You can deploy this model and start earning money today!

Kontext-Style/
Picasso_lora

Name: Picasso_lora
Availability: InStock

black-forest-labs/FLUX.1-Kontext-dev

adapter

Views

No views yet

Picasso Style LoRA for FLUX.1 Kontext Model

This repository provides the Picasso style LoRA adapter for the FLUX.1 Kontext Model. This LoRA is part of a collection of 20+ style LoRAs trained on high-quality paired data generated by GPT-4o from the OmniConsistency dataset.

Contributor: Tian YE & Song FEI, HKUST Guangzhou.

Style Showcase

Here are some examples of images generated using this style LoRA:

Inference Example

python

from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
import torch

# Load the base pipeline
pipeline = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev", 
    torch_dtype=torch.bfloat16
).to('cuda')

# Load the LoRA adapter for the Picasso style directly from the Hub
pipeline.load_lora_weights("Kontext-Style/Picasso_lora", weight_name="Picasso_lora_weights.safetensors", adapter_name="lora")
pipeline.set_adapters(["lora"], adapter_weights=[1])

# Load a source image (you can use any image)
image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))

# Prepare the prompt
# The style_name is used in the prompt and for the output filename.
style_name = "Picasso"
prompt = f"Turn this image into the Picasso style."

# Run inference
result_image = pipeline(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    num_inference_steps=24
).images[0]

# Save the result
output_filename = f"{style_name.replace(' ', '_')}.png"
result_image.save(output_filename)

print(f"Image saved as {output_filename}")

Feel free to open an issue or contact us for feedback or collaboration!