Instructions to use francsharma/chini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use francsharma/chini 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("francsharma/chini") prompt = "head shot portrait photo of a beautiful 20yo woman, lacey white shirt, smiling <lora:beauty_standard1:0.85>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from francsharma/chini: direct link, hf CLI and curl.
- Browser
- Download file 457 Bytes
-
https://huggingface.co/francsharma/chini/resolve/main/README.md
- Command line
-
hf download hf://francsharma/chini/README.md
-
curl -L -o README.md https://huggingface.co/francsharma/chini/resolve/main/README.md
457 Bytes
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/00315-3060916687-esrgan4x.png
text: >-
head shot portrait photo of a beautiful 20yo woman, lacey white shirt,
smiling <lora:beauty_standard1:0.85>
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
chini

- Prompt
- head shot portrait photo of a beautiful 20yo woman, lacey white shirt, smiling <lora:beauty_standard1:0.85>
Download model
Download them in the Files & versions tab.