v2 Model Module Restructure — Planning

Goal

Every component the toolkit loads (DiTs/transformers, unets, text encoders, vision encoders, VAEs, audio VAEs) becomes a class extending one base module in toolkit/models/v2. BaseModel (toolkit/models/base_model.py) stays as the multimodal holder that each arch in extensions_built_in/diffusion_models extends — that layer is good. The layer below it is what gets unified: one loading entry point, one quantization path, one save path, shared component definitions instead of per-model-folder copies.

End state this enables:

Live server with model hot-swap: a resident process where, when a generation or training run requests a different model, the unused components are dropped and the new ones loaded. Shared component classes (same TE/VAE reused across archs) make component-level reuse possible instead of full teardown/reload.
Model loading test suite: a test that loads each registered arch one at a time and runs an inference pass. Every model type gets added to this suite as it is migrated (see Testing below).
Comfy-aligned weights: weights live in the ComfyUI folder layout under MODELS_PATH (shareable with a ComfyUI install), download there when missing, and saves are comfy-format. Eventually defaults move to comfy / our own prequantized releases for everything.

Current state (survey 2026-08-27)

Three generations of loading conventions coexist:

Legacy monolith toolkit/stable_diffusion_model.py (is_flux / is_v3 branches); still the silent fallback in toolkit/util/get_model.py when an arch string doesn't match.
BaseModel subclass per arch (35 registered classes), each with a hand-written load_model() / save_model().
toolkit/models/v2/_mixin.py (OstrisModelMixin) — the intended fix, currently used by one model (v2/z_image.py → z_image extension).

Duplication highlights

BFL KL autoencoder: full copies in flux2/src/autoencoder.py and ideogram4/src/vae.py (header says "Flux2 KL autoencoder").
Qwen3-VL text encoder loaded independently in qwen_image, nucleus_image, krea2, ideogram4, mageflow, minimax_h3 — from several different repo sources; krea2 hand-patches the vision tower locally.
Qwen3ForCausalLM TE load: 3 verbatim line-for-line copies (z_image/z_image.py:257, z_image/z_image_l2p_model.py:471, zeta_chroma/zeta_chroma_model.py:146).
Flux1 VAE + T5 + CLIP trio loaded 4x from 2 different repos (chroma ×2, flux_kontext, legacy SD path).
Comfy-file resolver copy-pasted: minimax_h3/minimax_h3.py:248 (_resolve_comfy_file) → ltx2/ltx2.py:1254 ("mirrors MinimaxH3Model").
AutoencoderKLQwenImage latents mean/std handling triplicated (qwen_image, nucleus_image, krea2).
transformer. ↔ diffusion_model. LoRA key rename copy-pasted ~15x in convert_lora_weights_before_save/load overrides.
Fake CLIP/TE/config stubs redefined in ~5 places (canonical: toolkit/models/FakeVAE.py, toolkit/unloader.py).

Inconsistency highlights

Quantization, 5 paths: quantize_model() (block-streaming, ARA-aware — ~21 users), raw quantize() (~10 users, no block streaming/excludes, but the only path honoring quantize_kwargs), hidream's hand-rolled block loop, the v2 mixin's own quantize_, and bare TE quantization everywhere.
Known bugs: ~9 sites quantize the TE with qtype instead of qtype_te (chroma ×2, flux2, flux_kontext, cogview4, wan21, legacy SD, ...); toolkit/models/loaders/umt5.py accepts a comfy_files param it never uses, so wan21's comfy-TE path is a silent no-op.
Saving, 4 incompatible styles: diffusers save_pretrained folders, flat safetensors, safetensors-inside-diffusers-folder hybrids, and z_image's loaded-format-dependent branch. Dequant-on-save done 3 ways; the isinstance(v, QTensor) variant (chroma, flux2, boogu_image, ideogram4) misses torchao and Ostris weights entirely. Every save_pretrained override ignores its save_dtype argument.
Registry: linear scan in toolkit/util/get_model.py, silent SD1 fallback on a typo'd arch, eager import of every model file at startup. Second unsynchronized registry in ui/src/app/jobs/new/options.tsx.

