Instructions to use Jonjew/CharlotteRampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jonjew/CharlotteRampling with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Jonjew/CharlotteRampling") prompt = "<lora:charlotte-rampling-flux:1.1> the, her, woman, and, has elegant classic 1900s portrait giant elegant elaborate hat with colorful feathers low neck line showing bosom" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Charlotte Rampling Flux

- Prompt
- <lora:charlotte-rampling-flux:1.1> the, her, woman, and, has elegant classic 1900s portrait giant elegant elaborate hat with colorful feathers low neck line showing bosom
Model description
FROM https://civitai.com/models/1042026/charlotte-rampling-flux?modelVersionId=1169149
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for Jonjew/CharlotteRampling
Base model
black-forest-labs/FLUX.1-dev