Instructions to use cocktailpeanut/jojo-dev2pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/jojo-dev2pro 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("cocktailpeanut/jojo-dev2pro") prompt = "two people playing chess, jojo style" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 288 Bytes
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shuffle_caption = false
caption_extension = '.txt'
keep_tokens = 1
[[datasets]]
resolution = 512
batch_size = 1
keep_tokens = 1
[[datasets.subsets]]
image_dir = 'C:\pinokio\api\fluxgym.git\datasets\jojo-dev2pro'
class_tokens = 'jojo style'
num_repeats = 7 |