Surface the diffusion training API in the Images page. A "Train LoRA" button in the top
bar opens a self-contained dialog to fine-tune an SDXL LoRA on a folder of images: pick
the base model, dataset folder, output folder, an optional instance prompt, and the core
hyperparameters (steps, rank, resolution, batch, learning rate), then Start. The dialog
polls the training status while open and shows a progress bar, step count, live loss, and
the saved adapter path, with a Stop button for a clean stop.
The dialog is independent of the loaded generation model (training runs in its own
subprocess), and prefills the base model with the loaded checkpoint when it is SDXL, else
the SDXL base. api.ts gains startDiffusionTraining / stopDiffusionTraining /
getDiffusionTrainingStatus plus their types, matching the /api/train/diffusion routes.
Make the SDXL LoRA trainer reachable from the app with a small, self-contained job
service and JSON routes, deliberately separate from the LLM TrainingBackend (whose
lifecycle -- LLM config build, per-run SQLite rows, matplotlib plots, transfer-to-chat-
inference -- is text-training specific and would mis-handle a diffusion run).
core/training/diffusion_training_service.py: DiffusionTrainingService runs one job at a
time -- validate the config cheaply (before any spawn), spawn the trainer subprocess
(spawn context, parent-lifetime bound), pump its events (model_load_* / progress /
complete / error) into an in-memory status snapshot, and support a clean stop. The
subprocess context and target are injectable so the full start -> pump -> status ->
complete path is unit-tested without real multiprocessing or torch.
routes/training.py: POST /api/train/diffusion/start (400 on a bad config, 409 when a job
is already running), POST /api/train/diffusion/stop, GET /api/train/diffusion/status
(JSON poll). models/training.py: DiffusionTrainingStartRequest + response schemas
mirroring DiffusionLoraConfig, so model_dump() passes straight through.
Tests: test_diffusion_training.py -- service happy path, bad-config-before-spawn,
concurrent-job rejection, clean stop, crash-without-terminal-event, event transitions;
plus route wiring via the FastAPI TestClient (start / 422 / 400 / 409 / status / stop)
with a mocked service. The diffusion trainer's progress events already use the field
names this path expects.
The Studio training pump reads 'learning_rate' from progress events; the diffusion
trainer emitted 'lr'. Rename the field (and the CLI reader) so the trainer's events are
directly consumable by the existing training status/SSE machinery when it is wired into
the worker, without a translation shim.
First diffusion training path in Studio: train a LoRA on the SDXL U-Net from an
image + caption dataset and export it as a diffusers .safetensors that the existing
diffusion LoRA loader (and any diffusers pipeline) can load.
core/training/diffusion_lora_trainer.py:
- DiffusionLoraConfig with validation/defaults (rank, alpha, targets, lr, steps, grad
accumulation, resolution, min-SNR gamma, gradient checkpointing, lr scheduler, seed,
mixed precision).
- discover_image_caption_pairs: captions from metadata.jsonl / captions.jsonl, per-image
.txt/.caption sidecars, or a dreambooth instance_prompt fallback (pure, unit-tested).
- run_diffusion_lora_training: the loop -- freeze base, PEFT-wrap the U-Net attention
projections, VAE-encode (fp32 VAE to avoid the SDXL fp16 overflow), sample noise +
timesteps, predict, MSE loss with optional min-SNR weighting (epsilon / v-prediction),
AdamW + get_scheduler + grad accumulation + grad clipping, then export via
save_lora_weights. Emits worker-protocol events (model_load_*, progress, complete) and
polls should_stop for a clean stop with a partial save.
- run_diffusion_training_process: mp.Queue subprocess adapter (event_queue / stop_queue),
so the training worker can spawn it; plus a CLI entry point.
Only SDXL (U-Net) is trained here; DiT families and the Studio UI form + route wiring are
follow-ups. The trainer is decoupled and worker-ready.
Tests: test_diffusion_lora_trainer.py covers caption discovery (metadata / sidecar /
instance prompt / skip-uncaptioned / errors), config normalisation + validation, the SDXL
add-time-ids, and the dict->config adapter. Verified live on GPU: a 60-step SDXL LoRA run
lowers the loss, exports a ~45 MB adapter, and loading it back shifts generation from
baseline (mean abs pixel diff ~55/255).
The backend has always accepted a bare Hugging Face repo id (owner/name, or
owner/name:weight-file.safetensors) as a LoRA, downloading and applying it. But the
picker only rendered when the curated catalog had entries, and the catalog is empty,
so there was no UI path to apply any LoRA. Show the LoRA section whenever the loaded
model supports LoRA, and replace the curated-only dropdown with a text input: type a
Hub repo id, or pick a discovered adapter from a datalist of suggestions when the
catalog is populated.
