Address automated review feedback on the always-on assets change:
- setup_database(): on DB init failure or missing dependencies, call
disable_assets_routes() + asset_seeder.disable() so /api/assets/* returns a
clean 503 instead of 500s against an uninitialized DB (restores the fail-safe
the removed else-branch used to provide).
- feature_flags: derive the advertised 'assets' capability from live backend
availability instead of hardcoding True, so clients degrade gracefully.
- cli_args: re-add --enable-assets as a hidden deprecated no-op so existing
launchers still passing the flag don't abort argparse.
- routes: add assets_enabled() accessor for the feature-flag derivation.
The asset system is now always on. Removes the --enable-assets opt-in
flag and the conditional registration it gated:
- Asset API routes are always registered (with the user manager)
- The background asset scanner always runs (hashing stays opt-in via
--enable-asset-hashing, default off — no new blake3 cost on a default launch)
- /upload/image asset registration and output enrichment always active
- The "assets" server feature flag is now unconditionally true
- A database-init failure degrades gracefully instead of hard-exiting,
matching the prior default-path behavior
Add a --enable-asset-hashing CLI flag (action=store_true, default False)
and plumb it into the two asset-seeder call sites in main.py that
previously hardcoded compute_hashes=True (the startup scan and the
post-job output enqueue). Local runs now skip blake3 hashing unless the
user opts in, avoiding the startup/per-output cost on large models
directories while keeping hashing available for asset-portability
features.
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>
* main: implement --vram-headroom
Implement --vram-headroom for dynamic vram as a hybrid debug/diagnostic
option that can be used for people who still report shared VRAM spills.
They can trial and error the setting to maintain a bit more headroom to
avoid shared VRAM spills.
* main: implement --reserve-vram
Implement --reserve-vram as extra headroom on the simple method which
is semantically as close as possible to the stated functionality and
formet behaviour of non-dynamic VRAM.
Add this option for users who know they have so much ram they want
to pin everything or have a pagefile that outruns their disk speed.
The removes the RAM pressure caps completely and pins behind the
primary model load forcing all models to be permanently comitted
to RAM.
Some custom nodes .to weights completely out of load context which
can wreak havoc if its for a model that is not active. Detect this
condition and just let it fall-through to the non-dynamic loader
straight up.
Some custom nodes try to set this true globally. It messes with dynamic
VRAM with one-off spikes that can OOM but this is also very high risk
for windows where such allocations might get serviced by shared memory
fallback.
Trump it.
cleanup_models_gc can be called once per load_models_gpu via
free_memory, which in turn can de-activate an active model via
this reset_cast_buffers.
cleanup_models_gc() could also come via obscure garbage collector
paths so limit reset_cast_buffers to the post-node callsite instead.
* mm: split off registration helper to doer and headroom calc
* pinned_memory: implement registration comfy side
Move away from Aimdo buffer registrations which seem fraught with
danger and do it comfy side. Just start with the basic move.
* pinned_memory: do registrations as portable memory
* pinned_memory: discard async errors on registration fail
Like the good ol days.
* pinned_memory: implement abs shortfall retry
If pinned registration happens to fail despite the previous budget
ensures, consider the allocation shortfall, ensure it again, and
try again. This allows comfy pins to interoperate with other software
that might be doing substantive pinning.
* fix (MultiGPU): prevent freeze on manual abort when using MultiGPU CFG Split
Problem:
Upon manual abort application hangs indefinitely.
`InterruptProcessingException` inherits from `BaseException` and bypasses MultiGPU's worker error handling block so thread dies silently, leaving the main thread waiting forever for `result_q.get()`
Fix:
Catch `comfy.model_management.InterruptProcessingException` instead of `Exception` so it's caught and passed back via `result_q` to unblock the main thread when manual abort signal fires.
* oops
Matt Miller
j2gg0s
Silver
comfyanonymous
Jukka Seppänen
Barish Ozbay
rattus
Jedrzej Kosinski
Kohaku-Blueleaf
Talmaj
kelseyee
Alexis Rolland
John Pollock
Quasar of Mikus
person4268