Instructions to use SamuelTallet/FLUX.2-klein-4B-SDNQ-8bit-dynamic-hadamard256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SamuelTallet/FLUX.2-klein-4B-SDNQ-8bit-dynamic-hadamard256 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("SamuelTallet/FLUX.2-klein-4B-SDNQ-8bit-dynamic-hadamard256", dtype=torch.bfloat16, device_map="cuda") 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
This is FLUX.2 [klein] 4B optimized using SDNQ with INT8 dynamic quantization and Hadamard Rotation (Group size: 256).
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Model tree for SamuelTallet/FLUX.2-klein-4B-SDNQ-8bit-dynamic-hadamard256
Base model
black-forest-labs/FLUX.2-klein-4B