Backend:
- Sanitize a blank hf_token to None in begin_load and load_pipeline, so the
default empty Studio token loads anonymously instead of 401ing as an explicit
empty credential.
- Free the ACTIVE diffusion engine before LLM training and in the delete-cached
guard: on a native (sd_cpp) selection the diffusers singleton reports
unloaded, so training could start against a live sd-cli generation and
delete-cached could remove a GGUF the native engine is using. Both now go
through diffusion_engine_router.get_active_diffusion_engine().
- Refuse delete-cached while a background image load is downloading the repo
(or its companion base): status().loaded is False in that window, but the
delete would yank blobs from under the in-flight assembly. Both engines
expose the in-flight ids via a new loading_repo_ids().
- Cap request seeds at 2**53-1: seeds round-trip through JSON gallery recipes,
where JavaScript rounds larger integers, so a restored recipe generated a
different image. Random seeds were already masked to this range.
- Add the task field to CachedModelRepo: the handler sets it for cached
diffusers image repos but response_model silently dropped it, letting
image-only repos pass the chat picker's task gate.
Frontend:
- Offset sequential run seeds by the batch size: the native engine seeds image
j of a run at seed+j, so a +1 run offset regenerated the previous run's
batch-mates.
- Revert the optimistic quant selection when a load fails to start.
- Stop disabling the Images page on chat-only hosts: the native sd.cpp engine
exists exactly for the no-GPU route.
Address review findings on the LoRA path:
- resolve_one: normalise a blank/whitespace hf_token to None (anonymous access)
and reject a client-supplied weight file with traversal / absolute path.
- resolve_specs: convert FileNotFoundError from an unknown/stale id to ValueError
so the route returns 400 instead of a generic 500.
- _scan_local: disambiguate local adapters that share a stem (foo.safetensors vs
foo.gguf) so each is uniquely addressable.
- inject_prompt_tags: the backend-validated weight now wins over a user-typed
<lora:ALIAS:...> for a selected adapter; unselected user tags are left alone.
- diffusers _apply_loras: reject a .gguf adapter with a clear error before touching
the pipe (diffusers loads safetensors only).
- _unload_locked: drop the explicit unload_lora_weights() on teardown; the pipe is
dropped wholesale (freeing adapters), so the previous call could race an in-flight
denoise on the same pipe.
- Images page: use a stable LoRA key and clear the selection (not just the options)
when the catalog refresh fails.
- resolve_controlnet enforces catalog family compatibility so a direct API call
cannot load a ControlNet built for another family through the wrong pipeline.
- Unknown ControlNet ids now surface as a 400 (call site maps FileNotFoundError
to ValueError) instead of a generic 500.
- strength 0 disables ControlNet entirely, so a no-op selection never pays the
download / VRAM cost; the control image is decoded and validated BEFORE the
ControlNet is resolved or built, so a malformed image fails fast for the same reason.
- ControlNet loads use the base compute dtype (state.dtype is a display string,
not a torch.dtype, so it silently fell back to float32) and honor the base
offload policy via group offloading instead of forcing the module resident.
- Empty/malformed HF token coerced to anonymous access.
- Flux Union ControlNet control_mode mapped from the selected control type.
- resolve_controlnet drops the unused hf_token/cancel_event params.
- ControlNetSpec validates guidance_start <= guidance_end (clean 422).
- Images UI ControlNet Select shows its placeholder when nothing is selected.
Adds regression tests for family enforcement and the union control-mode map.
SDXL is the first U-Net family in the diffusion backend: its denoiser is
pipe.unet (UNet2DConditionModel), not a DiT pipe.transformer, and a single-file
.safetensors is the whole pipeline rather than a transformer-only file. The
backend previously assumed a DiT transformer everywhere, so add the two hooks a
U-Net family needs and register SDXL.
DiffusionFamily gains denoiser_attr ("transformer" for DiT, "unet" for SDXL) and
single_file_is_pipeline (SDXL loads a single file via pipeline_class.from_single_file
with the base repo as config, instead of transformer_class.from_single_file plus a
companion assembly). _align_vae_dtype now reads the denoiser generically so img2img
and inpaint keep the VAE and U-Net dtypes aligned.
The non-GGUF trust gate is extended with a short, exact-match, safetensors-only
allowlist of official base repos (the SDXL base/refiner and sdxl-turbo), because
SDXL ships only as a full pipeline and has no unsloth-hosted GGUF. Local paths stay
trusted as before; a random repo, even one that detects as SDXL, is still rejected.
The image-conditioned and ControlNet workflows are the standard SDXL pipelines,
built around the resident modules via from_pipe like every other family, so SDXL
gets txt2img, img2img, inpaint, outpaint, upscale, LoRA and ControlNet. There is no
native sd.cpp mapping yet, so the no-GPU route falls back to diffusers.
