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('SGalperin/flux_0_5_sky_wandb_wsx_adamw_lr8e4_r8', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('urban street with wsx').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]

SGalperin/flux_0_5_sky_wandb_wsx_adamw_lr8e4_r16

Flux DreamBooth LoRA - SGalperin/flux_0_5_sky_wandb_wsx_adamw_lr8e4_r8

Model description

Trigger words

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Use it with the 🧨 diffusers library

License

Intended uses & limitations

How to use

Limitations and bias

Training details

SGalperin/
flux_0_5_sky_wandb_wsx_adamw_lr8e4_r16