; chat rows keep raw ids. Click targets keep
  the full repo id, and the format badge still shows the artifact kind.
- Catalog check covers the new helper across GGUF/FP8/int8/nvfp4 in
  both cases plus no-op and suffix-only names.

* Offer official BF16/FP8 artifacts per model group and fix gallery label clipping

Model picker changes so groups are not limited to unsloth quant repos:

- model-catalog.ts: each image group that has an official vendor pipeline
  now carries its BF16 (official) artifact as the top (highest quality)
  entry - Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image, Qwen/Qwen-Image-2512,
  Qwen/Qwen-Image-Edit-2511, black-forest-labs/FLUX.1-dev, FLUX.1-schnell
  and FLUX.1-Kontext-dev. The LTX-2.3 video group now lists Lightricks'
  own bf16 and fp8 distilled single-file checkpoints alongside the GGUF.
  Resident sizes are set from the actual weight totals (FLUX ships a
  duplicate single-file that from_pretrained ignores, so FLUX bf16 is ~32
  GB not 54). The repos that used to be aliases are now real artifacts.
- The router already prefers the highest-quality artifact that fits the
  0.7 x GPU budget, so a datacenter GPU now defaults to official BF16
  while consumer GPUs still route to the fitting quant or GGUF. That is
  why bnb-4bit was the Z-Image-Turbo default before: it was the only
  non-GGUF artifact and it was already downloaded.
- diffusion.py: allowlist the four official image repos not previously
  trusted (qwen/qwen-image-2512, qwen/qwen-image-edit-2511,
  black-forest-labs/flux.1-schnell, flux.1-kontext-dev). All verified as
  safetensors-only diffusers model_index pipelines. The LTX-2.3
  checkpoints are already on the video trust list.
- catalog check: BF16-wins-on-datacenter, quant-wins-on-consumer, and the
  single-file load specs for the LTX-2.3 checkpoints.

Also fixes the video gallery thumbnail caption: the leading duration was
clipped by the rounded corner and selection border, so the strip now has
enough left/bottom padding to clear the curve.

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* video gallery: guard export transcode against a stream-less clip

_transcode_webm and _transcode_gif indexed src.streams.video[0] before
checking the stream list, so a container with no video stream raised a bare
IndexError that the broad handlers then re-labeled as a missing libvpx or
decoder. Raise an explicit RuntimeError naming the real cause in both the
WebM and GIF paths.

* Studio: honor explicit attention/format choices, fix distilled-LTX defaults and On Device catalog routing

* Remove stray planning notes accidentally committed to the branch

* video: add transformerQuant to the load callback deps

handleLoad reads transformerQuant but omitted it from the useCallback dep array,
so after the user changes only Precision and then selects a model or clicks
Reapply, the memoized callback keeps the stale closure and loads the previous
precision. The image page's equivalent callback already lists it.

* model picker: honor the format filter when routing catalog clicks; add catalog rows to the roving list

- routedArtifactFor now scopes a group's artifacts to the active format filter
  (the same matchesFormatFilter predicate the visibility check uses) before
  pickDefaultArtifact, so a group shown only because it owns a GGUF no longer
  routes a click to a large non-GGUF download. Covers both the Recommended and
  On Device grouped paths.
- hubOptionKeys now includes the catalog-group, search-catalog-group, and grouped
  On Device row keys in exact render order, so arrow/Home/End roving reaches the
  catalog rows instead of giving them a duplicate missing id and skipping them.

* model picker: don't treat a partial base cache as downloaded

A partially-cached base repo (a cancelled download that left only some weights)
was counted as downloaded, so an On Device click routed to a fresh multi-GB
re-download instead of the complete GGUF. The picker's endpoint (/api/models/
cached-models) did not carry a partial flag at all, so a frontend-only guard
could not see it. Surface partial from that endpoint by reusing the hub inventory
scan's snapshot-partial detector, plumb it through CachedModelRepo (backend +
frontend types), and skip partial base repos when building the downloaded set.

