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>
transformer., bare-diffusers, and lora_transformer_ bases are already in
network-key form for every arch, yet each per-arch resolve_targets repeated the
same passthrough branch for them. Move that into a shared
PASSTHROUGH_PREFIXES_DEFAULT set consulted by resolve_group_targets, leaving each
arch's resolve_targets to only the prefixes it actually rewrites (kohya / BFL).
lycoris_ stays in flux2, the one arch that recognizes it.
Pure refactor: the same keys resolve to the same modules.
Add an end-to-end regression per diffusers arch (chroma, flux2, zimage, ernie)
that loads a lora_transformer_ diffusers-flat state dict and checks the
expected modules bind. The chroma suite also asserts the shared
resolve_group_targets passthrough runs above each arch's own resolve_targets,
which returns nothing for this prefix.
OneTrainer saves LoRAs against the diffusers layout, keying each module as
'lora_transformer_' + the underscore-flattened module path with QKV pre-split.
That is sdnext's own network_layer_mapping namespace, but the native loader did
not list it as a known prefix, so parse_key dropped every key and the network
loaded zero modules ("not loaded").
Add lora_transformer_ to KNOWN_PREFIXES_DEFAULT and resolve it in a shared
resolve_group_targets helper that passes the base through unchanged, with no
rename or chunking. Routing every family loader through the helper gives all
diffusers arches (chroma, flux2, zimage, ernie) OneTrainer support without
per-arch wiring.
Fixes#4877
DPMSolverMultistepScheduler implements 3rd-order sde-dpmsolver++, but the preset grid stopped at order 2 on the SDE row. Add the order-3 variant next to DPM++ SDE and DPM++ 2M SDE to complete it.
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.
Seven 'list = None' / 'list[...] = None' parameters lacked the | None
annotation required by PEP 484; ruff RUF013 flagged them in CI.
Co-Authored-By: Claude <noreply@anthropic.com>
Same except-path as the previous commit: jobid is begun before the
try block but never ended on the error return, mirroring the
xyz_grid.py fix.
Co-Authored-By: Claude <noreply@anthropic.com>
Both options inspected pp.image (the original input, normally RGB)
instead of the background-removed RGBA result, so the checkboxes did
nothing. merge_alpha also discarded the convert('RGB') return value.
Operate on the rembg output and keep the converted image.
https: //claude.ai/code/session_014QWKWgKvMevcuvfCnsYoT2
Co-Authored-By: Claude <noreply@anthropic.com>
hasattr() on the task_args dict checked for an attribute instead of a
key, so it was always False and images passed via task_args were
ignored. Use dict.get instead.
https: //claude.ai/code/session_014QWKWgKvMevcuvfCnsYoT2
Co-Authored-By: Claude <noreply@anthropic.com>
The invalid-upscaler warning referenced selected_upscaler, which is only
assigned when a matching upscaler was found, so an unknown name (stale
infotext, removed upscaler) crashed the resize instead of falling back
to plain resampling.
https: //claude.ai/code/session_014QWKWgKvMevcuvfCnsYoT2
Co-Authored-By: Claude <noreply@anthropic.com>
A stale or renamed upscaler name fell through after the debug log and
dereferenced None in upscale(), raising AttributeError. Return early
like the standard upscale class does.
https: //claude.ai/code/session_014QWKWgKvMevcuvfCnsYoT2
Co-Authored-By: Claude <noreply@anthropic.com>
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