metadata

base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: <EMAD>
widget:
  - text: >-
      <EMAD> running the inc that open source code, models and datasets powering
      & powered by AI money
    output:
      url: image_0.png
  - text: >-
      <EMAD> running the inc that open source code, models and datasets powering
      & powered by AI money
    output:
      url: image_1.png
  - text: >-
      <EMAD> running the inc that open source code, models and datasets powering
      & powered by AI money
    output:
      url: image_2.png
  - text: >-
      <EMAD> running the inc that open source code, models and datasets powering
      & powered by AI money
    output:
      url: image_3.png
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - flux
  - flux-diffusers
  - template:sd-lora
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - flux
  - flux-diffusers
  - template:sd-lora

Flux DreamBooth LoRA - tuenguyen/emad_test

Prompt
<EMAD> running the inc that open source code, models and datasets powering & powered by AI money

Model description

These are tuenguyen/emad_test 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 <EMAD> 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('tuenguyen/emad_test', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('<EMAD> running the inc that open source code, models and datasets powering & powered by AI money').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]