Kraken Flux Lora

🚀 kraken-flux-lora

This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev. It offers capabilities in text-to-image and image-to-image generation, leveraging the power of the base model.

🚀 Quick Start

The main validation prompt used during training was:

editorial close-up of a woman, glowing flawless skin, peach blush and gloss, nude shimmer lips, editorial studio light

✨ Features

• Diverse Generation: Capable of text-to-image and image-to-image generation.
• Customizable Settings: Allows for various validation and training settings.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'hbxartdev/kraken-flux-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "editorial close-up of a woman, glowing flawless skin, peach blush and gloss, nude shimmer lips, editorial studio light"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]

model_output.save("output.png", format="PNG")

📚 Documentation

Validation settings

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery: