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.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("jerrrycans/watermark3000")
prompt = "remove all the watermarks from this image, all watermarks that are over this image"
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]Watermark3000
About this LoRA
This is a LoRA for the FLUX.1-Kontext-dev image-to-image model. It can be used with diffusers or ComfyUI.
It was trained on Replicate using: https://replicate.com/replicate/fast-flux-kontext-trainer/train
Prompt instruction
You should use remove all the watermarks from this image, all watermarks that are over this image as part of the prompt instruction for your image-to-image editing.
Training details
- Steps: 3000
- Learning rate: 0.001
- LoRA rank: 16
Contribute your own examples
You can use the community tab to add images that show off what you’ve made with this LoRA.
- Downloads last month
- 10
Model tree for jerrrycans/watermark3000
Base model
black-forest-labs/FLUX.1-Kontext-dev