Instructions to use vafipas663/flux2-klein-base-9b-distill-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vafipas663/flux2-klein-base-9b-distill-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-9B,black-forest-labs/FLUX.2-klein-base-9B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("vafipas663/flux2-klein-base-9b-distill-lora") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
LoRA for Klein Base 9B extracted from Klein 9B using https://github.com/kijai/ComfyUI-FluxTrainer
For the reasons I cannot explain, it produces static noise at strength > 0.5. And sometimes, you have to go as low as 0.1.
Compared to https://civitai.com/models/2324315/klein-4b9b-base-to-turbo-lora it handles CFG much better, and produces higher frequency details
But on the other hand, https://civitai.com/models/2324315/klein-4b9b-base-to-turbo-lora is much closer to Klein 9B in terms of its output
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Model tree for vafipas663/flux2-klein-base-9b-distill-lora
Base model
black-forest-labs/FLUX.2-klein-9B