The modular branch returned the pipe instead of assigning it, so the video
tab and the API failed with "model not loaded" and the whole post-load tail
was skipped along with it.
The registry helpers filtered only the 'None' placeholder, so the eleven
LTX separator rows resolved as models and a POST naming one reached
load_model with a null repo. is_model() now covers both sentinel kinds.
DiffusionPipeline.download derives ignore_patterns from the passed components and
never reads the caller's, so the kwarg set in load_override was inert and diffusers
logged it as an unexpected keyword. The unsharded connectors duplicate it was meant
to skip was fetched on every LTX-2.3 mirror load.
Resolve the repo to a snapshot built from those patterns and hand from_pretrained
the folder instead of the id. That skips its download path, so the passed component
folders it would have pruned are pruned here instead. The connectors override drops
its own copy of the kwarg, which ModelMixin never read either.
Selected from the specs the pipe already declares, update_components first and
load_components for the rest, so only what the workflow declares gets fetched. Weights
land in hfcache. Covers the TODO on component names.
Encoder sharing left off: shared_te_map matches a substring of the repo name and 4b hits
_dynamic_4bit, redirecting Qwen3-VL to another repo.
None from a family loader means unhandled, so the chain falls through to the folder
loader, which for a modular pipe builds an object holding only its from_config helpers
and installs it. Refuse it when none of the from_pretrained specs materialized.
load_components leaves a failed component as None and carries on, so the pipe reaches
generation short of one and fails somewhere unrelated. Compare against what the workflow
declares and refuse a pipe missing any of it. The conditioner fallback goes too, since
loading it another way just defers the failure into generation.
The ref2va checkpoint partition conditions on reference images instead
of keyframes, so it gets its own registry row and reference card, and
the video core marshals PIL images into task_args as
MiniMaxH3ImageReference. Images are converted to RGB first, since the
reference encoder reads the array raw. The keyframe path is unchanged.
Validation runs before the model load in one funnel shared by the tab
and the API, so a rejected request costs nothing: references on a
non-reference model, a reference model with nothing to condition on,
more than nine images, non-images, and aspect outside 1:4 to 4:1 all
return 400. The image path rejects a reference pipe without references
instead of reaching a transformer that was never loaded.
Add POST /sdapi/v1/video plus GET /sdapi/v1/video/models and
GET /sdapi/v1/video/file. The generation body is extracted from the
gradio handler into a keyword-only core, video_run.run, which returns a
structured result and raises typed errors; the positional generate
signature is unchanged and now adapts to the core. Omitting engine and
model drives the currently loaded checkpoint when it is video-capable,
which covers models loaded from local folders without a registry entry.
- registry helpers in models_def (find, engines, pipeline_classes,
workflow_for_class); validate_pipeline reuses the shared class set
- modular pipes stamp their workflow so out-of-registry loads dispatch
onto the modular branch
- disk switches (mp4_*) and wire switches (send_*) are independent;
artifacts above the base64 cap fall back to path plus the file route,
which is jailed to the video output directory and serves video/mp4
with range support
- always-on video scripts get bootstrapped default args, matching the
txt2img handler; missing bootstrap raised a TypeError per frame
- checkpoint overrides are rejected with a pointer to the checkpoint
endpoint; unknown engine, model and sampler names return 404 with the
valid choices
- cli/api-video.py client, test/test-video-api.py suite and a
full-test.sh entry; video mimetypes registered; rate-limit cost set
- remove the unreferenced video_ui.run_video dispatcher
The modular override forces Default instead of the None sentinel:
Default restores the model's own scheduler, which is the bespoke pair.
The reference entries request sampler: Default to match.
The local-folder skip in hf_auth_check keyed on model_index.json only,
so modular pipeline folders fell through to a hub auth check with a
filesystem path as the repo id.
Saving a modular pipeline now rewrites the component references in its
index to the destination folder, so a reload uses the saved quantized
weights instead of following the specs back to the source repositories.
The folder scan accepts modular_model_index.json for direct folders,
matching the snapshot branch, so saved pipelines list in the model
dropdown.
An empty prompt tokenizes to zero tokens and the conditioner fails
reshaping an empty sequence; a single space keeps the generate-on-empty
behavior other models have.
Modular pipelines run every stage inside one pipeline call, leaving
the ui on a single inference label. Forward hooks on the text encoder,
transformer and vae decoders now surface the current stage through
shared state, and the interrupt check runs in every stage so encodes
and tiled decodes abort promptly.
- saving a model registers a job instead of appearing idle
- group offload logs each component before the pin step instead of
only after completion
The modular text encoder quant config now excludes the vision tower.
The default skip pool covers diffusion module names, so qwen-vl vision
blocks quantized alongside the language layers; vision blocks have no
validated quantization precedent and run only for keyframe workflows.
Modular pipelines flagged as base models in the video registry cache
into the diffusers folder, so the folder scan and the model dropdown
pick them up once downloaded and the reference card reports them as
ready; video-only models keep caching into the shared cache and stay
out of the dropdown. The snapshot scan accepts modular_model_index.json
when the plain index is absent.