Decisions (locked in)

Save format = ComfyUI format. Single-file safetensors in comfy key layout. Must support saving quantized — primarily convrot8 and nvfp4 (comfy_quant marker format, see toolkit/util/comfy_quant_import.py) — and plain bf16, all in comfy format. Loading stays backwards compatible: diffusers dirs, transformers repos, and single files all still digest through load_model; only saving standardizes on comfy.

v2 folder layout mirrors the comfy save path structure:

toolkit/models/v2/
  _mixin.py            # base module (OstrisModelMixin, evolving)
  resolver.py          # comfy-layout weight resolution (lift from minimax_h3)
  diffusion_models/    # one file per DiT/unet family
  text_encoders/       # qwen3_vl.py, qwen3.py, t5.py, clip.py, gemma.py, ...
  vae/                 # flux_kl.py, qwen_image.py, wan.py, audio VAEs, ...
  vision_encoders/

Method names win from BaseModel: get_transformer_block_names and get_quantization_exclude_modules. The mixin's get_quantization_block_names gets renamed to match; resolve the classmethod-vs-instance-method mismatch while doing so.
Loading policy:
- Per-model special handling is allowed via the hook methods.
- If name_or_path is a diffusers/transformers source, load it with diffusers/transformers for now. Step 1 is migrating every model to the v2 module format and loader without breaking anything — same weights, same sources, same results.
- Each model declares a comfy_weight_names dict keyed per standard name_or_path. If the user points at a local folder or a non-standard repo, load it as-is. If name_or_path is the standard repo and we have matching comfy weight names, load those instead when any of them exist (locally under MODELS_PATH in comfy layout, or downloadable to there).
- Eventually the default flips to comfy weights / our own prequantized releases for everything.

Base module: what `OstrisModelMixin` still needs

The mixin already handles: diffusers dir / hub repo / local single file / org/repo/file.safetensors, key-conversion hooks on load and save, overridable backend hooks for transformers-lib models, block-wise quantize.

To add:

Comfy weight spec + resolver. aitk_comfy_repo / aitk_comfy_weight_names class attrs + find_comfy_weights (local-only until Phase 2); resolution chain generalized from minimax_h3._resolve_comfy_file into v2/resolver.py: explicit override → MODELS_PATH at the repo-relative comfy path → flat at root → recursive walk of the category folder → hub download to the repo-relative path (folder stays shareable with ComfyUI, no duplicate downloads).
Automatic prequantized import. Single-file path sniffs comfy_quant markers and routes through import_comfy_quantized_layers before load_state_dict, including the OstrisLinear missing-key whitelist that minimax_h3 and ltx2 each hand-rolled.
One save path. save_model(path, dtype): dequantize via dequantize_if_quantized (honors dtype), run convert_state_dict_on_save, write single-file comfy-layout safetensors. (Quantized-storage saves — convrot8 / nvfp4 with comfy_quant markers — land with Phase 2; diffusers-folder save as an explicit flag still to add.)
Tokenizer/processor declaration for text encoders (aitk_tokenizer_repo/aitk_processor_repo + load_tokenizer/load_processor).
Rename quantization hooks to the BaseModel spellings (decision 3): get_transformer_block_names (classmethod on the module).

Migration steps

Track progress here; check items off as they land.

Phase 0 — foundation (done 2026-08-27)

Evolve _mixin.py per the list above (comfy spec local-only until Phase 2)
Create v2/diffusion_models/, v2/text_encoders/, v2/vae/, v2/vision_encoders/; move v2/z_image.py → v2/diffusion_models/z_image.py
Lift the comfy resolver out of minimax_h3 into v2/resolver.py; point minimax_h3 and ltx2 at it (delete their copies)
BaseModel default convert_lora_weights_before_save/load doing the transformer. ↔ diffusion_model. rename, gated on the class attr lora_keys_use_comfy_prefix (default False, so passthrough models keep their behavior); the ~18 identical overrides replaced with the flag. Custom conversions (anima, hidream_o1, ltx2, wan21) keep their overrides; ltx2's now composes with the flag via super().

