- Remove codeformer, restoreformer, GFPGANv1.4, and GPEN-BFR ONNX
model URLs from the predefined list
- Remove the .fp16 ONNX restorer code path that bypassed detailer
processing to run face restoration directly
- 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
- Remove CodeFormer/GFPGAN import and setup from webui.py initialize()
- Remove face_restorers list, codeformer/gfpgan model path settings,
and face restore UI settings section from shared.py
- Remove restore_faces parameter from StableDiffusionProcessing
- Remove face_restoration import and restore_faces processing block
from processing.py
Remove all vendored face restoration code that is no longer maintained:
- modules/postprocess/codeformer_model.py, codeformer_arch.py, vqgan_arch.py
- modules/postprocess/gfpgan_model.py, restorer.py
- modules/face_restoration.py (base class and dispatcher)
- scripts/postprocessing_codeformer.py, postprocessing_gfpgan.py
- modules/facelib/ (vendored face detection/parsing library)
These were the only two backends registered in shared.face_restorers,
making the entire face restoration infrastructure dead code.
Nunchaku's SDXL UNet does not support offloading and raises
NotImplementedError when offload=True is passed. Skip the parameter
for SDXL and log a warning instead of crashing.
Filter out reference entries tagged "nunchaku" from Extra Networks
when the active backend is not CUDA, since Nunchaku requires NVIDIA
GPUs. Entries remain in shared.reference_models for programmatic
lookup but are not yielded to the UI.
- Rename HuggingFace org from nunchaku-tech to nunchaku-ai across all
nunchaku model repos (flux, sdxl, sana, z-image, qwen, t5)
- Add per-torch-version nunchaku version mapping instead of single global
version, with robust torch version parsing
- Add 'Fill (Nunchaku)' and 'Depth (Nunchaku)' options to Flux Tools
dropdown, loading models with +nunchaku suffix for SVDQuant quantization
- Mark Fill and Depth nunchaku reference entries as hidden so they remain
available for check_nunchaku() lookup but don't appear in Extra Networks
- Filter hidden reference models in ui_extra_networks_checkpoints
Replace manual Model/TE checkboxes in Quantization Settings with a
dedicated "Nunchaku" tab in the Extra Networks menu where users can
directly select nunchaku-quantized model variants. Detection is now
using a +nunchaku path marker for disambiguation.
- Relax sd_detect to match 'anima' without requiring 'cosmos' in name
- Use hf_hub_download for custom pipeline.py and adapter modules
- Register custom modules in sys.modules for Diffusers trust_remote_code
- Pass trust_remote_code=True to from_pretrained
- Map AnimaTextToImage to 'cosmos' model type for TAESD preview support
Anima replaces the Cosmos T5-11B text encoder with Qwen3-0.6B + a
6-layer LLM adapter and uses CONST preconditioning instead of EDM.
- Add pipelines/model_anima.py loader with dynamic import of custom
AnimaTextToImagePipeline and AnimaLLMAdapter from model repo
- Register 'Anima' pipeline in shared_items.py
- Add name-based detection in sd_detect.py
- Fix list-format _class_name handling in guess_by_diffusers()
- Wire loader in sd_models.py load_diffuser_force()
- Skip noise_pred callback injection for Anima (uses velocity instead)
- Add output_type='np' override in processing_args.py
CalamitousFelicitousness
Vladimir Mandic
awsr
Oliver Hertel
Disty0
Seunghoon Lee
Crashingalexsan