Flux DreamBooth LoRA - LeoTechServices/meathead-style-lora

Prompt

mthd16 pixel art, full body sprite of a young woman with dark brown skin, long black braids, glasses, wearing a green hoodie and jeans, standing idle, plain dark background

Prompt

mthd16 pixel art, full body sprite of a young woman with dark brown skin, long black braids, glasses, wearing a green hoodie and jeans, standing idle, plain dark background

Prompt

mthd16 pixel art, full body sprite of a young woman with dark brown skin, long black braids, glasses, wearing a green hoodie and jeans, standing idle, plain dark background

Prompt

mthd16 pixel art, full body sprite of a young woman with dark brown skin, long black braids, glasses, wearing a green hoodie and jeans, standing idle, plain dark background

Model description

These are LeoTechServices/meathead-style-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.

The weights were trained using DreamBooth with the Flux diffusers trainer.

Was LoRA for the text encoder enabled? False.

Trigger words

You should use mthd16 pixel art, a 16-bit pixel art game asset to trigger the image generation.

Download model

Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('LeoTechServices/meathead-style-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('mthd16 pixel art, full body sprite of a young woman with dark brown skin, long black braids, glasses, wearing a green hoodie and jeans, standing idle, plain dark background').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

License

Please adhere to the licensing terms as described here.

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]