Frontend catalog gains SDXL Base 1.0 and SDXL Turbo entries with SDXL step/guidance
defaults (Turbo: few steps, no CFG; base: ~30 steps, real CFG).
Tests: new test_diffusion_sdxl.py (family shape, detection, trust allowlist, model
kind, U-Net VAE-dtype alignment, LoRA gate) plus loader-branch tests in
test_diffusion_backend.py (pipeline-kind from_pretrained, single-file whole-pipeline
from_single_file, allowlist accept/reject). Verified live on GPU: sdxl-turbo loads
both as a pipeline and as a single file and generates coherent txt2img + img2img.
Loading unsloth/FLUX.2-dev-GGUF failed because detect_family knew only the
Qwen3-based FLUX.2-klein, so FLUX.2-dev (the full, Mistral-based Flux2Pipeline)
resolved to nothing and the load errored. Add a flux.2-dev family: Flux2Pipeline
+ Flux2Transformer2DModel over the black-forest-labs/FLUX.2-dev base repo (gated,
reachable with an HF token), with its FLUX.2 32-channel VAE and Mistral text
encoder wired for the sd-cli path from the open Comfy-Org/flux2-dev mirror.
text-to-image only: diffusers 0.38 ships no Flux2 img2img / inpaint pipeline for
dev. Frontend gets sensible dev defaults (28 steps, guidance 4), distinct from
klein's turbo defaults. Verified live: GGUF load resolves the family + gated base
repo and generates a real 1024x1024 image on GPU.
The Images picker's On Device tab hid every non-GGUF cached repo whenever a
task filter was active, so downloaded unsloth diffusion pipelines (bnb-4bit
and FP8 safetensors) never showed up there. List cached repos that pass the
task gate, limited under a filter to unsloth-hosted ones so base repos (which
fail the diffusion load trust gate) don't appear only to dead-end on click.
Chat behavior is unchanged: the task gate still drops image repos there.
Renamed the confusing "Transformer quant / GGUF default" control to "GGUF speed mode"
with an "Off (run the GGUF)" default, and reworded the hint to state plainly that FP8/INT8/
FP4 load the FULL base model (larger download + more VRAM) rather than re-packing the GGUF,
falling back to the GGUF if it can't fit. Behavior unchanged; labels/hint only.
The curated bnb-4bit / fp8 diffusion rows were filtered out of the Images picker's
Recommended list once cached (curatedSafetensorsRows dropped anything in downloadedSet),
so they vanished from the picker after the first load and could only be found by typing an
exact search. The row already renders a downloaded badge, matching how GGUF Recommended
rows stay visible when cached. Drop the exclusion so the curated safetensors always list.
handleModelSelect only loaded curated safetensors ids and GGUF variant picks; any other
non-GGUF pick (an on-device diffusers folder, or a future unsloth diffusers image repo
surfaced by search) silently did nothing. Treat such a pick as a full diffusers pipeline
load when the id is unsloth-hosted or on-device (the backend infers the family + base repo
and gates loads to unsloth/* or local paths), and show a clear message otherwise instead
of silently ignoring the click. Curated and GGUF paths are unchanged.
Some diffusers image repos (e.g. unsloth/Qwen-Image-2512-unsloth-bnb-4bit) carry a
stray "gguf" tag on the Hub but ship no .gguf files. The model search classified
them as GGUF from the bare tag, so the picker rendered the GGUF variant expander,
which then dead-ended at "No GGUF variants found." Trust the bare gguf tag only when
the repo is not a diffusers pipeline; the -GGUF name suffix and real gguf metadata
(populated via expand=gguf) remain authoritative, so genuine GGUF repos are unaffected.
The ControlNet control gated on workflow === 'txt2img', but the Images workflow tab ids are create/transform/inpaint/extend/upscale/reference/edit -- there is no 'txt2img'. So the picker never rendered even with a ControlNet-capable model loaded. Gate on 'create' (the text-to-image tab) for both the picker and the request wiring. Found via a live Playwright capture of the running Studio.
Add ControlNet conditioning, the #2 most-used diffusion workflow after
LoRA, on the diffusers backend for the families with ControlNet pipelines
(FLUX.1 and Qwen-Image), with Union models as the default picks.
Backend
- New core/inference/diffusion_controlnet.py: family-gated discovery
(curated Union models + local dirs + bare owner/name repos), resolution
to a loadable repo/dir, control-image preprocessing (passthrough +
a dependency-free canny edge map), and a supports_controlnet gate.
