Image-to-Image
Diffusers
Safetensors
FluxKontextPipeline
flux
flux.1
kontext
flux dev
lightning
turbo
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("camenduru/FLUX.1_Kontext-Lightning", 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]Update 7/9/25: This model is now quantized and implemented in this example space. Seeing preliminary VRAM usage at around ~10GB with faster inferencing. Will be experimenting with different weights and schedulers to find particularly well-performing libraries.
FLUX.1 Kontext-dev X LoRA Experimentation
Highly experimental, will update with more details later.
- 6-8 steps
Euler, SGM Uniform (Recommended, feel free to play around)Getting mixed results now, feel free to play around and share.
Model Details
Experimenting with FLUX.1-dev LoRAs and how it affects Kontext-dev. This model has been fused with acceleration LoRAs.
License
This model falls under the FLUX.1 [dev] Non-Commercial License, please familiarize yourself with the license.
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Model tree for camenduru/FLUX.1_Kontext-Lightning
Base model
black-forest-labs/FLUX.1-Kontext-dev