The modular loader passes sdnq quantization configs to load_components
as a per-component dict: transformers take the model config, the text
encoder takes the te config, and components without an entry load
unquantized. Pre-quantized repositories keep their own config, which
diffusers detects before a passed config applies.
Reference entries for the bf16 repo and the sdnq uint4 quant load the
modular pipeline through the standard dispatch. Image tabs run the
model in still mode with audio off; the video tab keeps its own
overrides through the shared per-generation hook. Detailer is not
supported and is disabled with a warning.
First natively modular model: the pipeline is driven directly through
ModularPipeline, with components fetched per workflow (fl2va covers
text and first/last-frame conditioning).
- per-generation overrides snap the canvas to /32, align frames to
the 17n+5 grid and duration window, and keep the bespoke scheduler
pair
- group offload for modular pipelines applied per component in
sd_offload; re-application is a guarded no-op
- audio checkbox pops the audio decode block so decode and muxing are
skipped
- frames=1 renders a single still image: the duration floor lifts per
instance and sub-floor latents pad at the vae decoder
- progress and interrupt handling via a transformer forward pre-hook
- vae scale factor override, tuple-safe patch size
Move the per-component placement walk from the api server into memstats
next to ram and gpu stats, and add it to the --monitor tick so offload
placement is visible in a log rather than only over the api.
- guard the walk internally: the supervisor loop logs its monitor line
unguarded, and the walk can race a reload or an offload rewrap
- monitor reports gb and merges into a fresh dict, since memory_stats
returns a module global that the per-generation log also prints
- endpoint keeps reporting raw bytes
Add a model section to /sdapi/v1/memory with loaded-model bytes summed
per pipeline component and device, so clients can tell resident weights
from offloaded ones and loop-critical components from edge ones.
- walk components over parameters and buffers, dedupe shared storages,
key by component name then device type
- read the raw model slot so a memory poll never triggers a model load
- section is exception-isolated like ram and cuda; reports an error
string if the walk races a reload
The guard compared component size against half of total system memory and
silently dropped to unpinned streaming above that. Total is the wrong
quantity: a component that fits comfortably in free memory gets denied, and
unpinned leaf streaming is slow out of proportion to its size because every
per-leaf transfer pays staging cost.
Budget against memory free at apply time less a reserve, and cache the
verdict on the module since a granted pin lowers the same reading it derives
from. A denied pin now also drops streams and falls back to block_level,
since leaf plus stream is only the right shape when weights are pinned, and
logs the per-step transfer volume so the cost of the slow path is visible up
front.
Model switches kept most of the previous model resident, and the next
load could stall in kernel reclaim while the freed memory was still held.
- strip group offload hooks in disable_offload so the meta move at unload
actually frees component weights; hook removal resolves wrapper
components that carry hooks on the inner model
- flush the torch pinned host cache in torch_gc so freed streaming
buffers return to the OS instead of staying cached in-process
- skip the pipe-level accelerator move for group-managed pipes: the
offload engine already placed every component, and the move only
dragged on-demand components to the accelerator for the trailing
eviction to undo
Tab runners pre-move the vae module to the gpu before generation,
which parks an on-demand component on the accelerator for the whole
denoise. Moves of a stamped module toward the accelerator now return
early; the entry bridge onloads it when its encode or decode runs.
The per-component stats block printed only from the balanced offload
path, so group mode loads showed no component classes, sizes or
quantization. Group and modular applies now print the same block once
per loaded model, with sizes measured directly when no balanced hook
map exists.
A pipeline-level move to the accelerator carries on-demand components
along with it, since modular pipelines skip only group-hooked modules;
the load path then left a resting vae on the gpu until its first use.
Bulk moves now re-evict stamped components, whose entry points onload
them when needed.
Vae-class components never take group hooks, so group mode kept them
resident on the gpu; a MiniMax-class video vae holds about 10GB that
way while running only seconds per generation. Components above 1GB
now rest in system memory: the apply_forward_hook bridge on encode and
decode fires an on-demand hook that moves the whole module to the
device, so tiled calls find every weight already loaded, and the
processing seams return it to cpu once outputs are materialized. Small
vaes stay resident since the transfer would cost more than it frees.
- placement is decided per component by measured size and requires the
entry bridge; components without it stay resident
- move_model no longer forces on-demand vaes to the gpu for
non-txt2img tasks, and full_vae_encode onloads before binding the
input, which otherwise lands on the resting device
- mode switches clear the stamp and hook in both directions
Group offload hooks report the onload device at module level while the
weights rest on cpu, so every native apply took the parameter
replacement branch in assign_weight and detached the written layers
from the hook's group bookkeeping. The activation and deactivation
walks now remove a component's group hooks before its first weight
write and reapply offload at the end of the pass: writes land in place
on the resting tensors and fresh groups snapshot the result.
- hooks come off lazily, only for components with a covered layer or a
pending backup or factor-stash restore; repeat activations with an
unchanged set leave the hooks untouched
- remove_group_offload_component follows wrapper components to the
inner model that carries the hooks
Vladimir Mandic
CalamitousFelicitousness