- diffusion.py: a ControlNet manager parallel to the LoRA one. Loads the
(small) ControlNet model once via from_pretrained (cached by id) and
builds the family's ControlNet pipeline via Pipeline.from_pipe(base,
controlnet=model), reusing the resident base modules at their loaded
dtype (no reload, no recast). Passes the control image + conditioning
scale + guidance start/end at generate time; cleared on unload.
- Families: FLUX.1 -> FluxControlNetPipeline/Model, Qwen-Image ->
QwenImageControlNetPipeline/Model. Others declare none (gated off).
- Gated off for the native engine, GGUF-via-diffusers, and torchao
fp8/int8 dense (same rule as LoRA). v1 conditions txt2img only.
- Request contract: optional controlnet on DiffusionGenerateRequest;
supports_controlnet in status; the choice persisted in gallery meta.
- New GET /api/models/diffusion-controlnets for the picker.
Frontend
- A ControlNet control in the Images rail (model select + control-image
upload + control-type select + strength slider), gated by the loaded
model's supports_controlnet + family, shown for text-to-image.
Tests
- New test_diffusion_controlnet.py (10): discovery/resolve/preprocess/gate
helpers, request validation, family wiring, and the diffusers pipe
manager (loads once, caches, from_pipe with controlnet, rejects
unsupported families).
Add community LoRA support across both diffusion backends, the single
biggest step toward broad image-workflow coverage.
Backend
- New shared module core/inference/diffusion_lora.py: adapter discovery
(local scan + curated catalog + owner/name[:file] Hub refs), download
via hf_hub_download_with_xet_fallback, alias sanitization, native
managed-dir materialization with collision-broken aliases, prompt-tag
injection (deduped against user-typed tags), and a supports_lora gate.
- Native sd-cli: resolve + materialize selected LoRAs into a per-run
managed dir, inject <lora:ALIAS:w> tags, pass --lora-model-dir with
--lora-apply-mode auto. The arg builder already emitted these flags.
- Diffusers: non-fused load_lora_weights + set_adapters manager, tracked
on the pipe so an unchanged selection is a no-op and a model swap
resets; cleared on unload. Never fuses (breaks quantized transformers
and blocks live weight tweaks).
- Gated off where unsupported: torchao fp8/int8 dense, GGUF-via-diffusers,
and native Qwen-Image (no LoRA name-conversion branch upstream).
- Request contract: optional loras on DiffusionGenerateRequest; empty or
omitted is identical to today. supports_lora surfaced in status; chosen
LoRAs persisted in gallery recipe metadata.
- New GET /api/models/diffusion-loras for the picker (family-filtered).
Frontend
- Repeatable multi-LoRA picker (adapter select + weight slider 0..2 +
remove), gated by the loaded model's supports_lora and family, max 8.
Tests
- New test_diffusion_lora.py (14): helpers, request validation, native
tag/dir wiring, diffusers set_adapters manager, supports_lora matrix.
Backend:
- Load non-GGUF safetensors models: full bnb-4bit pipelines and single-file
fp8 transformers, gated to the unsloth org plus a curated allowlist.
- Image-conditioned workflows built with Pipeline.from_pipe so they reuse the
loaded transformer/VAE/text-encoder with no extra VRAM: img2img, inpaint,
outpaint, and a hires-fix upscale pass.
- Instruction editing as its own family kind (Qwen-Image-Edit-2511,
FLUX.1-Kontext-dev) and FLUX.2-klein reference conditioning (single and
multi-reference) plus klein inpaint.
- Auto-resize odd-sized inputs to a multiple of 16 (and resize the matched
mask) so img2img/inpaint/edit no longer reject non-/16 uploads. Bound the
decoded image size and cap upscale output to avoid OOM on large inputs.
- Fixes: from_pipe defaulting to a float32 recast that crashed torchao
quantized transformers; image-conditioned calls forcing the slider size
onto the input image. Native sd.cpp engine rejects image-conditioned and
reference requests it cannot serve.
Frontend:
- Redesigned Images page with capability-gated workflow tabs (Create,
Transform, Inpaint, Extend, Upscale, Reference, Edit), a brush mask editor,
client-side outpaint, and a multi-reference picker.
- Advanced options moved to a right-docked panel mirroring Chat: closed by
default, toggled by a single fixed top-bar button that stays in place.
sd.cpp installer: pin the release, verify each download's sha256, add a
download timeout, and make the source repo configurable for a future mirror.
