☕ **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`).
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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.
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COST EXPLORER
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Cost analysis
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MODEL HANDBOOK
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Selection guide & FAQ
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.
