[Pfannkuchensack]

Summary

SD 3 / 3.5 LoRAs could not be used, and most installed as another model:

• diffusers and PEFT files went to the Qwen-Image probe, which shares the attention names.
• kohya files that also train both text encoders became SDXL.
• DreamBooth repos were "unknown".
• SD 3 had no LoRA loader and its denoise node did not patch.

After this PR, SD 3 LoRAs are identified with their size and applied:

• Formats:

  • diffusers, ai-toolkit and PEFT (transformer., base_model.model.), including attn2, the conv pos_embed.proj and top-level modules.
  • SimpleTuner LyCORIS (lycoris_ with flattened names, LoKR).
  • kohya sd-scripts with the original MMDiT names. The fused qkv is split into thirds, and the two adaLN projections stored with swapped halves are unswapped. Both match diffusers' single-file converter, which a drift test checks.
  • DreamBooth folders, which read the top-level weights, not checkpoint-* copies.
  • Text encoders: CLIP-L and CLIP-G parts apply to their encoders. T5 parts are dropped with a warning.
  • Layouts that cannot be applied are refused at install: DoRA, unknown layers, incomplete layers, and LoKR/LoHA on layers that must be split or swapped.

• Recognition: SD 3 is identified by its widths (1536 Medium, 2432 Large), because Qwen-Image uses the same attention names. The Qwen-Image probe now rejects SD 3 widths and CogView4's attn1. The generic SD/SDXL probe rejects kohya joint_blocks.

• Size: new optional variant (sd3_medium / sd3_large) on SD 3 Diffusers mains and SD 3 LoRAs, set at install. SD 3 Medium and 3.5 Medium share sd3_medium. The webv2 concept picker hides LoRAs of the other size when both sides record one.

• Nodes: new sd3_lora_loader and sd3_lora_collection_loader with transformer, CLIP-L and CLIP-G fields. sd3_text_encoder patches CLIP-L and CLIP-G with separate prefixes. sd3_denoise (1.2.0 → 1.3.0) refuses, before it loads the transformer:

  • a LoRA of the other size, by recorded variant or by shape;
  • a LoRA with dual attention on SD 3 Medium.

  It patches in place, or through a sidecar with FP8 Storage, and then reserves the patches.

• webv2 and docs: SD 3 joins LORA_SUPPORTED_BASES, the graph routes transformer and CLIP-L/G through the collection loader, and the size has labels and options in the model manager. The docs add a LoRA section to the SD 3 page and update the format matrix and the Generate panel page.

Related Issues / Discussions

Closes #9741

QA Instructions

Automated:

• CUDA_VISIBLE_DEVICES="" uv run --no-sync pytest -n 4 tests/backend/model_manager tests/backend/patches tests/backend/architectures tests/model_identification tests/app: 10,903 passed. The only failures were the bitsandbytes nf4/int8 tests, which need CUDA; with the GPU visible, tests/backend/model_manager/load/model_cache/torch_module_autocast passes. Each new test file also passes on its own.
• New coverage:
  • The kohya conversion matches convert_sd3_transformer_checkpoint_to_diffusers on random MMDiT weights, for Medium with attn2 and for Large.
  • The real key layouts land on the production-size transformer.
  • Probe exclusivity: each SD 3 fixture is accepted by exactly one config. The Qwen, CogView4, FLUX (incl. OneTrainer flat names), Anima and other fixtures are not taken as SD 3.
  • Stripped real files are in the identification suite: kohya Clybius/mspaint-style-sd3.5medium and a DreamBooth folder.
  • Records without a variant stay readable.
  • Denoise: the fit refusal comes before the transformer lock, and the FP8 Storage sidecar both computes the LoRA and reserves it.
  • invoke() hands CLIP-L and CLIP-G their own layers.
• ruff check/format, pnpm -C invokeai/frontend/api lint and test, pnpm -C invokeai/frontend/webv2 lint, and the webv2 generation/model tests (incl. settings/taxonomy and the concepts browser test): passed. webv1 tsc has the same 16 errors as main.
• Locally, the HF download tests fail only when HF_ENDPOINT points at a mirror that lacks the Xet endpoints; they pass without it.

Manual E2E on an RTX 4090 with SD 3.5 Medium (Diffusers), same seed with and without LoRA:

• LoRAs:

  • AdamLucek/sd3.5M-jareklfoto-LoRA-DreamBooth (diffusers)
  • bghira/sd35m-sfwbooru-lycoris (SimpleTuner LoKR)
  • jieliu/SD3.5M-FlowGRPO-PickScore (PEFT)
  • Clybius/mspaint-style-sd3.5medium (kohya, with attn2 and the swapped adaLN)
  • linoyts/Yarn_art_SD3_LoRA (diffusers with CLIP-L/G)
  • tensorart/stable-diffusion-3.5-medium-turbo (4 steps)

  Each applies visibly. Yarn with the text-encoder part differs from yarn with the transformer part only. The turbo LoRA gives a clean image at 4 steps where the base model does not.

• FP8 Storage: mspaint and yarn with CLIP.

• Logs: the server log contains no Failed to find module for LoRA layer key or Skipping LoRA layer.

• Browser (webv2): for SD 3.5 Medium, the concepts slot shows only Medium LoRAs, and a LoRA marked sd3_large is hidden. Sending a Large LoRA through a hand-built graph fails before loading with "LoRA '…' was trained for SD 3.5 Large and does not fit this SD 3 / 3.5 Medium model."

Review

The review findings are resolved:

• The speculative lora_transformer_ prefix is gone. No SD 3 file uses it, and it tied with FLUX OneTrainer files.
• Incomplete LyCORIS layers are refused at install instead of failing at conversion.
• Whether a kohya adaLN is swapped is decided by name and shape, so a LoKR on an unswapped modulation converts.
• The size refusal is readable and happens before the transformer is locked.
• override_fields is no longer mutated.
• One width-to-size helper serves the probe, the main config and the denoise.

Remaining limitations:

• OneTrainer SD 3 files that use lora_transformer_ and also train both text encoders are still taken as SDXL, as before this PR.
• A LoKR under FP8 Storage is applied in weight space and is slower than a plain LoRA.
• SD 3.5 Large is covered by meta/ground-truth tests only; no Large checkpoint was run.
• Separate default settings per SD 3 size are not part of this PR.
• The legacy UI (webv1) lists these LoRAs but does not wire them into SD 3 graphs.

Compatibility / Rollout

• Persisted configs: variant is optional and defaults to None, so stored SD 3 records stay valid and discriminator tags do not change. New installs record it. Existing mains and LoRAs get it, and misfiled LoRAs get the right base, through Re-identify in the Model Manager. Re-identify also resets a model's default settings, which the docs note. The picker and the denoise accept records without a size, and the denoise then checks the shapes.
• API: SD3VariantType joins the variant unions; the OpenAPI schema and types are regenerated.
• Merging with the ERNIE-Image ([enhancement]: LoRA support for ERNIE-Image #9739) and Ideogram 4 ([enhancement]: LoRA support for Ideogram 4 #9740) LoRA PRs: whichever merges later needs generateGraphNodeTypes.json regenerated.

Checklist

• The PR has a short but descriptive title, suitable for a changelog
• Meaningful regression coverage added / updated where needed; obsolete tests/code removed
• Persisted-state and API changes include required migrations / compatibility validation
• Relevant performance/efficiency opportunities considered; material claims have evidence
• Material review findings resolved and relevant checks rerun
• Documentation added / updated (if applicable)
• Updated What's New copy (if doing a release after this PR)

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