* add models for /update endpoint
* add logic for identifying out of date hf models
* add endpoint for updating hf models
* add relevant field to GgufVariantDetail
* make exception handling better
* add update_available flag for cached_models, and moved /update endpoint from inference -> models
* hook up /update endpoint on the frontend
* implement update scenarios for the model picker
* fix bug where downloaded flag for an older revision was being wrongly set to false
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix import and make hf calls async
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* remove has_vision from UpdateRequest
* fix ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* clear cancel event before updating gguf variant
* set _cancel_event back if it was set initially
* add hf_token to get_paths_info
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: harden model update endpoint and update checks
- update_hf_model: pass snapshot_download local_dir (local_path is not a
valid kwarg and 500s when updating bicodec audio models)
- get_gguf_variants: wrap the remote update check so a network, rate-limit,
gated, or offline failure degrades to "no update info" instead of failing
the whole variant listing, matching list_cached_models
- add regression tests for both paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: HF model update detection and Update action for cached models
Surface an "Update available" cue and a managed Update action for cached
on-device models. /api/hub/update-status compares each cached main GGUF
file's local blobs against the remote main revision using set membership
across all cached revisions, so a repo that was already updated (and still
holds the old snapshot alongside the new one) is not falsely flagged.
The Update action re-downloads through the download manager so it shows in
the Downloads panel with progress and cancel. The frontend wires the Update
button into the GGUF, on-device, and model-selector cards and keeps the
quant label fully visible when the action buttons crowd the row.
Adds regression tests for the multi-revision update check.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: accept force_download kwarg in hf_xet_fallback test double
The download seam now passes force_download to the attempt callable; the _FakeAttempt mock did not accept it, failing 6 tests with TypeError. Add the keyword (default False) so the scripted-results double matches the seam.
* Fix Studio model update regressions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address Studio update review feedback
* Address Studio update edge cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Share GGUF update status helper
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix GGUF update detection and cache cleanup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix cached GGUF update badges
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: shimmyshimmer <107991372+shimmyshimmer@users.noreply.github.com>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* i18n: register Japanese language support in messages
* i18n: add Japanese locale support
* Update studio/frontend/src/i18n/locales/ja.ts
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/frontend/src/i18n/locales/ja.ts
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* test(i18n): add ja locale to parity check
* i18n: fill remaining missing keys for Japanese locale
* i18n: fix terminal string localization in ja locale
---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* fix(studio/llama_cpp): disable trust_env on the loopback health probe
_wait_for_health() polls http://127.0.0.1:<port>/health with the default
httpx trust_env=True, so an ambient HTTP(S)_PROXY in the environment is
applied to the loopback request. A proxy that returns 503 for 127.0.0.1
makes every probe fail, so the loop runs until timeout and Studio load
hangs (trust_env=False returns 200 immediately).
Pass trust_env=False so the local readiness probe never goes through a
proxy. This mirrors the existing trust_env=False handling in the sibling
llama_http / external_provider HTTP clients.
* test(offline_gguf_cache): accept trust_env kwarg in fake_get mock
_wait_for_health now calls httpx.get(..., trust_env=False); update the retry test's fake_get to accept the kwarg so it doesn't raise TypeError.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/llama_cpp): bypass proxies for loopback clients
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/routes): bypass proxies for llama streams
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Studio: quick eject from the model selector
Add a one-click eject shortcut to the loaded-model pill so users do not
have to open the picker to unload a model.
- The loaded-status indicator shows a green checkmark at rest and swaps to
a red eject icon on pill hover, with an "Eject model" tooltip. Clicking
it ejects without opening the picker.
- On Device tab now uses the placeholder "Search local models" instead of
"Search Unsloth models".
- The picker's "Eject model" button uses medium font weight.
* Studio: drop unused group/eject marker class on the eject control
* Studio: make the inline eject control valid HTML
The eject shortcut was a focusable span (role/tabIndex) nested inside the
trigger button. A button's content model forbids focusable descendants, so
make it a plain decorative span (aria-hidden, no role/tabIndex) that keeps
the mouse shortcut. Keyboard and screen-reader users eject via the picker's
"Eject model" button.
* Studio: disable the inline eject shortcut on touch devices
On touch (no hover) the red eject icon and title tooltip never reveal, so
tapping the loaded pill could unload the model with no visible affordance.
Add [@media(hover:none)]:pointer-events-none so taps fall through to the
trigger and open the picker; touch users eject from the picker instead.
---------
Co-authored-by: shimmyshimmer <shimmyshimmer@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
* Add whole-document context mode to RAG chat attachments
Thread-attached files are injected in full when they fit a token budget,
instead of only top-K retrieved chunks, so the model reads the entire file
for summarize/reason-over-document requests. Oversized files fall back to
top-K retrieval so the context window is never blown. KB and project
corpora are unchanged (still retrieval).
