Important: The Hugging Face auto-generated Diffusers snippet and ordinary
style-LoRA workflows are not compatible with this functional adapter. For
Python, use the included example.py and pipeline.py. For ComfyUI, use the
included official workflow with the required Ostris custom nodes and keep
reference K/V caching enabled.
This rank-32 LoRA uses one image as an identity reference while allowing the
prompt to change clothing, pose, composition, and background. It works with
both photorealistic and illustrated subjects. The adapter was trained against
Krea 2 Raw and is intended for distilled 8-step inference with Krea 2 Turbo.
The adapter is deliberately different from a reconstruction or general edit
model. Its goal is to retain stable identity features without copying every
source-image detail. A close face or head-and-shoulders crop usually gives the
prompt more freedom than a full-body reference.
The included pipeline is the pinned
ostris/Krea2OstrisEdit
reference pipeline used by this adapter.
Files
text
1krea2_reid_rank32.safetensors LoRA weights
2pipeline.py Pinned Krea 2 reference pipeline
3face_crop.py Optional YuNet reference-crop helper
4models/face_detection_*.onnx Bundled YuNet INT8 detector
5example.py Minimal local example
6LICENSE.pdf Krea 2 Community License
7PIPELINE_LICENSE Apache-2.0 license for pipeline/example code
8COMFYUI_WORKFLOW_LICENSE MIT license for the adapted ComfyUI graph
9YUNET_LICENSE MIT license for YuNet
10NOTICE attribution and derivative-work notice
11SHA256SUMS release artifact hashes
12showcase/ fully synthetic reference/output examples
13workflow/krea2_reid_comfyui.json Official ComfyUI workflow
Tested capabilities
This release has been tested on:
photorealistic and illustrated subjects;
close portrait references driving three-quarter and full-body outputs;
square references driving portrait outputs;
substantial clothing, pose, framing, lighting, and background changes;
distilled 8-step Krea 2 Turbo inference with reference K/V caching.
All people and characters shown below are fully synthetic images generated for
this release. They do not depict a real person or an existing character IP.
Each source and result is the first 8-step output from its preselected prompt
and fixed seed (17072026 through 17072032); no rerolls or weaker candidates
were removed from these showcase groups.
Photorealistic identity across clothing and scene changes
Synthetic reference
Red suit / glass gallery
Winter coat / city street
Synthetic photorealistic reference
ReID result in a red suit
ReID result in a winter coat
Illustrated identity across costume and composition changes
Synthetic reference
White-and-gold dress / conservatory
Synthetic illustrated reference
ReID illustrated result
Stylized 3D identity across framing and environment changes
Synthetic reference
Exploration suit / alien desert
Synthetic stylized 3D reference
ReID stylized 3D result
Input contract
The model takes exactly one RGB reference image and a prompt describing the
desired output. The output aspect ratio is independent from the reference.
Both reference-conditioning routes are required:
Qwen3-VL image conditioning in the prompt embedding.
Clean reference VAE tokens appended through the edit pipeline.
The adapter was trained with isolated reference attention. kv_cache=True
therefore belongs to the model contract: it precomputes the reference K/V once
and reuses it through all denoising steps.
For best prompt control, crop the reference around the face, hair, and a modest
amount of upper body. Full-body references can preserve clothing or pose more
strongly. The example limits reference conditioning to a 384 * 384 pixel
budget without changing its aspect ratio.
Optional automatic face crop
YuNet is not part of the LoRA and is not required by the reference pipeline.
You may pass any prepared reference image directly. Automatic cropping is
provided only as a convenient default for users who primarily want to preserve
the subject's face while giving the prompt more freedom over clothing, pose,
and composition.
The included example runs the bundled YuNet INT8 detector on a 320x320 view,
selects the highest-confidence face at a 0.35 threshold, maps that box back to
the original image, and builds a larger head crop around it. Detection and
cropping do not reduce the source image before the final crop is taken. If no
face passes the threshold, the original image is used unchanged.
This preprocessing is a recommendation, not a model requirement. Keep the
full reference when clothing, body shape, pose, or surrounding context should
remain influential. Pass --no-face-crop when the reference is already
prepared or automatic cropping is undesirable.
Recommended settings
Setting
Value
Base
krea/Krea-2-Turbo
Steps
8
Guidance scale
0.0
LoRA scale
1.0
Reference pixel budget
384 * 384
Reference VLM encoding
enabled
Reference K/V cache
enabled
ComfyUI diffusion precision
Comfy-Org INT8 ConvRot
LoRA scale 1.0 is the tested default. Lower values relax identity retention;
higher values can overconstrain the image and are not the primary tested path.
