Rows are picked from a searchable list of installed files instead of typed into a
table of dropdowns, and an input the node calls advanced (the trained stack) is
folded away unless something feeds it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Empty is a working state now, logged as "No active LoRAs", so the hop every
LoRA needs is already wired into a fresh pipeline.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Quantization + a per-model attention backend read from ComfyUI's own registry, so only
installed ones are offered. The LoRA loader now takes its own list and scores wrapper
prefixes against the model's key map, instead of applying a diffusers LoRA as a silent no-op.
LTXAVTEModel.load_sd passes the full checkpoint sd to Gemma3_12B.load_state_dict,
which has vision_model and multi_modal_projector as real attributes with matching
key names. Vision weights are already present after DualCLIPLoader runs.
Drop _vision_loaded flag check; detect vision capability by attribute existence.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Checkpoints converted for ComfyUI use vision_model.* and
multi_modal_projector.* as top-level keys directly, not the HuggingFace
model.vision_tower.* nesting. Accept both formats.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When no vision keys are found, log the top-level key prefixes actually
present in the checkpoint so the user can see whether the vision tower
was stripped. Also rename all [FunPackGemmaLoader] log prefixes to
[FunPackGemmaVision].
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Companion to DualCLIPLoader: takes the already-built CLIP object (which has
the correct LTXAVTEModel structure) and loads the SigLIP vision tower +
multi-modal projector from the Gemma3-12B-IT checkpoint, setting
_vision_loaded=True. Workflow: DualCLIPLoader → FunPackGemmaVision → Studio.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
FunPackGemmaLoader: drop-in replacement for Load CLIP Model that additionally
loads the SigLIP vision tower and multi-modal projector from Google's
Gemma3-12B-IT checkpoint into the already-initialized Gemma3_12B module,
enabling reference-image vision prompting without a separate CLIP Vision model.
Vision prompting: _v2_encode_prompt accepts reference_image; when the clip
has _vision_loaded=True, tokenizes with image=reference_image and
skip_template=False so Gemma3 sees the reference frame before generating
conditioning. Cache key includes image fingerprint to avoid stale hits.
source_image from Studio/refine_v2 flows through to _v2_conditioning_source.
K/V injection revamp (Technique 5): identity blocks in i2v mode now use
in-context conditioning instead of lerp injection. Pre-hook prepends reference
hidden-state tokens to vx and extends v_pe with neutral-rotation entries
(cos=1, sin=0) so video tokens self-attend to reference tokens in attn1.
Post-hook crops the reference prefix from the block output. Strength raised
from 0.02 → 1.0 (sigma-gated: active at 0.35–0.80). _extend_v_pe helper
handles 2-D/3-D/4-D PE tensors. Anchor blocks keep the existing lerp path.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Removed from LORA_BLOCK_TYPE_PROFILES, LORA_TYPE_BLOCK_ZONES,
LORA_REFINER_TYPE_PROFILES, ACTION_LORA_TYPES, the validation
special-case, and the Studio JS LORA_TYPES dropdown.
normalize_lora_type() keeps the concept->action mapping for silent
backwards compatibility with any existing saved stacks.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add LORA_TYPE_BLOCK_ZONES: type-aware block zones that suppress bad-fit
blocks to 0.0 and mildly boost good-zone blocks per LoRA type
- Add SEMANTIC_ANCHOR_BLOCKS {14, 19}: confirmed semantic focal points
(PAG default=14, STG defaults=14,19) protected from all suppression
- Add _semantic_anchor_quality: uses LoRA energy share in blocks 14+19
as a quality signal, scaling all block weights 0.92-1.08x accordingly
- Stack priority: weaker LoRA keeps base scale on anchor blocks instead
of being damped, letting the dominant signal carry semantic content
- Fix range display: status line now shows effective weights (model_weight
* scale) instead of raw multipliers; adds zeroed= and anchor_q= fields
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