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* model picker + diffusion: drop partial/unloadable cached rows, skip defer-compile before a LoRA gen

- On Device (cached non-GGUF) rows filtered partial-download snapshots back in: sortedCachedModels
  gated on passesTaskGate + a groupForRepoId key match but, unlike downloadedSet, never checked
  c.partial, so an incomplete unsloth snapshot showed as a loadable On Device row (click errors or
  silently re-fetches multi-GB). It also admitted repos that only match the catalog by group KEY
  (a base / uncurated-quant sibling like Qwen/Qwen-Image-2512) which have no loadable artifact and
  dead-end at the trust gate. Add !c.partial and gate on artifactForRepoId (what loadSpecFor
  resolves) instead of groupForRepoId, so a cached row shows only when the backend can load it.

- Deferred speed-auto engaged the compile profile on the 3rd generation BEFORE _apply_loras. A
  compiled transformer rejects LoRA (supports_lora is False) and _apply_loras raises before its
  unchanged-selection no-op, so once compile engaged every LoRA generation on that load failed
  permanently. Skip the deferral when a LoRA is requested (compile and LoRA are mutually exclusive)
  and let it engage on a later LoRA-free generation.

* Scope the cached-model partial probe to the listed snapshot dir

list_cached_models builds each row from the largest/complete copy across HF cache
roots, but _cached_repo_partial probed is_snapshot_partial with no repo_cache_dir,
so the scan spanned every root: a stale .incomplete copy in one root would flag a
complete copy in another as partial and hide the usable model from the picker (the
click then routes to a re-download). Forward the winning snapshot's repo_path so all
three partial signals are scoped to that copy, matching the sibling inventory paths
(models/dataset cache_inventory, local_inventory).

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* Do not auto-route to gated repos, prefer complete cached copies, defer compile past attached LoRA, scope group expand keys

Four fixes:
- pickDefaultArtifact's not-downloaded ladder returned the gated BF16 FLUX.1-dev / Kontext-dev
  before the open GGUF on a large GPU, so a bare group click routed to a repo the user may lack
  license/token access to. Add a gated flag and skip gated artifacts in the not-downloaded ladder
  (an already-downloaded gated artifact is still returned).
- list_cached_models picked the largest duplicate cache copy and computed partial only on it, so a
  larger partial copy shadowed a smaller complete one; since partial rows are dropped from the
  picker the usable model vanished. Prefer completeness, then size.
- the deferred-speed compile engaged on a no-LoRA generation while an adapter from a prior
  generation was still attached, baking it into the compiled graph (the later unload is swallowed
  on a compiled pipe); also defer while adapters remain attached.
- routeGroupClick's GGUF fallback toggled the context-free canonicalId while the chevron toggles
  the context-scoped expandKey, leaving the format list un-collapsible in one context, dead in the
  other, and risking cross-context expansion; thread expandKey through.

* Guard video pipeline repos from deletion, drop the always-failing LTX FP8 artifact, prefer 720p Hunyuan

Three round-6 fixes:
- cached non-GGUF video repos now surface in the Video On-Device picker with the normal delete
  action, but /delete-cached only guarded chat + the Images engine, so a loaded/loading Wan / LTX /
  Hunyuan pipeline could have its HF snapshot removed from under it. Add a VideoBackend
  loading_repo_ids accessor and a video loaded/loading guard mirroring the Images one.
- the catalog advertised Lightricks/LTX-2.3-fp8 as loadable, but the LTX-2.3 loader refuses the
  official scaled-FP8 single file (.weight_scale/.input_scale) and points to GGUF/BF16, so a pick
  routed to a ~76 GB download that always fails on load. Remove the FP8 artifact.
- pickDefaultArtifact only sorts by format, so the HunyuanVideo group's 480p (listed first) beat
  the 720p even on GPUs where 720p fits the budget. List 720p first so the fit loop prefers it and
  falls back to 480p only on smaller cards.

* diffusion: add compute int8/fp8_dynamic text-encoder quant, wire into video

Add two torchao compute text-encoder quant modes to the diffusion precision
engine, alongside the existing layerwise fp8 and weight-only nvfp4:

- int8: per-token activation + per-channel weight (torch._int_mm), with per-layer
  keep-bf16 selection. int8 degrades on large encoders unless the most
  quant-sensitive decoder blocks stay bf16, so it engages only for families with
  a measured keep-bf16 schedule (qwen-image / qwen-image-edit keep first+last 6,
  flux.2-dev keeps first 3); a family without one falls back to fp8.
- fp8_dynamic: per-row fp8 compute (torch._scaled_mm), keeping the matmul in fp8
  on the tensor cores instead of upcasting each forward like the layerwise fp8.

