Comment-only pass over the Python this PR touches: drop what the code already
says, collapse multi-line explanations that still read on one line, and keep
the reasoning that is not recoverable from the code. No code, docstring
semantics or behaviour changes; verified with an AST comparison against the
previous revision, and the backend suite is unchanged (same 37 environment
failures as before: the API integration tests that need a live keyed server,
the flash-attn install hooks, and the GPU memory fields).
The generic quantize_text_encoders pass only covers text_encoder.._3, so
HiDream's HEAVIEST encoder (Llama-3.1-8B TE4, 16.1 GB bf16) always stayed
dense. TE4 is assembled separately (hidream_te4_kwargs), so the fp8 path
now lives there: when the requested TE quant is layerwise fp8 and the
device/family qualify, TE4 prefers the hosted pre-cast checkpoint
(unsloth/HiDream-I1-Full-FP8, 8.6 GB) and falls back to dense-load-then-
cast; a mid-pass cast failure reloads a fresh dense encoder instead of
shipping partial state. The pre-cast loader and builder gain
config_subfolder/config_overrides for standalone encoder repos whose
config sits at the root and whose pipeline needs forward flags
(output_hidden_states/attentions).
Verified on B200: bit-identity 291 tensors (225 fp8, 0 mismatches),
hosted checkpoint engages through the real backend (marker + status fp8),
load 24.3 s vs 48.0 s dense, LPIPS 0.133 mean over 3 same-seed pairs vs
the dense-TE render (gate 0.25), non-black frames.
A 17B MoE DiT (16 double + 32 single layers, 4 routed experts) with four text
encoders, on HiDreamImagePipeline (diffusers 0.39). One family covers the open
Full / Dev / Fast repos (same arch); per-variant generation defaults follow the
upstream inference recipes (Full 50 steps at guidance 5, the distilled Dev 28
and Fast 16 guidance-free).
The repos name a Llama-3.1-8B text_encoder_4 in their model_index but do not
ship its weights; the official example passes the gated meta-llama repo in by
hand. The loader instead assembles the component from the open unsloth mirror
(byte-identical weights, already inside the non-GGUF trust gate), injected at
the three pipeline from_pretrained sites, with output_hidden_states matching
the official example. Memory planning counts the assembled TE4: 34.2 GB DiT +
28.8 GB encoders, ~63 GB bf16-resident.
