Instructions to use ashen0209/flux-lora-wlop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ashen0209/flux-lora-wlop with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FLUX", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ashen0209/flux-lora-wlop") prompt = " " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Flux DreamBooth LoRA - ashen0209/flux-lora-wlop

- Prompt

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Model description
These are ashen0209/flux-lora-wlop LoRA weights for FLUX.
The model is trained based on https://huggingface.co/ashen0209/Flux-Dev2Pro (using this model to train a LoRA produces a better result), but please do not apply the LoRA on my trained model. Just use it on original Flux-dev.
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('ashen0209/flux-lora-wlop', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a portrait of a woman').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]
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