Centralize setup optimizations#1623
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…()/evict() API
Replaces the per-model `{part}_to(device)` methods across all model classes,
plus scattered call sites in dataLoader/modelSetup/modelSampler/GenericTrainer,
with generic BaseModel methods driven by the existing ModelType.model_parts()
registry: materialize(*parts), evict(*parts), and materialize_only(*parts)
(evict everything else, then materialize the given parts - the swap-in/swap-out
pattern used throughout the Samplers and text-caching setup). eval() and
adapters() are likewise made concrete on BaseModel instead of hand-written per
model. Models whose component names diverge (Wuerstchen) or that have
components outside model_parts() (SD's depth_estimator, Anima's
text_conditioner) override the relevant methods directly.
Also fixes multi-TE samplers (Flux/SD3/SDXL/HiDream/HunyuanVideo) that
previously evicted all but the first text encoder and ran encode_text with
the rest still on temp_device.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…elper into BaseModelSetup
hameerabbasi
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Jul 16, 2026
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Bunch of torch_gcs removed. They should be in evict and/or materialize.
| model.text_encoder_to(self.train_device) | ||
| model.materialize_only("text_encoder") | ||
| model.eval() | ||
| torch_gc() |
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Careful here, do the materialise functions have GC built-in?
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the meaning of materialize_only() is evict everything except this
evict() contains torch_gc()
note that this is introduced by #1617 not here
unfortunately github doesn't have an easy feature to only show the changes of PR B when it builds on top of PR A
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here is the commit that is this PR 5f2caab
hameerabbasi
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Jul 16, 2026
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Thanks; the once concern I had is resolved.
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Summary
This is another refactor PR that does not change any behaviour, but is necessary to implement future PRs without having to copy their wiring code across all ~ 15 models
Contains #1617
Test plan
pre-commit run --all-filespassesAI assistance