Text-to-Image
Diffusers
Safetensors
English
Flux2KleinPipeline
flux
flux2-klein
quantization
sdnq
4-bit precision
dynamic-quantization
low-vram
google-colab
t4
batch-image-edit
background-removal
8-bit precision
Instructions to use codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic", 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
Download colab_notebooks/twin_input_setup/decrypt_results.ipynb from codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic: direct link, hf CLI and curl.
- Browser
- Download file 6.09 kB
-
https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/resolve/main/colab_notebooks/twin_input_setup/decrypt_results.ipynb
- Command line
-
hf download hf://codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/colab_notebooks/twin_input_setup/decrypt_results.ipynb
-
curl -L -o decrypt_results.ipynb https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/resolve/main/colab_notebooks/twin_input_setup/decrypt_results.ipynb
6.09 kB