README.md

metadata

language:
  - en
base_model:
  - black-forest-labs/FLUX.1-Kontext-dev
  - HighCWu/FLUX.1-Kontext-dev-bnb-hqq-4bit
pipeline_tag: image-to-image
library_name: diffusers
tags:
  - Style
  - lora
  - Jojo
  - FluxKontext
  - Image-to-Image
datasets:
  - HighCWu/OmniConsistencyJojo

Jojo Style LoRA V1 Trained with FLUX.1 Kontext dev 4bit on 16GB VRAM

This repository provides the Jojo style LoRA adapter for the FLUX.1 Kontext Model. It was trained by a fork version of ostris/ai-toolkit with the config file => train_lora_flux_kontext_16gb_jojo.yaml

Try Jojo_lora_4bit_training_v2 for low vram high resolution training.

Style Showcase

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

Inference Example

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

try: # A temp hack for some version diffusers lora loading problem
    from diffusers.utils.peft_utils import _derive_exclude_modules

    def new_derive_exclude_modules(*args, **kwargs):
        exclude_modules = _derive_exclude_modules(*args, **kwargs)
        if exclude_modules is not None:
            exclude_modules = [n for n in exclude_modules if "proj_out" not in n]
        return exclude_modules
    peft_utils._derive_exclude_modules = new_derive_exclude_modules
except:
    pass

pipe = FluxKontextPipeline.from_pretrained("HighCWu/FLUX.1-Kontext-dev-bnb-hqq-4bit", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("HighCWu/Jojo_lora_4bit_training_v1", weight_name="flux_kontext_jojo_style_lora_v1.safetensors", adapter_name="jojo")
pipe.set_adapters(["jojo"], [1.0])
pipe.to("cuda")

# 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 = "JojoV1"
prompt = f"Turn this image into the style of JoJo's Bizarre Adventure. "

# Run inference
result_image = pipe(
    image=image, 
    prompt=prompt, 
    height=1024, 
    width=1024, 
    guidance_scale=4.0,
    num_inference_steps=28,
).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!