Reference Latent+

Stock ComfyUI has a ReferenceLatent node, and it's about as thin as a node gets: drop a latent in, it tacks it onto your conditioning under the reference_latents key, done. Binary. Whole image, every step, no strength knob. The moment you want "that character's face but not their pose, background, and crop," it's useless - you're back to LoRA training or a ControlNet that fights your composition.

Reference Latent+ is the version the stock node never grew up to be. It's a drop-in replacement by shootthesound (Peter Neill, the same person behind the Fizgig Klein LoRA workbench and the LoRA block analyzer - a known quantity on r/StableDiffusion) that gives you per-image strength, per-image timestep gating, auto-masks, and up to four references in a single node. If you're doing reference-based character or style transfer on Flux, Flux2/Klein, Wan, Hunyuan, Qwen-Image, Z-Image, or Lumina2, this is the node you reach for.

How it works

Same trick as stock ReferenceLatent, one layer deeper. The node VAE-encodes your images internally (yes, there's a vae input, so no more hand-wiring LoadImage → VAE Encode → ReferenceLatent chains) and attaches them to your conditioning's reference_latents key, which those models read directly. Two things make it smart:

• Per-image timestep gating. Each ref gets its own start_percent / end_percent. The node partitions the denoising schedule so every timestep has exactly one set of active refs - no double-counting. This is the killer feature on Flux2/Klein: style and identity live in the clean, late timesteps, so gating a ref to 0.6–1.0 lets your prompt's composition run free early and only adds the ref's look at the end.
• Megapixel cap. max_megapixels (default 1.0) downscales each ref by area before encoding, aspect preserved, never upscaled. Reference latents cost quadratic attention, so a 2K ref photo will wreck your generation speed; capping keeps it proportional to a 1024² render.

The inputs that matter

Only four required: conditioning, vae, image_1, plus the max_megapixels and mask_fill_mode globals. Connect image_1 and an image_2 slot appears; connect that and image_3 shows up - progressive disclosure, up to four refs.

• image1_strength - signed, -5.0 to 50.0, default 0.85. 1.0 equals stock behavior; 0 bypasses that image entirely; negative is the experimental "anti-reference" that pushes the model away from that ref.
• image1_face / hair / body / clothes / background - MediaPipe auto-mask regions. Tick "face" and only the face gets encoded as a ref. No more background leaking into your output. grow, feather, and ignore_area refine the mask.
• image1_start_percent / end_percent - that timestep gate. Defaults to 0→1 (always on).

Output is a single conditioning tensor - wire it into your KSampler's positive input and you're done.

Installing it

Via ComfyUI Manager, search "Reference Latent+" (pack title comfyui-ReferenceLatentPlus). Or the manual way:

cd ComfyUI/custom_nodes
git clone https://github.com/shootthesound/comfyui-ReferenceLatentPlus

Then restart ComfyUI. It appears under advanced/conditioning > Reference Latent+. The MediaPipe auto-mask feature is optional: pip install mediapipe into your ComfyUI venv and the selfie_multiclass model auto-downloads to models/mediapipe/ on first use. There's a ready Klein 9B example workflow in examples/.

Where people get burned

• No MediaPipe = silent no-op. Ticking a region does nothing if the package isn't installed - no error, the ref just isn't masked. Explicit mask_N wiring still works regardless.
• latent_zero and latent_noise are off-distribution. No model trained on masked reference latents; at high mask coverage behavior is exploratory. pixel_grey (the default) stays in-distribution - treat it as the safe option.
• Negative strength is experimental. Great for "make it not look like this," unpredictable otherwise.
• reference_latents_method is Flux-only. Other models ignore it harmlessly, so don't sweat the key not mattering on Wan.

The masking-of-ref-latents idea is genuinely novel - nobody else in the ecosystem does it, which means there's no community-validated baseline. Test the defaults on your own use case, then fiddle. That's the whole point of the node.