schedulers_sigma is a hidden OptionInfo registration; the visible control is the sampler-accordion dropdown. Its choices still listed the k-diffusion set, including polyexponential (which the diffusers backend does not support) and omitting betas, lambdas, and flowmatch. Align the list with what sd_samplers_diffusers accepts and the dropdown offers. The list feeds warn-only validation, so this clears a dead value and spurious debug logs, not user-facing behavior.
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.
Nothing sets keep_prompts on the model, so the guard never affected behavior.
JSON-caption prompts are preserved by the JSON short-circuit in
apply_curly_braces_to_prompt; the per-process p.keep_prompts path
(detailer, mixture-of-diffusers) is unchanged.
Register the subclass with generic.set_pipeline and build it with
from_pretrained, passing the SDNQ transformers and shared text encoder
while the vae, scheduler, and tokenizer load from the repo.
The dynamic-prompt brace processor in apply_styles_to_prompts strips the
{} and [] out of a JSON caption, leaving non-JSON that trips the model's
weight-baked safety placeholder. Let a model opt out of style and wildcard
processing via keep_prompts and set it for Ideogram4, then normalize the
prompt in encode_prompt: valid JSON to the compact training form, plain
text wrapped into a minimal caption so basic prompts still generate.
diffusers shipped first-party Ideogram 4 (transformer + pipeline) in
9b0818cf, so drop the in-tree port and keep only SD.Next integration glue.
Bump the diffusers pin to 9b0818cf and build diffusers' Ideogram4Pipeline
from a thin loader with per-transformer SDNQ. A small subclass keeps the
text encoder resident for the Qwen3-VL tap under balanced offload, and the
step callback denormalizes the preview latent from vae.bn before unpatchify.
Deletes the ported transformer, pipeline, scheduler, text encoder, and
latent-norm constants.
- move the text encoder on-device for the tapped forward (bypasses the offload hook)
- wrap the denoise loop in the diffusers progress bar
- live preview through the shared TAE FLUX.2 decoder (Flux.2 VAE)
Diffusers-native port of the 9.3B flow-matching DiT: dual-transformer asymmetric CFG, a 13-layer Qwen3-VL tap encoder deduped with VQA and prompt-enhance, the Flux.2 VAE, and a logit-normal schedule. Loads a published bf16 repo with SDNQ at load.
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
create_paths() pre-created every per-type output folder via fix_path(), which resolves relative values against data_path (the repo root). With a base images/grids folder configured, those bare dirs are never written to (generation uses the base-joined path), so they accumulate as empty stray folders at the repo root on every launch. Guard the bare creations behind an unset base; the resolved base+specific block already creates the real targets.
taesd_layers < 3 drops spatial upsample blocks in the TAESD/TAEHV
decoders, shrinking preview output 2x/4x. Both UIs size the live preview
from the image's intrinsic pixel dimensions (modern via object-fit:
scale-down, standard via max(naturalWidth, 512px)), so lower layer counts
rendered the preview physically small.
Rescale the decoded preview spatially by 2^(3-layers) in
sd_vae_taesd.decode. Gated to TAESD and TAEHV, the only decoders that
honor taesd_layers; TAEM1 and Hybrid VAEs decode at full size and are left
untouched. Rank-agnostic so it covers both image (CHW) and video (TCHW)
previews, including single-frame video models used for txt2img.
Vladimir Mandic
CalamitousFelicitousness
Disty0
Ryan Meador