Krea-2-PoseControl-LoRA-checkpoints

Krea-2 Pose Control-LoRA

Pose-conditioned image generation for Krea-2 using a 17-keypoint body skeleton.

<p align="center"> <img src="https://raw.githubusercontent.com/s-adhit/krea2-pose-controlnet/main/docs/showcase/final/hero-v2/final/final_generation_montage.png" alt="Krea-2 Pose Control final generations" width="100%"> </p> <p align="center"> <img src="https://raw.githubusercontent.com/s-adhit/krea2-pose-controlnet/main/docs/showcase/final/hero-v2/final/final_condition_montage.png" alt="Krea-2 Pose Control pose conditions" width="100%"> </p>

The pose condition controls broad body geometry and placement, while the prompt controls subject appearance, clothing, environment, lighting, and style.

Model

Recommended release:

release/krea2-pose-control-mix025.safetensors

• Candidate: mix-025
• Saved parameters: 215,488,512
• SHA-256: 6d97e9c2e102e07928fc8864346401a0d2e6082d610ca6b037c4704102e3f8d1

The other checkpoint folders in this repository are historical training runs. release/ contains the recommended public model.

Pose conditioning

The model uses 17 COCO-style body keypoints:

• nose, eyes, ears
• shoulders, elbows, wrists
• hips, knees, ankles

Hands, fingers, and face landmarks are not part of the control signal.

Use pose rasters generated in this project's trained format rather than assuming arbitrary OpenPose renders are compatible.

Inference

Recommended settings:

Base: Krea-2 Turbo
Steps: 8
CFG: 0
mu: 1.15
Control scale: 1.0
Geometry: native aspect-preserving

PYTHONPATH=. python inference.py \
  --turbo-ckpt /path/to/krea2-turbo.safetensors \
  --release-artifact release/krea2-pose-control-mix025.safetensors \
  --prompt "fantasy mage, ornate robes, cinematic lighting" \
  --pose-image /path/to/pose.png \
  --output output.png \
  --seed 42

For stronger pose adherence, a control scale of 1.25–1.50 can also be useful.

See the prompting guide for examples and prompt recommendations.

Results

Native aspect-preserving inference gave better pose adherence than dynamic-768 in the frozen evaluation.

Limitations

• Overlapping multi-person poses are harder.
• Fingers and facial pose are not directly controlled.
• Prompts that conflict with the supplied pose, framing, or subject count can reduce pose adherence.

Links

• GitHub
• Prompting guide
• Release decision
• PoseBridge dataset
• Krea-2