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.
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
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 keep-current-scheduler sentinel leaked into user-facing surfaces:
infotexts and filenames recorded Sampler: None, restoring such an
infotext pushed an invalid value into the dropdown, and the API
samplers list never offered Default at all.
- infotext always records the sampler, mapping a stray None to Default
- parse maps legacy Sampler: None infotexts to Default
- api samplers list leads with Default; the remaining excluded config
keys are shared templates, not samplers
ToriiGate 0.5 is a Qwen3.5 vision fine-tune trained on a single system prompt
and a single query structure, and it degrades on anything else. The shared qwen
handler strips angle brackets and underscores from the question, which mangles
the model's format templates, and the generic caption instructions are not what
it was trained on.
- hold the model's caption formats, system prompt and query builder in modules/caption/toriigate.py
- build the system prompt and user query from those templates in the qwen handler
- offer the native formats in the task dropdown and in the caption api prompt groups
- drop Normal Caption for this model, which has no format between short and long
generate.py: p.ip_adapter_masks was reinitialized inside the per-adapter loop, discarding all but the last adapter's masks; move it beside the other accumulators. process.py: req.params is dict|None, so a null params body crashed .items() in post_preprocess/post_mask. api.py: split(':') without maxsplit broke auth/auth-file entries whose password contains a colon. gallery.py: allowed_paths stored quote(path) but the membership check and path guards use the raw path, causing duplicate accumulation and an ineffective whitelist; also drop the unused FastAPI import (pylint W0611 surfaced when this file is linted).
Co-Authored-By: Claude <noreply@anthropic.com>
Reorder samplers_data_diffusers into recognizable solver-family groups (Euler, DPM/DPM++, UniPC/DEIS, Heun/KDPM2, ER-SDE, Classic, Distilled, Misc), each ending with its FlowMatch variants, and Res4Lyf as a fenced experimental section, so the dropdown is scannable.
Dividers are SamplerData sentinels with U+2500 names: create_sampler keeps the current scheduler when one is selected, get_sampler_name falls back to Default, set_samplers and validate_sampler_name exclude them, and a visible_samplers() helper drops them from the xyz axes, detailer, and folder pickers. The main and refine dropdowns render them as section labels. No sampler is removed or renamed, so saved infotexts, styles, and API calls keep resolving.
img2img still defaulted sampler_name to UniPC after the txt2img default was switched to Default in 7fbac675f, so an img2img request that omits the sampler forced UniPC instead of keeping the model's own scheduler. The control endpoint already used Default; align img2img with both.
validate_sampler_name only matched the exact, case-sensitive name, so near-miss client names such as lowercase variants were rejected while an omitted name silently used the model scheduler via the Default sentinel. Fall back to find_sampler and return the canonical name so create_sampler applies the intended sampler; unknown names still return 404. Add an API test covering case-insensitive resolution and rejection of unknown names.
Surface YoloRestorer.restore() as a standalone operation: a Detailer
postprocessing script in the Process tab and a thin /sdapi/v1/detail
endpoint, neither requiring a base generation pass.
- modules/postprocess/yolo.py: YoloRestorer.make_processing() builds the
synthetic Img2Img processing object both entry points feed to restore(),
resolving the seed so the inpaint passes are reproducible
- modules/api/process.py: post_detail handler exposes the full detailer
parameter set and returns the detailed image plus optional annotations
as base64
- scripts/postprocessing_detailer.py: reuses shared.yolo.ui('extras') and
runs through make_processing()
- modules/postprocessing.py: run_extras takes a per-script script_args
dict, also letting the extras API drive other scripts such as Remove
background; omitting it leaves existing callers unchanged
- modules/api/models.py: ReqDetail / ResDetail
- modules/processing_info.py: guard create_infotext's Image/Hires CFG
reporting against an unset (None) cfg_image, matching the is-not-None
checks the other cfg_image readers use; the detailer inpaint pass runs
with it unset
- test/test-detailer-api.py: covers both paths; effect tests measure the
diff inside the detected region with extreme isolated parameter values,
and the suite disables model quantization for the run and restores the
original settings afterward
CalamitousFelicitousness
Vladimir Mandic
QualiaRain