Usage
Install a current PyTorch build for your GPU, then install the dependencies:
Run the included example after downloading this repository:
bash
1python example.py \2 --reference character.png \3 --output result.png \4 --prompt "a polished digital illustration of the character, full body, wearing a black suit, standing on a city street"
The example enables the optional face crop by default. To inspect exactly what
the pipeline receives, add --save-reference-crop prepared_reference.png. To
use the input image unchanged:
bash
1python example.py \2 --reference character.png \3 --no-face-crop \4 --output result.png \5 --prompt "a polished digital illustration of the character, full body, wearing a black suit, standing on a city street"
The default BF16 example enables model CPU offload. Systems with enough VRAM
may pass --full-gpu. Quantized Krea 2 runtimes may also load the LoRA, but
quantization setup is runtime-specific and is not bundled here.
ComfyUI
The official workflow is
workflow/krea2_reid_comfyui.json. It uses
the MIT-licensed
ostris/ComfyUI-Krea2-Ostris-Edit
custom nodes, which implement the clean VAE reference-token path, Qwen3-VL
image conditioning, isolated reference attention, and cached reference K/V
required by this adapter.
Import the JSON workflow, select one prepared reference image, enter the
desired output prompt, and queue the graph. The target width and height on the
Empty Latent Image node are independent from the reference dimensions. The
graph defaults to the official Comfy-Org INT8 ConvRot Krea 2 Turbo checkpoint
at 1024x1024. This exact ComfyUI path was validated end to end on an RTX 5090
with the included LoRA and workflow settings. The repository's application
runtime uses a separately packaged INT8 ConvRot checkpoint; do not substitute
that internal file because stock ComfyUI expects the Comfy-Org quantization
format.
BF16 remains a valid fallback, but 1024x1024 BF16 plus Qwen3-VL and reference
K/V exceeded 31.35 GiB in the tested stock ComfyUI runtime. If using BF16,
start at 512x512 or configure additional model offload.
Do not copy settings from a generic Krea 2 edit workflow. The tested contract
is already preset in the included graph:
ComfyUI setting
Required/tested value
LoRA strength
1.0
Krea 2 Ostris Edit kv_cache
true
Steps
8
CFG
1.0
Sampler / scheduler
euler / simple
Prepared reference budget
384 * 384 total pixels
Diffusion checkpoint
krea2_turbo_int8_convrot.safetensors
ComfyUI CFG 1.0 is the no-CFG path corresponding to Diffusers
guidance_scale=0.0. The workflow scales the prepared reference to the tested
pixel budget. For maximum freedom over outfit and pose, prepare a close face or
head-and-shoulders crop before loading it. The bundled automatic YuNet crop is
currently part of the Python example, not the ComfyUI graph.
How it works
The target starts from normal Krea 2 flow-matching noise. The reference image is
encoded by Qwen3-VL for high-level visual context and by the Qwen-Image VAE as a
separate clean token sequence at flow time zero. The LoRA was trained with
paired same-identity references. This teaches the reference path to preserve
identity while releasing source-specific pose, clothing, framing, and
background.
With isolated reference attention, reference tokens attend to one another and
their per-block K/V is independent of the noisy target. The pipeline computes
that K/V once and injects it at every denoising step.
Responsible use
Use reference images only when you have the subject's consent or another
lawful basis to use them.
Do not use this model for deceptive impersonation, fraud, harassment,
non-consensual intimate imagery, biometric identification, or misleading
political or media content.
Deployments must comply with the Krea 2 Community License and Acceptable Use
Policy, implement appropriate content moderation, and disclose AI-generated
content where required.
Limitations
A full-body or costume-heavy reference may still transfer clothing and pose.
A very tight crop can omit identity-defining accessories or hair details.
Similar-looking characters and heavily occluded faces can be confused.
Identity retention varies with prompt, seed, crop, and stacked LoRAs.
Fine facial age, skin texture, or makeup can drift under large scene and
lighting changes in photorealistic outputs.
The model does not guarantee biometric identity and is not a face-recognition
system.
The included BF16 example is provided as a portable reference path; the
reported performance measurement below is from the tested INT8 runtime.
Training summary
The adapter is a transformer-only LoRA with rank/alpha 32/32. Training used
paired images of the same identity, one reference per sample, Qwen3-VL image
conditioning, clean VAE reference tokens, and isolated reference attention.
The final candidate is the 25,600-step checkpoint. Training data and training
infrastructure are not distributed.
In the tested INT8 ConvRot Krea 2 Turbo runtime on an RTX 5090, 1024x1024
8-step inference took approximately 4.55 seconds for the final checkpoint.
This is an implementation- and hardware-specific measurement, not a BF16
benchmark claim.
License
The LoRA weights are a derivative of Krea 2 and are released under the Krea 2
Community License in LICENSE.pdf. The pinned pipeline and example code are
distributed under Apache-2.0 in PIPELINE_LICENSE. This is an unofficial
community release and is not endorsed by Krea or Ostris. The optional YuNet
detector is distributed under its MIT license in YUNET_LICENSE. Use,
modification, and distribution of the model weights are subject to the included
Krea 2 Community License Agreement.
