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
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.
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
Add endpoints for browsing, downloading, and deleting dicts hosted on
HuggingFace. Manifest is fetched and cached from the remote repo to
show available dicts with download status and version comparison.
- /dicts/remote GET: list available dicts from HF manifest
- /dicts/{name}/download POST: download dict from HF with atomic write
- /dicts/{name} DELETE: remove local copy and evict cache
- ItemDictRemote model with downloaded/update_available flags
- Exclude manifest.json from local dict listing in all scan paths
Move all caption/interrogate/tagger/VQA API code out of the monolithic
endpoints.py and models.py into a new self-contained modules/api/caption.py,
following the loras.py / nudenet.py self-registering pattern.
- Move 15 Pydantic models (ReqCaption, ResCaption, ReqVQA, ResVQA,
ReqTagger, ResTagger, dispatch union types, etc.) from models.py
- Move 11 handler functions from endpoints.py
- Deduplicate ~150 lines via shared _do_openclip, _do_tagger, _do_vqa
core functions called by both direct and dispatch endpoints
- Add register_api() that registers all 8 caption routes
- Add promptgen field to ResVLMPrompts (bug fix: handler returned it
but response model silently dropped it)
- Improve all endpoint docstrings and Field descriptions for API docs
- Remove caption_openclip_min_length from settings, API models, endpoints, and UI
(clip_interrogator library has no min_length support; parameter was never functional)
- Split vlm_prompts_florence into base Florence prompts and PromptGen-only prompts
(GENERATE_TAGS, Analyze, Mixed Caption require MiaoshouAI PromptGen fine-tune)
- Add 'promptgen' category to /vqa/prompts API endpoint
- Fix gaze detection: move DETECT_GAZE check before generic 'detect ' prefix
to prevent "Detect Gaze" matching as detect target="Gaze"
- Update test suite: remove min_length tests, fix min_flavors to use mode='best',
add acceptance-only notes, fix thinking trace detection, improve bracket/OCR tests,
split Florence/PromptGen test coverage
- Update cli/api-interrogate.py to use /sdapi/v1/tagger for DeepBooru
- Handle tagger response format (scores dict or tags string)
- Remove DeepBooru test from interrogate endpoint tests
- Update API model descriptions to reference tagger for anime tagging
Add prompt field to VQA endpoint and advanced settings to OpenCLIP endpoint
to achieve full parity between UI and API capabilities.
VLM endpoint changes:
- Add prompt field for custom text input (required for 'Use Prompt' task)
- Pass prompt to vqa.interrogate instead of hardcoded empty string
OpenCLIP endpoint changes:
- Add 7 optional per-request override fields: min_length, max_length,
chunk_size, min_flavors, max_flavors, flavor_count, num_beams
- Add get_clip_setting() helper for override support in openclip.py
- Apply overrides via update_interrogate_params() before interrogation
All new fields are optional with None defaults for backwards compatibility.
Update API model field descriptions to match the hints in locale_en.json
for consistency between UI and API documentation.
Updated models:
- ReqInterrogate: clip_model, blip_model, mode
- ReqVQA: model, question, system
- ReqTagger: model, threshold, character_threshold, max_tags,
include_rating, sort_alpha, use_spaces, escape_brackets,
exclude_tags, show_scores
Add comprehensive caption/interrogate API with documentation:
- GET /sdapi/v1/interrogate: List available interrogation models
- POST /sdapi/v1/interrogate: Interrogate with OpenCLIP/BLIP/DeepDanbooru
- POST /sdapi/v1/vqa: Caption with Vision-Language Models (VLM)
- GET /sdapi/v1/vqa: List available VLM models
- POST /sdapi/v1/vqa/batch: Batch caption multiple images
- POST /sdapi/v1/tagger: Tag images with WaifuDiffusion/DeepBooru
Updates:
- Add detailed docstrings with usage examples
- Fix analyze_image response parsing for Gradio update dicts
- Add request/response models for all endpoints
- Remove /sdapi/v1/face-restorers route from api.py
- Remove get_restorers() function from endpoints.py
- Remove gfpgan_visibility, codeformer_visibility, codeformer_weight
fields from ReqProcess model
- Remove GFPGAN and CodeFormer entries from run_extras() signature
and create_args_for_run dict in postprocessing.py
Vladimir Mandic
CalamitousFelicitousness
awsr