The selective int8 caster reuses the committed transformer-quant factory
(_make_quant_config / make_filter_fn / exclude_tokens_for_scheme) plus a small
structural first/last-N block skip, so it depends only on committed APIs.

Wire text-encoder quant into the video backend, which previously loaded the
companion encoder (Gemma3 / UMT5 / Qwen2.5-VL) dense bf16 while quantising only
the DiT. text_encoder_quant is plumbed through the load request, validation, the
load chain, the resolved record, and status, mirroring the image backend; it
applies for every load kind (the encoder is dense regardless of how the DiT was
sourced). Widen the image and video load request Literals and add the video
status field.

Tests: int8 family-schedule routing and fp8 fallback, fp8_dynamic routing,
hardware gates (int8 sm_80+, fp8_dynamic sm_89+), the structural block selection,
the real int8 filter closure (keeps the first blocks plus the vision tower /
lm_head / T5 wo dense), and the video route threading and 422 validation.

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* text-encoder quant: skip the torchao modes under offload (both backends)

quantize_text_encoders applied int8-with-schedule / fp8_dynamic / nvfp4 (all torchao) to the
text encoder regardless of the offload policy. An offload placement then moves the quantized encoder
with Module.to(), which torchao tensor subclasses reject (aten._has_compatible_shallow_copy_type is
unimplemented) -- a hard crash, the same one the DiT path already skips torchao quant under offload to
avoid. Add offload_active to quantize_text_encoders and skip the torchao modes when set; layerwise fp8
is not torchao and still streams under offload. Both the video and image loaders pass
offload_active = (offload policy != none).

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* diffusion: skip non-bf16 linears for scaled_mm quant schemes

The fp8 / mxfp8 / nvfp4 schemes run on torch._scaled_mm and the fp4 / mx GEMMs,
which assert a bfloat16 input weight. On a mixed-precision DiT that keeps some
linears in fp32 for numerical stability (the Wan and Hunyuan video transformers
do this), quantize_ hits the first fp32 linear, raises, and the best-effort
wrapper swallows it to None, so the whole transformer stays dense with no error
and no speedup or memory saving.

Add a require_bf16 gate to make_filter_fn and pass it for the scaled_mm schemes
in quantize_transformer (and the fp8_dynamic text-encoder caster). The gate
skips non-bf16 linears so the scheme engages on the bf16 ones. int8 uses
torch._int_mm, which quantizes fp32/fp16 weights fine, so it leaves the gate off
and keeps its current coverage.

Verified on Wan2.2-TI2V-5B: fp8 and mxfp8 now quantize 303 linears via the
committed quantize_transformer path where they previously engaged 0.

* prequant builder: mirror the scaled-mm bf16 gate offline

The runtime DiT quantizer skips non-bf16 Linears for the scaled_mm schemes (fp8,
nvfp4, mxfp8) so the scheme engages on a mixed-precision transformer instead of
aborting on the first fp32 Linear. The offline prequant builder reused make_filter_fn
without that gate, so building an fp8/nvfp4/mxfp8 checkpoint for a mixed-precision DiT
(Wan, Hunyuan keep _keep_in_fp32_modules in fp32 even under torch_dtype=bf16) would hit
the same fp32 Linear and abort, breaking the builder's stated offline == runtime,
LPIPS-0 invariant. Thread require_bf16 = scheme in _SCALED_MM_SCHEMES through the builder,
record it in the checkpoint metadata, and verify it on load (mirrors the existing
exclude_name_tokens guard) so a future _SCALED_MM_SCHEMES change cannot silently load a
checkpoint built under the old filter.

* Keep nvfp4 fp32 linears quantised (bf16 gate is fp8/mxfp8 only)

Verified on torchao 0.17 / B200: fp8 per-row asserts 'PerRow quantization only
works for bfloat16 precision input weight' and mxfp8 asserts 'Only supporting bf16
out dtype', but NVFP4's high-precision conversion quantises an fp32 weight fine
(forward included). So the bf16 skip-gate must be fp8/mxfp8 only, not all scaled_mm
schemes -- otherwise nvfp4 leaves large fp32 projections dense, losing the intended
memory/speed gain. Rename _SCALED_MM_SCHEMES -> _REQUIRE_BF16_SCHEMES = (fp8, mxfp8)
and thread it through the runtime filter, the offline builder, and the loader
require_bf16 verification (offline == runtime preserved).

