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🎭 Regional LoRA QKV — multi-character LoRAs in ONE image. We might have actually solved it.
Perfil de ejecución
Descripción de la fuente
🎭 Regional LoRA QKV — multi-character LoRAs in ONE image. We might have actually solved it.
Two (or more) trained character LoRAs, one frame, zero identity bleed — at strength 1.0. If you've ever put two character LoRAs in the same Z-Image render and watched them melt into the same face, here's the part nobody told you: on fused-attention models, regional engines have been silently throwing away the layers that carry the identity. This pack fixes that — and adds a router so you don't even have to draw the masks.
🔬 The actual problem (and the actual fix)
Z-Image / Lumina2 (NextDiT) fuses attention QKV into a single Linear(2560 → 11520) . Character LoRAs are trained on split projections — separate to_q / to_k / to_v , each 2560 → 3840 . Those shapes don't match the fused module, so a typical regional engine's dimension gate skips them: it applies ~120 attention-out + MLP layers per character and drops the entire QKV stack — where most of the likeness lives. That's why regional identity has felt weak, and why people crank strength to 1.3–1.5 and still get mush.
Fuentes
1 fuenteExtractos de fuentes
1 extractoSource context: 103 downloads · Type Workflows · Base model ZImageTurbo
This engine composes each split LoRA into the correct output slice of the fused module — q into 0–3840, k into 3840–7680, v into 7680–11520 — as a mask-gated delta. The base forward is untouched, masking still gates tokens, and identity arrives at strength 1.0 . With diagnostics on you can watch it happen: matched 120 full + 90 fused-QKV layers .
🧭 Zero-touch placement (Auto Region)
Tier 1 — box LLM: a local Ollama vision model (default qwen3-vl:8b ) reads your prompt and returns true 2D boxes per character — big foreground face vs small figure down the hall, not just stripes.
Tier 2 — spatial language: no Ollama? "X on the left, Y on the right" is parsed deterministically, with depth handling and automatic axis choice.
Tier 3 — mention order: equal feathered bands as the last resort. Feather is a fraction of band width, so seams look identical at every aspect ratio.
Trigger words drive everything: characters whose trigger is missing from the prompt are skipped automatically, so you can batch-queue a prompt file and each prompt selects its own cast. One character active → full frame, no masking overhead.
📦 What's in the zip
ComfyUI-Regional-LoRA-QKV/ — the node pack: Define Region (one per character), Auto Region (optional router), Apply to Model . No pip installs, no extra wheels — plain Python on what ComfyUI already ships.
workflows/ZImage_Regional_QKV.json — a complete, grouped, titled two-character graph using core ComfyUI nodes only plus this pack.
README.md (full technical writeup) + INSTALL.md .
🛠 Complete install — everything you need, start to finish
ComfyUI: a current build with native Z-Image / Lumina2 support (the workflow uses only core loaders/samplers: UNETLoader , CLIPLoader in lumina2 mode, ModelSamplingAuraFlow , EmptySD3LatentImage , SamplerCustomAdvanced ).
The pack: unzip ComfyUI-Regional-LoRA-QKV into ComfyUI/custom_nodes/ , restart. Nodes appear under RIFT → Regional LoRA QKV .
Z-Image model → models/diffusion_models/ . Any Z-Image Turbo build works; tested with fasciumzImageTurbo (lastMERGE) from Civitai.
Text encoder → models/text_encoders/ : the Qwen3-4B encoder from the official Z-Image release (fp16 or an fp8 build — both fine). Load it with CLIPLoader , type lumina2 — the workflow has this preset.
VAE → models/vae/ : the standard Z-Image 16-channel VAE; tested with UltraFlux VAE Improved v10 .
Your character LoRAs → models/loras/ . Z-Image character LoRAs trained on split q/k/v (the common trainer output) are exactly what the fused composition is for; LoRAs that already match full layers compose too.
Optional but recommended — Ollama for box placement: install Ollama, then ollama pull qwen3-vl:8b . The node calls http://127.0.0.1:11434 by default and yields VRAM back before sampling ( keep_alive ). Skip this entirely and Tier 2/3 take over — nothing breaks.
Open the bundled workflow , then touch exactly four things: Character A's LoRA + trigger word, Character B's LoRA + trigger word — and write a prompt that mentions both triggers. Queue.
✅ Verify it's working (first run)
Leave diagnostics ON. The console must print, per character:
matched ~120 full + fused-QKV layers Fused QKV composition active: 30 fused module(s), equal 3-way split (out=11520 / slice=3840)
fused-QKV: 0 on a Z-Image LoRA means your trainer used q/k/v key names the matcher doesn't know yet — post the printed unresolved-target list in comments and the token rules get extended.
🎛 Settings that matter (the workflow ships with all of these)
patch_size 2 (MM-DiT: Z-Image / Lumina2 / Flux / Qwen-Image) · text_token_position: beginning · normalize_overlapping_masks: ON (overlaps blend instead of double-stacking)
strength 1.0 — the whole point; no more 1.3–1.5 compensation
feather 0.15 · sampling: euler / simple / 8 steps / shift 3.1 (the Turbo recipe)
➕ More characters, manual masks, other models
More people: chain more Define nodes — each character_pack output into the next node's input; the last one feeds Auto Region.
Manual placement: delete Auto Region, wire the last Define straight into Apply, give each Define its own real mask. (The SolidMask in the workflow is just a placeholder — Auto Region rewrites it per prompt.)
Other MM-DiT families (Flux, Qwen-Image): set patch_size accordingly; fused-QKV matching depends on that model's module naming.
⚠️ Honest limitations
Slice composition assumes equal contiguous q/k/v (standard MHA — true for Z-Image/Lumina2). GQA models are detected and logged , never mis-composed.
Masks are static per generation. Two characters is the tuned case; more works via chaining.
💜 Support
Everything here is free and stays free — the format spec, the nodes, the workflows, the cartridges, the LoRAs. If it saved you a night of debugging (it contains several hundred of mine), tips keep the 5090 warm:
☕ Buy me a coffee on Ko-fi
💛 Sponsor on GitHub
🔁 Liberapay (recurring)
⚡ Or right here: the Civitai tip button on this page sends Buzz directly.
Requisitos
character_a.safetensors
No resueltoCheckpoint · Unknown
character_b.safetensors
No resueltoCheckpoint · Unknown
MiniMax-H3-fl2va-Q3mix.gguf
No resueltoCheckpoint · Unknown
qwen_3_4b_fp8_mixed.safetensors
EncontradoText encoder · 5.25 GB · SAFETENSORS · Unknown
ultrafluxVAEImproved_v10.safetensors
EncontradoVAE · 320 MB · SAFETENSORS · Unknown
