CthomasdesignandClaude Fable 5
98ebf5b0bf
Split SingleStreamDiT.__call__ into prepare_conditioning() (text fusion, RoPE tables, attention masks — identical every step) and denoise_step(); sample() now prepares once and steps 8 times instead of recomputing the whole text path per step. Also eliminates the two .item() GPU syncs that _additive_mask forced on every step. __call__ still composes both, so the external API is unchanged. Parity: bit-exact incl. LoRA case. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
ComfyUI-Krea2-MLX
Run Krea-2-Turbo text-to-image locally on Apple Silicon inside ComfyUI, using Apple's MLX framework — fast and memory-efficient (quantized to fit a 32 GB Mac). Supports stackable LoRAs (official Krea / diffusers and ai-toolkit / PEFT formats).
The pure-MLX pipeline (krea2_engine/) is vendored directly in this repo — no external git
dependency — and runs in-process, returning standard ComfyUI IMAGE tensors. It's a
self-contained node group: load → (LoRAs) → generate → IMAGE, then use any ComfyUI node downstream
(save, upscale, etc.). It does not expose a torch MODEL, so it does not plug into ComfyUI's
native KSampler/ControlNet.
macOS on Apple Silicon (M-series) only. The MLX dependencies do not run on Windows/Linux. ~24 GB unified memory recommended for 1024².
Install
Via ComfyUI-Manager (recommended): search for Krea2-MLX and install; Manager runs
requirements.txt, which installs the vendored pipeline's MLX deps into ComfyUI's Python.
Manual:
cd ComfyUI/custom_nodes
git clone https://github.com/cthomasdesign/ComfyUI-Krea2-MLX
<ComfyUI-python> -m pip install -r ComfyUI-Krea2-MLX/requirements.txt
Restart ComfyUI.
Models
Put a transformer build in ComfyUI/models/krea2/ — the Krea2 Model Loader lists what's
there and infers precision from the filename (mixed_4_8 / 8bit / bf16):
| Build | Size | Repo → file |
|---|---|---|
| mixed-4/8 | 9.8 GB | avlp12/Krea-2-Turbo-Alis-MLX-mixed-4-8 → transformer_mixed_4_8.safetensors |
| 8-bit | 14 GB | avlp12/Krea-2-Turbo-Alis-MLX-8bit → transformer_8bit.safetensors |
On first generation the VAE + Qwen3-VL-4B text encoder (~9 GB) download automatically from
krea/Krea-2-Turbo (cached in ~/.cache/krea2_alis_mlx). Put LoRAs in ComfyUI/models/loras/.
Nodes (category Krea2 MLX)
| Node | Purpose |
|---|---|
| Krea2 Model Loader (MLX) | Pick a build from models/krea2 → KREA2_PIPE (cached; stays resident). |
| Krea2 LoRA (MLX) | Pick a LoRA from models/loras + strength → KREA2_LORASTACK. Chain several to stack. |
| Krea2 Generate (MLX) | KREA2_PIPE + prompt/size/steps/seed (+ optional LoRA stack) → IMAGE. Progress bar + Cancel supported; NSFW safety_filter on by default. |
| Krea2 Unload (MLX) | Frees the cached model and clears the Metal cache to reclaim memory. |
Minimal workflow: Krea2 Model Loader → Krea2 Generate → Preview Image.
With LoRAs: Krea2 LoRA → (Krea2 LoRA → …) → Krea2 Generate. See example_workflows/.
Notes
- One 12.9B model (~18–20 GB with encoder/VAE) stays resident. On 32 GB, avoid loading large torch models simultaneously; use Krea2 Unload to free memory.
- The
safety_filterruns the pure-MLXFalconsai/nsfw_image_detectionclassifier; the Krea 2 Community License (§4.2) requires reasonable content-filtering in deployments — keep it on there. - Where required by law, disclose that outputs are AI-generated (License §4.3).
License & attribution
Independent, unofficial — not an official Krea product or endorsed by Krea. The model is a
Derivative of krea/Krea-2-Turbo under the Krea 2
Community License (see LICENSE); the MLX port is by avlp12
(Alis MLX), with LoRA support added in
krea2-mlx-studio. The engine (krea2_engine/)
is vendored here from that lineage — see NOTICE for the full attribution chain and
what changed at each step. Model weights are downloaded by the user, not redistributed here.
Node/engine code here: MIT (per upstream). NSFW classifier:
Falconsai/nsfw_image_detection
(Apache-2.0). By using the weights you agree to the Krea 2 Community License (commercial use requires
< $1M annual revenue, or an enterprise license).