--- license: mit tags: - inline-studio - character - lora - flux2 - krea2 --- # Unified Face Models 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. Built and trained on the canvas in [Inline Studio](https://github.com/inlineresearch/Inline-Studio). ## Comparisons 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](1.png) | ![Comparison 2](2.png) | | ![Comparison 3](3.png) | ![Comparison 4](4.png) | ## 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: [FLUX.2 + Krea 2: multi-model portable consistent characters](https://inlinestudio.art/workflows/flux-2-krea-2-multi-model-portable-consistent-characters-training-only) 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`. ## Related Workflows - [Flux 2: Portable consistent characters, without LoRA training](https://inlinestudio.art/workflows/flux-2-portable-consistent-characters-without-lora-training) - [Flux 2 + Krea 2: Multi model portable consistent characters, Training only](https://inlinestudio.art/workflows/flux-2-krea-2-multi-model-portable-consistent-characters-training-only) - [Krea 2: Generate consistent images with unified .char model](https://inlinestudio.art/workflows/krea-2-generate-consistent-images-with-unified-char-model) - [Flux 2 Klein: Generate consistent images with unified .char model](https://inlinestudio.art/workflows/flux-2-klein-generate-consistent-images-with-unified-char-model) ## License MIT. The reference images are the character's own; check that you have the rights to any faces you train on before publishing a `.char`.