Generalize the grounded encode to N reference images (scene + subject):
both go through the vision tower with a per-image <vision> block, spliced
and deepstack-injected at their own token positions, with multi-image
get_rope_index. Validated vs full torch Qwen3-VL — two-image grounded tap
matches to cosine 1.0, mean|Δ| 3.1e-5 (single-image unchanged). generate_edit
now feeds both image + image_b through grounding when grounding_px > 0.
Fix a ZeroDivisionError in preprocess: max_pixels is now clamped so a tiny
grounding_px (e.g. from a stale workflow whose widgets predate the
grounding_px control) can't zero the area ceiling. The example edit
workflow gains the grounding_px widget so its values align with the node.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Groundwork for image-grounded conditioning (rung B). Vendors mlx-vlm's
pure-MLX Qwen3-VL vision tower into krea2_engine/vision/ (self-contained:
inlined SDPA helper, local VisionConfig) and adds torch-free image
preprocessing (smart-resize + CLIP normalize + patch flatten).
Both validated against the PyTorch reference (tools/test_vision_tower.py):
- tower vs transformers Qwen3VLVisionModel: merged embeds + all 3 deepstack
features match to max|Δ|~2e-4, cosine 1.0 (bf16 rounding).
- preprocessing vs transformers image processor: max|Δ|~2e-7, grid exact.
Loads the visual.* weights already present in the Krea-2-Turbo text
encoder (315 tensors, 0 missing). Not yet wired into any node — the
grounded-encode integration (M-RoPE, deepstack injection, 12-layer tap)
is the next step. mlx-vlm is MIT; attribution added to NOTICE.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