Phase 1 — migrate all models to v2 modules, no behavior change

Every arch's components become v2 classes; if name_or_path is diffusers, it still loads via diffusers. Nothing about sources or outputs changes yet. Suggested order (worst duplication first), each including its loading test (see Testing):

text_encoders/qwen3.py — Qwen3TextEncoder + OstrisTransformersMixin backend + BaseModel.prepare_text_encoder policy helper; the 3 verbatim TE stanzas (z_image, z_image_l2p, zeta_chroma) replaced. Verified with real Z-Image weights (load + encode on GPU).
text_encoders/qwen3_vl.py — Qwen3VLTextEncoder with drop_vision_tower / patch_vision_patch_embed; the 4 identical patch_qwen_vl_patch_embed copies (krea2, mageflow, boogu_image, Qwen3VLCaptioner) consolidated; TE loads migrated in krea2, mageflow, nucleus_image. Still on their own paths: ideogram4 (loads via AutoModel), minimax_h3 (custom truncated/prequantized comfy load — port later), qwen_image (Qwen2.5-VL, needs its own class)
text_encoders/t5.py, text_encoders/clip.py — T5TextEncoder, CLIPTextEncoder, CLIPTextEncoderWithProjection; migrated chroma ×2, flux_kontext, f_light (T5 stanzas → prepare_text_encoder, fixing their qtype → qtype_te bug) and hidream (CLIP ×2 + T5 with subfolder overrides; slow-tokenizer classes preserved via use_fast=False)
vae/qwen_image.py — QwenImageVAE + QwenImageVAEHolderMixin (frame-dim + latents mean/std handling built in, tiling opt-in via vae_decode_tiled_on_low_vram); the triplicated encode/decode deleted from qwen_image, nucleus_image, krea2 and all three VAE loads routed through the v2 loader
vae/autoencoder_kl.py — KLVAE (diffusers AutoencoderKL through the universal loader); migrated the scattered loads in chroma, flux_kontext, f_light, hidream, z_image
vae/flux2_kl.py — the BFL-style Flux2 KL autoencoder unified from the flux2 + ideogram4 copies (both files deleted; flux2's encode/decode/small-decoder superset + ideogram4's diffusers key converter). Verified bit-identical to both originals (weights, encode/ decode outputs, converter mapping) and round-tripped real ae.safetensors weights on GPU. Packing/normalization stays per-model — flux2 packs (c pi pj) with BatchNorm running stats, ideogram4 packs (ph pw c) with its latent_norm tables; the conventions are incompatible.
z_image — transformer, TE (qwen3), and VAE (KLVAE) all on v2 modules. z_image_l2p still has its local progressive-transformer subclass (rebasing it onto the v2 class deferred; its TE is migrated)
qwen_image family — v2/diffusion_models/qwen_image.py (single-file loads stay on diffusers' from_single_file until the comfy flip) + v2/text_encoders/qwen25_vl.py (slow tokenizer preserved); edit variants inherit
nucleus_image — v2/diffusion_models/nucleus_image.py, TE stanza collapsed to prepare_text_encoder
krea2, ideogram4, mageflow — TE/VAE migrated; their custom local DiT classes still to be rebased onto the mixin
chroma, chroma_radiance — both vendored Chroma classes now carry OstrisModelMixin with the block-count sniff moved into a new aitk_config_from_state_dict hook (mixin now supports checkpoint-derived configs + load_from_state_dict for non-safetensors sources, used by radiance's .pth path). zeta_chroma transformer left as-is: its config depends on holder state (patch_size), not the checkpoint
flux_kontext — v2/diffusion_models/flux.py (FluxTransformer2DModel); whole model now loads through v2 (transformer, T5, CLIP, KLVAE)
flux2 — TE/VAE partially migrated (flux2_kl); custom DiT still local. krea2/mageflow/ideogram4/zeta_chroma DiTs stay model-specific: their configs come from model_kwargs / holder state, so the mixin adds nothing until the comfy-weights flip (Phase 2)
minimax_h3 (+ ref2va), ltx2 family — already on the shared resolver + comfy_quant_import; the full mixin port waits for Phase 2, when the mixin's single-file precision policy (stored-precision loading, fp32-key protection) is settled to match their deliberate behavior
wan21 / wan22 family — v2/diffusion_models/wan.py (WanTransformer3DModel, both wan22 dual loads included) + v2/text_encoders/umt5.py (UMT5TextEncoder + PatchedT5Tokenizer; loaders/umt5.py is now a thin compat shim, comfy_files still reserved for Phase 2 — no local comfy umt5 file to verify the key conversion against). wan21's TE qtype → qtype_te bug fixed via prepare_text_encoder
hidream family — vendored transformer carries the mixin; v2/diffusion_models/hidream.py wraps the diffusers class for hidream_e1; both load via the switchable hidream_transformer_class through load_model
omnigen2 — vendored transformer carries the mixin, load migrated
boogu_image, ernie_image, prx_pixel_t2i — their vendored diffusers-style DiT classes now carry OstrisModelMixin (subfolder + block names on the class) and the holders load via load_model
f_light — DiT class carries the mixin (aitk_subfolder="dit_model"), load migrated
anima — loads through diffusers modular pipelines (AnimaModularPipeline); not a mixin fit, revisit at Phase 2
flux2 DiT — holder-config params classes (Flux2/Klein variants), defer like krea2/mageflow
ace_step — one bundled safetensors holds model+TE+VAE+tokenizer via its own load_models; decomposing into v2 components is its own task
Per-model fixes folded in as each migrates: qtype_te bug, dequant-on-save (dequantize_if_quantized everywhere), raw-quantize() → quantize_model()

