Instructions to use babafuture/paula with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use babafuture/paula 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("babafuture/paula") prompt = "Paula101, close-up face portrait, soft light, neutral expression, 50mm lens, shallow depth of field" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Paula
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- Paula101, close-up face portrait, soft light, neutral expression, 50mm lens, shallow depth of field

- Prompt
- Paula101, headshot, slight smile, natural lighting, clean skin, symmetrical face

- Prompt
- Paula101, cinematic portrait, dramatic shadows, serious expression

- Prompt
- Paula101, front view, intense gaze, high detail, ultra-sharp, blurred background

- Prompt
- Paula101, expressive face, studio lighting, neutral background
Trigger words
You should use Paula101 to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
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Model tree for babafuture/paula
Base model
black-forest-labs/FLUX.1-dev