- core/rag/store.py: all_chunks_for_scope returns every completed-document
chunk for a scope, ordered document-then-index, joined with filename.
- core/rag/tool.py: whole_document_context renders the chunks as the same
<chunk> blocks + citation source-map retrieval produces, returns None
when empty or over budget.
- core/inference/tools.py: build_rag_autoinject tries whole-document first
for thread scopes, falls through to search_for_autoinject otherwise.
- core/rag/config.py: THREAD_WHOLE_DOC + WHOLE_DOC_MAX_TOKENS (env-tunable).
- tests/test_rag_whole_document.py: store ordering, whole-doc render +
budget cutoff, auto-inject whole-doc vs top-K fallback, KB never whole-doc.
* Add scanned-PDF OCR fallback to RAG ingestion
A PDF page with no extractable text layer (a scanned or image-only page)
previously ingested as empty, so image PDFs were invisible to retrieval and
whole-document context. Such pages are now rendered and transcribed by the
loaded vision model during ingestion, so they become searchable and readable
like any other page. This restores OCR for the RAG document flow without a
separate extraction pipeline.
- core/rag/parsers.py: render_pdf_pages renders whole pages (1-based) to PNG.
- core/rag/captioner.py: factor the shared vision call into _vision_complete;
add _ocr_one + ocr_pages (transcribe rendered pages, OCR_MAX_PAGES bound).
- core/rag/ingestion.py: _ocr_scanned_pages runs right after parse, replacing
text on near-empty PDF pages. No-op when OCR is off, no page is scanned, or
no vision model is loaded (degrades like figure captioning).
- core/rag/config.py: OCR_SCANNED, OCR_MIN_CHARS, OCR_MAX_PAGES, OCR_DPI,
OCR_TIMEOUT_S, OCR_MAX_TOKENS (env-tunable).
- tests/test_rag_ocr_fallback.py: page render, ocr_pages gating + cap, scanned
PDF end-to-end OCR into chunks + whole-doc, born-digital skips OCR, disabled
leaves the page empty.
* Broaden OCR prompt to figures/tables and guard against repetition runaway
The OCR prompt now asks the vision model to also transcribe text inside figures,
diagrams, charts and tables, so labels and table cells on scanned pages are
indexed rather than skipped. Verified on real documents that this does not
regress plain-text transcription.
Some vision models loop on sparse images (e.g. a title-only cover) and emit the
same line hundreds of times. _collapse_runaway caps any run of identical
consecutive lines so a pathological page cannot flood the index; legitimate
short repeats (a label appearing a few times) survive. Applied in ocr_pages.
* Restrict whole-document injection to thread attachments only
whole_document_context resolved the combined project+thread scope, so a project
chat (the frontend sends both thread_id and project_id) injected the entire
project corpus in full, contradicting the design that project and KB corpora stay
retrieval-only. A large project corpus could also push the total over budget and
drop a small thread attachment back to top-K.
Resolve the thread scope alone in whole_document_context, and in
build_rag_autoinject only enter whole-doc mode when a thread attachment is present
and no KB is selected (a KB pick is exclusive: search that corpus). Project
sources and KBs keep top-K retrieval. Adds regression tests for the mixed
project+thread payload, the budget isolation, and KB precedence.
* Address review: keep project retrieval, harden budget + OCR guards
Follow-up to the 8-reviewer pass on the whole-document + OCR work.
- Preserve project grounding in project chats. The thread-scope-only fix made
whole-doc exclusive of retrieval, so a thread attachment silently dropped the
project corpus for that turn. build_rag_autoinject now whole-docs the thread
attachment AND retrieves the project sources top-K, merged under one citation
numbering via tool.render_sources. KB selection stays exclusive.
- Budget: a NULL/zero token_count no longer bypasses the cap (length-based
fallback in _row_token_count), so a malformed huge doc can't inject in full.
- OCR runaway guard: _collapse_runaway now also caps each distinct line at a
generous total across the page (not just consecutive), bounding the
interleaved/alternating loops weak models emit; blank-line floods collapse too.
- OCR: warn when a scanned PDF exceeds OCR_MAX_PAGES (pages past the cap stay
untranscribed) instead of silently dropping them.
- Document the known limits: OCR'd pages have no PDF highlight regions; vision
models need a micro-batch >= image tokens (Gemma-family) or the server aborts.
- Tests for project-retrieval composition, NULL-token budget, and interleaved
runaway; drop the now-superseded exclude-project test.
* Add OCR toggle to RAG retrieval settings
Make scanned-PDF OCR user-controllable per upload instead of only via the
RAG_OCR_SCANNED config default. The retrieval settings panel gains an OCR
scanned pages switch (persisted in localStorage, on by default); the chosen
value is read fresh at upload time and sent with each document upload.
