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/new-chart3-1-flux-fill-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('This is a four-panel image grid: [TOP-LEFT]: The original chart. [TOP-RIGHT]: An edited version of [TOP-LEFT] keeping only data elements. [BOTTOM-LEFT]: Another chart. [BOTTOM-RIGHT]: An edited version of [BOTTOM-LEFT] applying the same data-only transformation as in [TOP-RIGHT]. ').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]