Instructions to use evyatarXR/Yad2Photo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use evyatarXR/Yad2Photo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("evyatarXR/Yad2Photo") prompt = "Yad2 photo" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
|
Download README.md from evyatarXR/Yad2Photo: direct link, hf CLI and curl.
- Browser
- Download file 557 Bytes
-
https://huggingface.co/evyatarXR/Yad2Photo/resolve/main/README.md
- Command line
-
hf download hf://evyatarXR/Yad2Photo/README.md
-
curl -L -o README.md https://huggingface.co/evyatarXR/Yad2Photo/resolve/main/README.md
557 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: Yad2 photo
license: other
Yad2Photo
Model description
Trigger words
You should use Yad2 photo to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/turbo-flux-trainer.