Character files that work on more than one image model. Each one carries FLUX.2 reference images
and a trained Krea 2 adapter in the same 107 MB container, so the same face renders on either
model without keeping two sets of files in sync.
File
Krea 2 adapter trained on
Train resolution
Apply strength
emmy-s500-v2.char
Krea 2 Turbo (krea2_turbo_bf16)
1024
0.5
emmy-s5.char
Krea 2 RAW (krea2_raw_bf16)
512
1.0
Same five references and same description in both, so their FLUX.2 side is identical. They differ
only in how the Krea 2 adapter was trained, and both are published so the two can be compared on
the same face.
The same character rendered from the same .char, so the differences come from the model and the
adapter rather than from the references.
Comparison 1
Comparison 2
Comparison 3
Comparison 4
Why one file holds two things
FLUX.2 klein and Krea 2 accept a character in completely different ways, so a single artifact
cannot work for both.
FLUX.2 klein has a reference channel. Reference images become tokens the model attends to at every
denoising step, which is why a prompt can address them by position. Feeding it resized copies of
the originals is enough, and no training is involved.
Krea 2 has no reference channel. Its image input is ordinary img2img: the picture is encoded once
into the starting latent and then denoised away, so there is nothing left for the model to hold on
to. The only way to give Krea 2 a persistent identity is a trained LoRA.
A .char stores identity once and compiles a payload per model family:
emmy-s500-v2.char
manifest.json payload index, fingerprints, training record
refs/000..004.png the 5 reference images, immutable
derived/face_000..004.png YuNet face crops at 512px
text/description.md the locked description, which is also the trigger
scoring/centroid_sface.json 128-d SFace identity centroid
scoring/centroid_dinov2-base.json 768-d DINOv2 subject centroid
scoring/embeds_*.json per-reference embeddings
payloads/flux2-klein/ref_000..004.png references resized onto FLUX.2's policy
payloads/krea2-lora/adapter.safetensors the trained Krea 2 LoRA, 183 MB
The references and the description are the truth. Everything under payloads/ and scoring/ is
cache and can be rebuilt from refs/. Adding a third model means adding a payload, not rebuilding
the character.
What is in them
Shared by both files:
References
5 images, three at 2048px and two at 640px
Trigger
emmy4k woman with, natural unretouched skin, black hairs, fair skin
FLUX.2 payload
flux2-klein, 5 compiled references, max 1 MP, dimensions rounded to a multiple of 16
Krea 2 payload
krea2-lora, one 183 MB adapter
Krea 2 training
emmy-s500-v2.char, trained on Turbo:
Base
krea2_turbo_bf16.safetensors (Krea 2 Turbo)
Steps
500
Rank
16
Resolution
1024
Apply strength
0.5
emmy-s5.char, trained on RAW:
Base
krea2_raw_bf16.safetensors (Krea 2 RAW)
Steps
500
Rank / alpha
16 / 16
Learning rate
1e-4, constant, Adam8bit
Resolution
512
Batch size
1
Scope
full (attention + feed-forward)
Caption dropout
0.05
Apply strength
1.0
The RAW file came first, and training on RAW was deliberate: Turbo is step-distilled, and an
adapter trained on RAW loads onto Turbo afterwards anyway. The Turbo file tests that directly by
training on Turbo itself, at 1024 rather than 512, and applying at half strength.
Every training image used the description above as its caption in both runs, so emmy4k is what
each adapter binds to.
The 500-step checkpoint beat the 800-step one on the same references. With five images that is 100
passes each, and past roughly that point the adapter starts returning the training frames instead
of the person in them.
Using it
Drop the file in models/characters/ and add a Load Character node, then wire it into the
character input of a FLUX.2 or Krea 2 node.
Inline Studio picks the payload for whichever model you wired it to. FLUX.2 gets the five
references and a prompt line naming their positions. Krea 2 gets the adapter. Either way the
description is prepended to your prompt, so type only what changes:
walking on a forest trail, half body shot
Not the description again. It is already there, and repeating it pushes the adapter harder toward
the frames it trained on.
For Krea 2, generate with Krea 2 Turbo at 8 steps and guidance 0, whichever file you use. The
RAW-trained adapter loads onto Turbo as intended, and the Turbo-trained one was built against it.
Each file carries its own apply strength, 1.0 for the RAW one and 0.5 for the Turbo one, so leave
the strength alone unless you are deliberately dialling the identity up or down.
Building your own
The full graph that produced this file, from five photos to a .char with both payloads, is
published here:
Import it into Inline Studio, swap the reference images, and run the graph. It encodes the
character, compiles the FLUX.2 references, builds a training dataset from the same references,
trains the Krea 2 LoRA, attaches it, and writes the .char.