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-07-06 17:48:39 -07:00

256 lines
9.3 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Disk-backed persistence for generated videos.
Each video is an MP4 under ``studio_root()/videos``. Unlike a PNG, an MP4 has no
portable text-chunk we can embed the generation recipe into, so every video is
stored as a pair: ``{id}.mp4`` holds the encoded bytes and ``{id}.json`` holds
the full recipe as a UTF-8 JSON sidecar (the source of truth the gallery reads
back). The pair travels together on disk; a lone file is not a valid record.
The gallery is intentionally dumb storage: the route owns the metadata schema
and passes a plain dict; this module only writes/reads/sorts files.
"""
from __future__ import annotations
import json
import os
import re
import uuid
from pathlib import Path
from typing import Any, Optional
from loggers import get_logger
from utils.paths import ensure_dir, studio_root
logger = get_logger(__name__)
# Video ids are file stems; restrict to filename-safe chars so a crafted id
# can't escape the gallery directory.
_ID_RE = re.compile(r"^[A-Za-z0-9_-]{1,128}$")
def gallery_dir() -> Path:
return ensure_dir(studio_root() / "videos")
def save(mp4_bytes: bytes, meta: dict[str, Any]) -> dict[str, Any]:
"""Persist encoded MP4 bytes plus their recipe sidecar; return the record."""
video_id = uuid.uuid4().hex
directory = gallery_dir()
(directory / f"{video_id}.mp4").write_bytes(mp4_bytes)
# Write the sidecar via a tmp file + os.replace so a reader never sees a
# half-written recipe (an incomplete json would make the pair unlistable).
sidecar = directory / f"{video_id}.json"
tmp = directory / f"{video_id}.json.tmp"
tmp.write_text(json.dumps(meta), encoding = "utf-8")
os.replace(tmp, sidecar)
return _record(video_id, meta)
def _record(video_id: str, meta: dict[str, Any]) -> dict[str, Any]:
return {
**meta,
"id": video_id,
"url": f"/api/inference/video/gallery/{video_id}/file",
}
def video_path(video_id: str) -> Optional[Path]:
"""Resolve an id to its on-disk MP4, or None if missing / unsafe."""
if not _ID_RE.match(video_id):
return None
path = gallery_dir() / f"{video_id}.mp4"
# Defence in depth: confirm the resolved path is still inside the gallery.
try:
path.resolve().relative_to(gallery_dir().resolve())
except ValueError:
return None
return path if path.is_file() else None
def transcode(video_id: str, fmt: str) -> Optional[bytes]:
"""Re-encode a stored MP4 for the Download menu: "webm" (VP9, web embeds)
or "gif" (shareable preview). Returns the encoded bytes, or None when the
id doesn't resolve. Raises RuntimeError when the codec/deps are missing so
the route can 501 with a clear message. MP4 downloads never come through
here -- the /file route streams the original bytes."""
path = video_path(video_id)
if path is None:
return None
normalized = fmt.strip().lower()
if normalized == "webm":
return _transcode_webm(path)
if normalized == "gif":
return _transcode_gif(path)
raise ValueError(f"Unsupported export format '{fmt}'. Use webm or gif.")
def _transcode_webm(path: Path) -> bytes:
import io
try:
import av
except Exception as exc: # noqa: BLE001 -- no PyAV -> no transcode
raise RuntimeError("WebM export needs the 'av' package (PyAV).") from exc
buf = io.BytesIO()
try:
with av.open(str(path)) as src, av.open(buf, "w", format = "webm") as dst:
if not src.streams.video:
raise RuntimeError("WebM export failed: the clip has no video stream.")
in_v = src.streams.video[0]
rate = in_v.average_rate or 24
out_v = dst.add_stream("libvpx-vp9", rate = rate)
out_v.width = in_v.codec_context.width
out_v.height = in_v.codec_context.height
out_v.pix_fmt = "yuv420p"
# Realtime-oriented settings: VP9's default "good" profile encodes a
# few frames per second; cpu-used 8 + row-mt is many times faster at
# a small quality cost, right for a download button.
out_v.options = {"deadline": "realtime", "cpu-used": "8", "row-mt": "1"}
for frame in src.decode(in_v):
for packet in out_v.encode(frame.reformat(format = "yuv420p")):
dst.mux(packet)
for packet in out_v.encode():
dst.mux(packet)