Backend: the three upload routes accept an optional ocr form field and pass it
through start_ingestion to _ocr_scanned_pages, which now treats None as use the
config default and an explicit bool as an override. The on/off policy lives only
in _ocr_scanned_pages now, so ocr_pages no longer re-checks the config (that
double gate would have blocked a per-upload ocr=True while the default was off).
Tests cover both override directions (force on while config off, force off while
config on).
* Add "Describe figures & charts" toggle with chart-aware captions
Surface RAG figure captioning as a user control and make it actually useful for
graphs and plots. The figure detection already clustered vector drawings and
raster images into regions and rendered them, but captioning was off by default,
had no UI, and used a thin generic prompt.
Accuracy: the caption prompt now asks for chart type, axis titles and units,
legend or series, salient trends and readable values, and table columns, while
forbidding invented numbers. The token budget is configurable (CAPTION_MAX_TOKENS)
and captions pass through the same runaway guard as OCR so a looping vision model
cannot flood the index.
Control: a per-upload caption override threads from the three upload routes through
start_ingestion and _run, with the on/off policy single-sourced in _run (caption
self-gating removed from caption_images, mirroring the OCR change) so a force-on
override works when the config default is off. The frontend adds a "Describe
figures & charts" switch in the retrieval settings, persisted in localStorage and
sent with each upload. Default on; it is a no-op without a vision model and bounded
to CAPTION_MAX_IMAGES figures per document.
Tests cover the new caption_images contract, the runaway guard on captions, the
chart-aware prompt and token budget (and that OCR keeps its own prompt and budget),
and both override directions end to end through ingestion.
* Generalize figure understanding: transcribe-first prompt + high-DPI tiling
Make figure/chart description work across any visual and any model strength, not
just a strong VLM on simple figures. Two changes, validated by a recall benchmark
on authoritative documents (ResNet/Attention papers, USDA, UN UDHR).
1. Transcribe-first caption prompt. The caption now asks the model to transcribe
every visible label verbatim (titles, axis labels and units, legends, every
box/node/arrow label, table cells, equations) and then add a one-line summary,
instead of only describing the figure. Transcription is the most model-robust
visual task, so weak models that cannot reason about a chart still recover its
labels.
2. High-DPI tiling of figure pages. Figure-bearing pages are rendered as an
overlapping grid of high-DPI tiles (plus a full-page pass for context); each
tile is transcribed, then merged and de-duplicated. This keeps small diagram
labels legible and covers every sub-figure without relying on exact region
detection, which previously missed sub-figures and small labels.
Supporting changes: figure render DPI 130 -> 200 with a clip margin so edge labels
are not lost; vision calls are deterministic (temperature 0) so transcription does
not randomly drop labels; the repetition guard now applies to captions too. New
config knobs: FIGURE_DPI, FIGURE_MARGIN_FRAC, FIGURE_TILE_ROWS/COLS, FIGURE_TILE_
OVERLAP, FIGURE_FULLPAGE, CAPTION_MAX_PAGES, larger CAPTION_MAX_TOKENS, and
CAPTION_MAX_IMAGES as a per-document tile budget.
Measured figure context recall (per-label, dense academic figures):
Qwen2.5-VL: 0.50 -> 0.83 (overall 0.81 -> 0.94)
Gemma-4-E2B (weak): ~0 with loops -> 0.83 (overall 0.91)
Born-digital text and scanned-page recall are unchanged (no regression).
parsers gains _figure_boxes (shared detection), pages_with_figures, and
render_pdf_figure_tiles; captioner gains merge_page_captions and a temperature
parameter; ingestion routes figure captioning through the tiled path.
* Fix RAG review issues: whole-doc budget pre-check, figure gating, empty re-ingest, vision auth
Whole-document context now runs a cheap token-sum pre-check (store.scope_token_estimate)
before hydrating every chunk's text, so an attachment that cannot fit the budget is
rejected without loading the whole corpus into memory. The estimate mirrors
all_chunks_for_scope's filter and the per-row token-count fallback exactly.
Ingestion skips all figure work (PDF rasterization and detection, not just the caption
call) unless a vision model is loaded, so a text-only deployment pays nothing. When OCR
is enabled, scanned/image-only pages are excluded from figure tiling since OCR already
transcribes them whole, avoiding double vision work and overlapping index entries; a
scanned figure page is still tiled when OCR is off.
start_ingestion no longer dedupes forever to a prior ingest that produced zero chunks
(e.g. a scanned PDF uploaded before a vision model was loaded): the empty record is
dropped and the content is re-ingested.
