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-4B", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("DirtScan/trail-delighter")
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]Trail Delighter — FLUX.2 Klein 4B LoRA
This is a LoRA for FLUX.2 Klein 4B specifically for the task of giving natural light to mountain bike trail photographs.
The process was as follows:
- Photos are taken of a mountain bike trail (often sunny, cast shadows, lens flare, glare)
- A selection of these were de-lighted using https://fal.ai/models/fal-ai/qwen-image-edit-2509-lora-gallery/lighting-restoration - This creates a "teacher set"
- This teacher set was then used to train a new LoRA, but with a base of FLUX.2 Klein 4B (instead of Qwen) - overall making the model smaller and easier to run.
License
The adapter is provided under Apache-2.0, subject to the terms of the base FLUX.2 Klein model.
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Model tree for DirtScan/trail-delighter
Base model
black-forest-labs/FLUX.2-klein-4B