except RuntimeError:
raise
except Exception as exc: # noqa: BLE001 -- surface as "encoder unavailable"
raise RuntimeError(f"WebM export failed (libvpx-vp9 unavailable?): {exc}") from exc
# Audio is intentionally dropped: Opus muxing needs a 48 kHz resample chain
# and most exported clips are silent; the original MP4 keeps the audio.
return buf.getvalue()
def _transcode_gif(path: Path) -> bytes:
import io
try:
import av
from PIL import Image
except Exception as exc: # noqa: BLE001 -- missing deps -> no transcode
raise RuntimeError("GIF export needs the 'av' and 'Pillow' packages.") from exc
frames: list[Any] = []
try:
with av.open(str(path)) as src:
if not src.streams.video:
raise RuntimeError("GIF export failed: the clip has no video stream.")
in_v = src.streams.video[0]
rate = float(in_v.average_rate or 24)
# Full-rate GIFs are enormous and stutter in chat apps; ~12 fps is
# the sweet spot, achieved by skipping source frames.
step = max(1, round(rate / 12))
for i, frame in enumerate(src.decode(in_v)):
if i % step:
continue
frames.append(frame.to_image().convert("P", palette = Image.Palette.ADAPTIVE))
except RuntimeError:
raise
except Exception as exc: # noqa: BLE001 -- surface as "decoder unavailable"
raise RuntimeError(f"GIF export failed to decode the clip: {exc}") from exc
if not frames:
raise RuntimeError("GIF export decoded no frames.")
duration_ms = max(20, int(1000 * step / rate))
buf = io.BytesIO()
frames[0].save(
buf,
format = "GIF",
save_all = True,
append_images = frames[1:],
duration = duration_ms,
loop = 0,
)
return buf.getvalue()
def _sidecar_path(video_id: str) -> Path:
return gallery_dir() / f"{video_id}.json"
def _read_meta(sidecar: Path) -> Optional[dict[str, Any]]:
try:
raw = sidecar.read_text(encoding = "utf-8")
except OSError:
return None
try:
meta = json.loads(raw)
except (ValueError, TypeError):
return None
return meta if isinstance(meta, dict) else None
def _mtime(path: Path) -> float:
try:
return path.stat().st_mtime
except OSError:
return 0.0
def list_videos(limit: Optional[int] = None, offset: int = 0) -> list[dict[str, Any]]:
"""A newest-first window of videos for infinite scroll.
Ordered by MP4 mtime (a cheap stat, ~= generation order) so a months-old
gallery isn't opened in full just to sort it; only the window's sidecars are
read. limit=None returns everything from ``offset`` on. A file without its
pair (an MP4 with no readable json sidecar, or a sidecar with no MP4) is not
a valid record and is skipped."""
try:
paths = list(gallery_dir().glob("*.mp4"))
except OSError:
return []
paths.sort(key = _mtime, reverse = True)
# Page over READABLE records, not raw files: filtering an orphan MP4 out of an
# already-sliced window would drop valid videos that sort after it and make the
# route's has_more wrong. Read only as far as needed to fill the requested window.
want = None if limit is None else offset + limit
records = []
for path in paths:
meta = _read_meta(_sidecar_path(path.stem))
if meta is None: # no readable sidecar (orphan mp4) — skip
continue
records.append(_record(path.stem, meta))
if want is not None and len(records) >= want:
break
return records[offset:] if limit is None else records[offset : offset + limit]
def delete(video_id: str) -> bool:
"""Remove both files of a pair; True if the MP4 existed."""
path = video_path(video_id)
if path is None:
return False
# Best-effort on the sidecar: a leftover json without its mp4 is skipped by
# list_videos anyway, so a failed sidecar unlink must not fail the delete.
try:
_sidecar_path(video_id).unlink()
except OSError:
pass
try:
path.unlink()
return True
except OSError as exc:
logger.warning("video_gallery.delete_failed: %s", exc)
return False
def clear() -> int:
"""Delete every gallery pair; return how many videos were removed."""
removed = 0
try:
paths = list(gallery_dir().glob("*.mp4"))
except OSError:
return 0
for path in paths:
# Drop the sidecar first; an orphaned json is harmless (skipped by list).
try:
_sidecar_path(path.stem).unlink()
except OSError:
pass
try:
path.unlink()
removed += 1
except OSError:
continue
return removed