Vision OCR and caption requests now send the backend Authorization header, so they
match the chat endpoint and do not 401 under direct-stream (--api-key) mode.
Adds tests for the budget estimate, scanned-page exclusion, the vision-model gate, the
empty re-ingest path, and the auth-header passthrough.
* Trim RAG vision-ingestion comments and docstrings
Tighten the verbose multi-line docstrings and comments added across the RAG vision
ingestion work (captioner, config, parsers, ingestion, store, tool, build_rag_autoinject,
the RAG tests, and the chat-store/upload-hook frontend toggles) to one or two lines while
keeping their intent. No code changed: verified comment/docstring-only against the prior
commit, and the RAG test suite still passes.
* Fix figure-tiling exclusion and client dedupe for re-ingestable docs
Figure tiling now excludes only the pages OCR actually transcribed, not every
text-less page. _ocr_scanned_pages returns the set of pages it OCR'd, and _run passes
that to pages_with_figures as exclude_pages (replacing the ocr_on-keyed min_text_chars
heuristic). A scanned page that OCR skipped (past OCR_MAX_PAGES, or whose OCR returned
empty) is no longer dropped from captioning, so a chart on such a page still gets a
caption.
The document panel's upload dedupe no longer skips re-selecting a file whose only
matching doc completed with zero chunks. Such a doc is re-ingestable (e.g. a scan
attached before a vision model loaded), and the backend re-ingests on the same content
hash, so the client must let it reach the backend; healthy or still-indexing docs are
still skipped. The SSE complete frame's chunk count is recorded on the doc so the
check is exact.
Adds a regression test for the un-OCR'd scanned figure page and updates the
pages_with_figures test to the exclude_pages interface.
* Address review findings: whole-doc budget guard, job numChunks, dead code, upload cap
whole_document_context now treats a non-positive max_tokens as "never inject" instead
of injecting the whole corpus unbounded, so RAG_WHOLE_DOC_MAX_TOKENS=0 tightens rather
than disables the budget (the real off switch stays RAG_THREAD_WHOLE_DOC=0).
The job-status endpoint and get_job_status now expose num_chunks (joined from the
document), and the upload hook threads it through the SSE-fallback completion paths
(reconcile + poll). Previously a document that finished via the connection-cap fallback
had no chunk count client-side, so the re-ingest dedupe wrongly treated it as empty and
re-uploaded it. IndexJob/JobEvent gain the field and the untyped cast is dropped.
Removes the dead render_pdf_figures function (superseded by the tiling path), its test,
and the unused FIGURE_MARGIN_FRAC config knob.
Adds an upload size cap (RAG_MAX_UPLOAD_BYTES, default 200 MB; 413 on exceed with the
partial file cleaned up) so a pathological file can't drive unbounded parse + vision
work. render_pdf_figure_tiles clamps rows/cols to >= 1 (no ZeroDivisionError on a
misconfigured grid). Captioning progress is reported after OCR so the bar is monotonic.
sqlite connections set busy_timeout=5000 so a long figure/scan ingest holding its
connection doesn't make a concurrent ingest/read fail with "database is locked".
Adds tests for the non-positive budget, the zero-grid clamp, job-status num_chunks, and
the oversize-upload rejection.
* Extract PDF text as layout-aware Markdown via pymupdf4llm
parsers._pdf now extracts each PDF page as Markdown with pymupdf4llm.to_markdown
(page_chunks=True) instead of flat page.get_text("text"), so tables, headings and lists
keep their structure in the indexed chunks and retrieve far better (a table's cells stay
associated with their row instead of flattening into a token stream). Gated by
RAG_PDF_MARKDOWN (default on); falls back to plain PyMuPDF text when the toggle is off,
pymupdf4llm is missing, extraction fails, or a page yields no Markdown. The scanned-page
OCR and figure-tiling passes operate on rendered pixels and are unaffected; docx/html/txt
keep their existing extractors.
The preview-highlight locator already strips Markdown punctuation when building anchors;
it now also splits anchor tokens on pipes so a Markdown table row still anchors to the
raw PDF word stream.
Declares pymupdf4llm as a studio/RAG dependency (was only transitively present via the
data-designer plugin). Adds parser tests (Markdown table reaches the page text, the
plain-text fallback, the missing-lib fallback) and a locator test for table-pipe anchoring.