Phase 2 — comfy weights become the preferred source

Wire comfy_weight_names per model; standard-repo name_or_path + existing comfy weights → load comfy
Comfy-format save (bf16 + convrot8/nvfp4 quantized) as the default full-weight save
Publish/verify comfy repacks per model as they flip

Phase 3 — live server

Component-level identity (which TE/VAE instances are shared between archs) so a model switch drops only what the next run doesn't need
Resident process: request comes in → diff requested components vs loaded → unload/load the difference
Legacy stable_diffusion_model.py archs: grandfather or port last

Testing

testing/test_model_loading.py: per-arch load + one small sample through the normal training-style flow (get_model_class → load_model → generate_images). --arch X runs one in-process; --all runs every registered arch in its own subprocess (full unload between archs). 15 archs registered so far — add each model type as it migrates.
Missing weights skip rather than fail: default is HF_HUB_OFFLINE=1 and hub/file errors classify as SKIP; --allow-download opts into fetching. (GPU + local-weights test, not CI-portable.)
Full sweep run 2026-08-27: 14/15 PASS (zimage, qwen_image, krea2, boogu_image, ernie_image, ideogram4, hidream_o1, anima, wan21, wan22_5b, chroma, flux_kontext, flux2_klein_4b, ltx2.3 — the quantized 22B ltx stack doesn't fit 32GB, needs the 96GB card). mageflow blocked upstream: microsoft/Mage-Flow-Base 404s on the hub (cached locally, so it runs offline — recheck whether the repo moved/went private).
Registry carries realistic per-arch sample settings (native res, steps, CFG) so sweep outputs are visually verifiable, not just "a file exists". Verified: all 14 produce proper generations. Findings from the quality pass: boogu emits a black frame below native res at low-step/high-CFG (settings regime, present pre-restructure, not a migration bug); chroma's FakeCLIP hardcoded device 'cuda' broke any non-cuda:0 run (pre-existing, fixed — FakeCLIP now takes the real device); ideogram4's fp8 release renders its own "blocked by safety filter" card for a plain cat prompt (model behavior, not a bug — investigate its trigger).
Round-trip test per model: load → save comfy format → reload from the save → outputs match (bf16) / load cleanly (quantized saves). Lands with the Phase 2 comfy save path.
Each newly migrated model adds its test in the same PR as its migration.

TODO / look at later

Quantize-path consolidation quirks: quantize_kwargs is honored only by the raw quantize() call sites and silently dropped by quantize_model(); the ARA path inside quantize_model hardcodes uint8. Decide the unified behavior when consolidating.
toolkit/models/loaders/umt5.py dead comfy_files param (wan21 comfy-TE no-op) — fix when wan migrates.
Registry hardening: error (don't fall back to SD1) on unknown arch; lazy per-arch imports; single source of truth shared with the UI's options.tsx model list.
Fake/stub components: consolidate on toolkit/models/FakeVAE.py / toolkit/unloader.py, delete local copies.
Vendored upstream code (hidream/src, omnigen2/src, ltx2 converter's private comfy-quant parser): dedupe against toolkit utils where practical.

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