Flux-Fill DreamBooth LoRA - hjh3927/flux-fill-chart1-2-data-lora

Prompt

Three-panel image. [Left]: Full infographic with data elements and decorations. [Center]: Only data elements(bars, pie or annular sectors, lines, scatter points). [Right]: Only decorative elements(text labels, titles, icons, pictograms, arrows). Center and Right are complementary, together forming the Left.

Model description

These are hjh3927/flux-fill-chart1-2-data-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev.

The weights were trained using DreamBooth with a custom Flux diffusers trainer.

Was LoRA for the text encoder enabled? False.

Trigger words

You should use chart separated into data primitives and decorative elements 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('hjh3927/flux-fill-chart1-2-data-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Three-panel image. [Left]: Full infographic with data elements and decorations. [Center]: Only data elements(bars, pie or annular sectors, lines, scatter points). [Right]: Only decorative elements(text labels, titles, icons, pictograms, arrows). Center and Right are complementary, together forming the Left.').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]