* Pin pymupdf4llm to 0.3.4 so the package scan does not pull onnxruntime
The lockstep pymupdf4llm 1.27.x line makes pymupdf-layout a hard dependency,
which in turn pulls onnxruntime (plus numpy/networkx/protobuf). The security-audit
pip scan-packages job resolves requirements --with-deps, so adding pymupdf4llm to
no-torch-runtime.txt and studio.txt surfaced onnxruntime's un-baselined CRITICAL
finding and flipped the hf-stack shard from pass to fail.
pymupdf4llm 0.3.x keeps pymupdf-layout behind an optional [layout] extra, so a plain
install resolves to pymupdf + tabulate only and never touches onnxruntime. 0.3.4
requires pymupdf>=1.27.1, satisfied by our pinned pymupdf==1.27.2.3, and to_markdown
(page_chunks=True) produces equivalent layout-aware Markdown on real PDFs (verified on
the Attention, ResNet and USDA documents). Production already installs these files
--no-deps, so onnxruntime was never shipped at runtime; this only fixes the scanner.
The parser test now asserts Markdown markup (heading or table pipes) rather than table
pipes specifically, since 0.3.4 emits a heading but not a pipe table on the tiny
borderless synthetic fixture; both markers are absent from the plain-text fallback.
* Fix RAG whole-doc review findings
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address RAG whole-doc review follow-ups
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address RAG review follow-up edge cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Reserve image budget for whole-document RAG
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: wire imatrix GGUF option and FP8/NVFP4 compressed export into the export UI
GGUF export gains an importance-matrix toggle. When enabled it auto-downloads the
upstream Unsloth imatrix for the base model (or uses a custom path), which unlocks
the IQ low-bit quants iq2_xxs, iq2_m, iq3_xxs and iq4_xs. Merged export gains an
FP8 / NVFP4 compressed-tensors precision selector that runs llm-compressor for vLLM.
Backend threads imatrix_file through routes -> orchestrator -> worker -> export_gguf
(both the local save and the hub push), and maps the new compressed format_type
values onto the fp8/nvfp4 save_method, reporting the "<dir>-<suffix>" sibling output
directory. Frontend adds the imatrix Switch on the GGUF card and a merged precision
picker on the merged card, threaded through the export runtime store.
Depends on unslothai/unsloth#6706 (save.py imatrix_file and compressed-tensors
export) and unslothai/unsloth-zoo#839 (quantize_gguf imatrix flag).
* Studio export: guard imatrix/compressed against older unsloth builds and force imatrix for IQ quants
Addresses review feedback on the export wiring:
- GGUF: pass imatrix_file only when set, so a plain no-imatrix export (e.g. Q4_K_M) no
longer fails with an unexpected-keyword error against an unsloth build that predates the
imatrix_file parameter. When imatrix is requested but unsupported, return a clear
upgrade message instead of a TypeError.
- Merged: gate FP8/NVFP4 compressed-tensors export on the installed unsloth actually
supporting it, returning a clear message rather than a cryptic save_method failure.
- Frontend: IQ quants (iq2_xxs, iq2_m, iq3_xxs, iq4_xs) are imatrix-only, so force the
imatrix on when one is selected and lock the toggle, instead of submitting an IQ quant
with no imatrix that llama.cpp would reject.
Extends the backend tests for the new capability guards and the conditional kwarg wiring.
* Studio: upload compressed merged models to the Hub without recompressing
For an FP8/NVFP4 Hub export the model is already produced locally in the "<dir>-<suffix>"
output. Uploading it directly with HfApi.upload_folder (mirroring export_base_model) avoids
re-running the expensive compressed-tensors quantization a second time inside
push_to_hub_merged, which for NVFP4 also re-runs calibration and risks OOM. Falls back to
push_to_hub_merged when there is no local compressed output to reuse.
* (feat) Add project names to studio training runs to avoid models being overwritten when doing similar training runs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update studio/frontend/src/features/export/export-page.tsx
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/frontend/src/features/export/export-page.tsx
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/frontend/src/features/export/export-page.tsx
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* better project name sanitization, removed duplicated project name normalization
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* implement checkpoint scanning utilities and tests for base model inference
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard project_name against null and use leading important modifiers
* Fix/adjust training project names for PR #6512
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix/adjust training project names for PR #6512
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address project-name review feedback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Show project names in training recents
* Keep GGUF export directories source-specific
---------
Co-authored-by: NZ-Linix <nz-linix@outlook.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: NZ-Linix <linus.ordowski@outlook.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Speed up Studio desktop startup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address Studio startup review findings
* Keep orphaned run cleanup before readiness
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Local models in the Studio Hub tab (Custom folders, LM Studio, and
Local models sections) did not reveal their on-disk path on hover,
unlike the Fine-tuned rows which already do. Each of these rows maps
over a LocalModelInfo with a required path, so pass tooltipText built
from the model name and path via a small shared localPathTooltip
helper, matching the existing FT-row tooltip format.
Refs #6382
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* cascade user message deletion to include assistant reply
* Fix comment typo in delete-thread-message
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
