Group offload hooks report the onload device at module level while the
weights rest on cpu, so every native apply took the parameter
replacement branch in assign_weight and detached the written layers
from the hook's group bookkeeping. The activation and deactivation
walks now remove a component's group hooks before its first weight
write and reapply offload at the end of the pass: writes land in place
on the resting tensors and fresh groups snapshot the result.
- hooks come off lazily, only for components with a covered layer or a
pending backup or factor-stash restore; repeat activations with an
unchanged set leave the hooks untouched
- remove_group_offload_component follows wrapper components to the
inner model that carries the hooks
Cached networks are shared objects, and network_load overwrote their
multipliers before network_deactivate ran, so fuse-mode removal recomputed
the subtraction delta with the new values: a strength edit froze at its
first applied value and a later removal left residue in the model weights.
network_load now stages the values on the net and network_activate promotes
them, so the removal pass always subtracts the delta that was applied.
Backup mode restores from stored tensors and was unaffected.
Under a pressed balanced offload, dispatched modules hold meta tensors
whose data lives in the accelerate offload map. The factor path raised
trying to move a meta svd tensor and aborted activation mid-pass; the
legacy requantize path silently skipped those layers. Both left the
model with a partially applied network.
Rebuild the offload state with apply_balanced_offload(force) at
activate and deactivate entry: modules come back real on cpu with
hooks intact and the execution device unchanged, so both paths see
usable tensors and the next forward re-onloads under the watermark.
Network activation ran after prompt encoding, so text encoder lora
weights never affected embeds on the first generation and the stale
result was then served from the embed cache. The trailing unfiltered
activate in network_load also overrode the te exclude filter, so the
lora_apply_te setting was never honored.
- parse and activate networks in process_base before pipeline args are built
- activate_filtered gates text encoder components on per-request or global
lora_apply_te; used by base, hires, detailer and faceid call sites
- network_load accepts activate=False for callers that run their own
deactivate/activate sequence with include/exclude
- network_activate walks excluded components in restore-only mode so a
filtered text encoder reverts to backup instead of keeping stale deltas
- loaded_loras cache is single-entry since per-filter entries go stale when
the setting toggles
- prompt embed cache key includes the effective lora_apply_te value
applied_layers is cleared and re-populated on every network_activate call.
With lora_apply_te=True the second activate (TE-only pass) finds all
modules already at the target state and skips them all, leaving
applied_layers empty and breaking the restore trigger on the next gen.
native_active is set from loaded_networks at the end of activate, so it
survives idempotent re-runs and only flips false after the restore call
clears loaded_networks.
Dispatch anima loras through a dedicated native loader covering kohya,
bfl/ai-toolkit, and hybrid (bfl with alpha plus qwen3 text encoder)
formats. Cosmos 2.0 path rename is mirrored from diffusers in flat
(underscore) form so rewritten paths match network_layer_mapping keys
without further conversion.
Split model_type from cosmos to anima so a future base-cosmos2 lora
path stays separable. Update flow_models, taesd supported list, and the
taesd wanvideo bucket so samplers and preview decoding keep working
after the split.
Extend assign_network_names_to_compvis_modules to walk pipe.llm_adapter
under the lora_llm_adapter_ prefix, and add llm_adapter to
default_components so activate and deactivate include it for anima
models while staying inert elsewhere via the existing getattr guards.
CalamitousFelicitousness
Vladimir Mandic
awsr
Disty0
AI-Casanova