README.md

metadata

title: Krea 2 Pose ControlNet
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
pinned: false
short_description: Generate characters in a pose from a reference image
python_version: '3.12'
models:
  - krea/Krea-2-Turbo
  - thedeoxen/Krea-2-pose-controlnet

Krea 2 Turbo — Pose ControlNet LoRA

Drive generation with a body pose, either from a ready-made skeleton image or from a normal photo whose pose you can edit interactively before generating.

Two input modes

• Pose Image: upload an already-rendered OpenPose/DWPose skeleton image; it is used as the control input exactly as provided.
• Regular Image: upload a normal photo; a DWPose skeleton is auto-extracted and shown in an interactive editor where you can drag individual joints to tweak the pose. The (possibly edited) skeleton is what conditions generation. (Editor inspired by linoyts/Flux-2-control-pose.)

How it works

1. Pose extraction: photos are processed with DWPose (via controlnet_aux) to produce an OpenPose skeleton map on a black background.
2. Conditioning: the pose map is fed to the Krea 2 model through the Ostris Edit community pipeline — the pose image enters both the Qwen3-VL text encoder (as a vision token) and the transformer sequence (as a clean VAE reference latent at t=0). The pose image is scaled to the nearest ~1 MP preferred resolution bucket (an exact replica of ComfyUI's FluxKontextImageScale: nearest-aspect bucket → center-crop → Lanczos), and that same scaled image drives both the VAE reference latents and the output canvas. This keeps the pose latent grid and the output grid identical and runs the model at its native ~1 MP resolution — which is what makes the model actually follow the control pose at full quality.
3. Generation: the model generates a new image following the body pose while the text prompt defines appearance, clothing, and scene.

Model

• Base: krea/Krea-2-Turbo (8-step distilled, guidance_scale=0 disables guidance)
• LoRA: thedeoxen/Krea-2-pose-controlnet (recommended weight 0.8–1.0, ~10 steps, CFG ~1.0)