Instructions to use Veetance/FLUX-Klein-9B-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Veetance/FLUX-Klein-9B-NF4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Veetance/FLUX-Klein-9B-NF4", 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
- Draw Things
- DiffusionBee
FLUX-Klein-9B-NF4 | Debloated
This repository contains the FLUX Klein 9B runtime bundle prepared for the Asset Editor runtime.
Model Details
- Architecture: FLUX Klein 9B
- Quantization: NF4 where applicable inside the runtime bundle
- Primary use: local asset generation and refinement workflows for the Asset Editor stack
- License: personal, non-commercial use only
Included Runtime Components
transformer/text_encoder_9b_nf4/tokenizer/vae/scheduler/model_index.json
Integration
Optimized for the Veetance Asset Editor runtime.
Links
- Core engine: Veetance Asset Editor
- Triage and development: Asset Editor GitHub
See LICENSE.txt for the personal-use terms.
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