Instructions to use CaaretAI/FLUX.2-klein-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CaaretAI/FLUX.2-klein-4B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CaaretAI/FLUX.2-klein-4B", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
FLUX.2-klein-4B (Caaret backup)
Backup of the public weights at black-forest-labs/FLUX.2-klein-4B.
These files are the original checkpoint. Caaret does not ship a requantized copy. Mining is a runtime hook: wide transformer linears (inner size โฅ 2048) run as noisy INT7 GEMM while the picture is drawn, and the same linears keep searching at 1024 rows when no picture is waiting. A real request takes the GPU before the next layer.
Official sampler for this distilled model: 1024ร1024, 4 steps, guidance_scale=1.0.
On 2026-10-26 an idle pass of these weights on an RTX 3080 found Caaret block 180 (09c4e838e1102d0cce8f5fb055e85d0b253439a3f5decae7c4aa384a6ba7edd4).
License: Apache 2.0, same as the upstream model.
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black-forest-labs/FLUX.2-klein-4B