Instructions to use camenduru/FLUX.1-Kontext-dev-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use camenduru/FLUX.1-Kontext-dev-int4 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("camenduru/FLUX.1-Kontext-dev-int4", 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
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-dev-int4", 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]pip install git+https://github.com/huggingface/diffusers.git
pip install -U bitsandbytes
pip install -U transformers
pip install sentencepiece
pip install protobuf
import torch
from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import uuid
pipe = FluxKontextPipeline.from_pretrained("kpsss34/FLUX.1-Kontext-dev-int4", torch_dtype=torch.bfloat16)
pipe.to("cuda")
input_image = load_image("./1.jpg")
image = pipe(
image=input_image,
prompt="woman wearing dress",
guidance_scale=2.5,
num_inference_steps=30
).images[0]
filename = f"generated_image_{uuid.uuid4().hex}.png"
image.save(filename)
print(f"Saved image as {filename}")
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Model tree for camenduru/FLUX.1-Kontext-dev-int4
Base model
black-forest-labs/FLUX.1-Kontext-dev