feat(fp8): enable FP8 storage for Anima

[Pfannkuchensack]

Summary

Stacked on # — depends on the extra_skip_patterns mechanism introduced there. Merge that one first.

The fp8_storage toggle was shown for Anima main models but did nothing: AnimaCheckpointModel never called _apply_fp8_layerwise_casting. This wires it in — the state dict is already cast to a single model_dtype before load_state_dict, so the layerwise cast has one unambiguous compute dtype to restore to.

Wiring alone renders a heavily dithered image with no fine detail at all. The cause is t_embedder: it produces the adaln_lora conditioning consumed by every block, so casting it to FP8 corrupts every token everywhere. None of the generic skip patterns reach it — they target diffusers' module names (norm, pos_embed, patch_embed, proj_in/out) and this architecture names the equivalent modules differently (x_embedder, final_layer, adaln_modulation_*).

AnimaTransformer now declares _skip_layerwise_casting_patterns, the same attribute diffusers models use, so the loader needs no special-casing.

Note this is the same module as Z-Image's t_embedder, broken through a different mechanism: there diffusers read weight.dtype and cast the input to float8; here the plain precision loss is enough.

Related Issues / Discussions

Follow-up to #8945 (FP8 storage), #9231 (hook-based casting) and the Z-Image PR this is stacked on.

QA Instructions

Needs a CUDA GPU. Model Manager → an Anima main model → Default Settings → enable FP8 Storage → Save.

1. Generate. Expect in the log:

   FP8 layerwise casting enabled for anima-preview (storage=float8_e4m3fn, compute=torch.bfloat16, param_size=2012MB)
   

   and the transformer resident at ~2012MB instead of 3988MB. On main there is no FP8 line at all — that is the dead toggle this PR fixes.

2. Quality vs. bf16. Note a fixed seed, then run once with FP8 on and once off. Two gotchas that will otherwise give you a false result:

   • Disable the invocation cache (PUT /api/v1/app/invocation_cache/disable), or the second run returns the first run's image unchanged and the two look pixel-identical.
   • Anima needs 35 steps / CFG 4.5; at 9 steps / CFG 1.0 both runs look mushy and the comparison says nothing.

   Expect the same composition with slightly coarser fine structure under FP8 — not a different image, and definitely not a dithered mess. A dithered result means the skip patterns are not being applied.

3. Regression: with FP8 off, output must be unchanged from before this PR.

Measured during development, same seed/steps/CFG each run — this is what pins the skip list down:

skip list	param_size	result
none	1994 MB	dithered, no fine detail ❌
t_embedder	2010 MB	clean ✅
+ x_embedder, final_layer	2012 MB	clean ✅
+ adaln_modulation	2180 MB	clean ✅

So t_embedder is necessary and sufficient. The two I/O layers are kept as ~2MB of margin, matching what diffusers skips by default for comparable DiTs. adaln_modulation is deliberately not listed — it costs 168MB and made no difference.

Unit tests:

uv run --extra cuda --extra test pytest tests/backend/model_manager/load/test_load_default_fp8.py tests/backend/anima -q --no-cov

Merge Plan

Merge after the Z-Image PR — _apply_fp8_to_nn_module(..., extra_skip_patterns=...) does not exist without it. No DB schema, no redux slice, no API schema change otherwise.

Checklist

• The PR has a short but descriptive title, suitable for a changelog
• Tests added / updated (if applicable)
• ❗Changes to a redux slice have a corresponding migration — n/a, no redux changes
• Documentation added / updated (if applicable) — n/a
• Updated What's New copy (if doing a release after this PR) — n/a