metadata

base_model: black-forest-labs/FLUX.1-Fill-dev
library_name: diffusers
license: other
instance_prompt: >-
  Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure
  white background
widget:
  - text: >-
      Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure
      white background
    output:
      url: image_0.png
  - text: >-
      Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure
      white background
    output:
      url: image_1.png
  - text: >-
      Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure
      white background
    output:
      url: image_2.png
  - text: >-
      Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure
      white background
    output:
      url: image_3.png
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - flux
  - flux-diffusers
  - template:sd-lora

Flux-Fill DreamBooth LoRA - cucucu666/flux_hard_5.13

Prompt
Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure white background

Model description

These are cucucu666/flux_hard_5.13 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 Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure white background 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('cucucu666/flux_hard_5.13', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Hadshdw object, hard gray shadow on the bottom, blurred shadow edges, pure white 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]