☕ **Support on Ko-fi** — all tips go straight to GPU compute for future versions. 🧰 **Training code is public:** krea2edit-trainer — the ai-toolkit extension these LoRAs were trained with, geometry-matched to the nodes, with measured consumer-GPU VRAM requirements. > **V2 ships as two LoRAs:** > > - **`krea2_identity_edit_v2.safetensors` — Krea 2 Identity Edit V2.** Put a person > from one photo into any scene, pose, outfit and lighting. Extremely improved > likeness, better sharpness and skin/fabric texture, and far more freedom to > restage than v1.2. > - **`krea2_general_edit_v2.safetensors` — Krea 2 General Edit V2.** Instruction-based > editing: remove, recolor, relight, swap backgrounds, add objects, change text and > outfits, restyle, flat lays. > > **License:** free for personal / non-commercial use; commercial use needs a > license from me, case by case — see License. > > **Use one LoRA per task** — they share a lineage, so loading both at full strength > breaks the image (see Using both). > > **Requires the V2 nodes** (`comfyui-krea2edit` ≥ 2.0.0), which add **reference > placement**: choose per reference whether it is pinned onto the output layout or > placed beside it — e.g. keep a scene exactly while placing a person freely in it. > > **Low-VRAM versions:** `..._v2_r128.safetensors` (0.91GB) and `..._v2_r64.safetensors` (0.46GB) > for both LoRAs — rank-reduced from the full files, near-identical quality. > > Prior versions remain available (`..._v1_2*`, `..._v1_1*`, `..._v1`).
MiewID-msv2 is a feature extractor trained for re-identification using contrastive learning on a large, high-quality dataset of 54 wildlife species - terrestrial and aquatic - including fins, flukes, flanks, faces. Intended use is re-identification of individuals from different species by matching against a database of grount-truth samples. Model features can also be used for species classification by retrieval. View more usage examples at https://github.com/WildMeOrg/wbia-plugin-miew-id/tree/main/wbia_miew_id/examples
- `kokoro_vi.pth`: PyTorch Kokoro `KModel` checkpoint for inference. - `kokoro_vi.onnx`: ONNX Runtime export of the acoustic model. - `kokoro_vi_voicepack.pt`: default Vietnamese voicepack. - `config.json`: Kokoro config/vocab used by both PyTorch and ONNX inference. - `voicepacks/*.pt`: additional Vietnamese voicepacks. The ONNX CLI downloads `kokoro_vi.onnx`, `kokoro_vi_voicepack.pt`, and `config.json` from this repository when local paths are not provided. Install `onnxruntime-gpu` and pass `--device cuda` to use CUDAExecutionProvider when available. Vietnamese G2P is handled by `vig2p`, matching the GitHub inference and training code.