Also restore LoRAs when loading a recipe. restoreSettings now parses the recipe's
"id:weight" strings (splitting on the last colon, since the id itself may contain one
for a specific weight file) back into the selection, so replaying a saved image
reproduces its adapters. The generate payload trims hand-typed ids and drops empty /
zero-weight rows, and a model swap clears the selection (a LoRA is family-specific)
without discarding a free-text pick that is not in the curated list.
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.
When a repo id resolves to no diffusion family the load raised 'Could not infer a
diffusion family... Pass family_override (z-image)', which points at an unrelated
family and doesn't say what is supported. Replace it with a message that lists the
supported families (from a new supported_family_names helper) and notes that video
models and image models whose diffusers transformer has no single-file loader are
not supported. Applies to both the diffusers and native sd.cpp load paths. Also
refreshes two stale family-registry comments that still called FLUX.2-dev omitted.
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 chat picker treats a cached repo as an image model, and hides it, only
when it ships a diffusers model_index.json. Single-file, ComfyUI, and
ControlNet image checkpoints (an FP8 Qwen-Image, a z-image safetensors, a
Qwen-Image ControlNet) carry none, so they surfaced as loadable chat models.
Fall back to resolving the repo id against the known diffusion families, the
same resolver the Images backend loads from, so these checkpoints are tagged
text-to-image and stay in the Images picker only.
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.
* MLX CI: find llama-cli where save_pretrained_gguf actually installs it
The GGUF reload step hardcoded the CWD-relative paths llama.cpp/llama-cli and
llama.cpp/build/bin/llama-cli, but save_pretrained_gguf builds and installs llama.cpp
under unsloth_zoo's LLAMA_CPP_DEFAULT_DIR ($UNSLOTH_LLAMA_CPP_PATH, else
~/.unsloth/llama.cpp), so the reload could not find the binary and failed the Mac M1
job with "llama-cli not found". _find_llama_cli now searches that install directory
(and honors the env override) before falling back to the old CWD layout, with a
recursive glob as a last resort. The search is a strict superset of the previous
paths, so it cannot regress a layout that already worked.
* MLX CI: return an absolute llama-cli path from the locator
Resolve the located binary to an absolute path. If UNSLOTH_LLAMA_CPP_PATH is a
relative directory (e.g. "."), Path(".") / "llama-cli" normalizes to the bare name
"llama-cli", and subprocess.run treats a separator-less argument as a PATH lookup
rather than a file to execute, raising FileNotFoundError. resolve() makes the returned
path absolute so it always runs the intended binary.
* MLX CI: give llama-cli EOF on stdin so GGUF reload cannot hang
With the binary now found, the GGUF reload actually invokes llama-cli and it timed
out after 300s generating 24 tokens on a 270m model, which is a stdin block rather
than slow generation: subprocess.run captured stdout/stderr but left stdin inherited,
so -no-cnv still left llama-cli waiting for interactive input. Pass
stdin=subprocess.DEVNULL so it receives an immediate EOF and runs the single prompt to
completion.
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
The LoRA alias is used as the diffusers PEFT adapter name, and PEFT rejects names
containing "." (module name can't contain "."). sanitize_alias kept dots, so a LoRA whose
filename carries a version tag (e.g. Qwen-Image-2512-Lightning-8steps-V1.0-bf16) failed to
apply with a 400. Replace dots too; the alias stays a valid native <lora:NAME:w> filename
stem. Adds regression coverage for internal dots.
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.
Condense the verbose comments and docstrings added by the recent
chat template, GPT-OSS detection, PEFT tensor-parallel, and Studio
inference proxy fixes. Comments and whitespace only; no code changes.
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.
check_js_file put the bundle's KB size inside the finding's check label, which is
part of the baseline match key (package, file, check). When tensorboard's
projector_binary.js grew from 1918 KB to 1933 KB, the reviewed baseline entry stopped
matching and the benign HIGH resurfaced, red-failing the studio and extras
scan-packages shards. Move the size into the evidence field (shown for review, not
matched) and keep the check label constant, then update the one tensorboard baseline
entry to the size-agnostic label. The finding is suppressed again and will not
re-break when the bundle grows by a few KB. Scanner self-tests pass unchanged.
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
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.
Adds the opt-in speed path for the GGUF diffusion transformer behind a
selectable speed mode (default off, so output is unchanged until a profile
is chosen):
- diffusion_eager_patches.py: shared eager fast-paths (channels_last,
attention/backend selection, fused norms and QKV) installed at load and
rolled back on unload or failed load.
- diffusion_compile_cache.py / diffusion_gguf_compile.py: a persistent
torch.compile cache and the GGUF-transformer compile wiring.
- diffusion_arch_patches.py: architecture-specific patches.
- diffusion_patch_backend.py: shared install/restore plumbing.
- diffusion_speed.py: speed-profile planning.
Tests for each module plus the benchmarking and probe scripts used to
measure speed, memory, and accuracy of the path.
* 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>
