Instructions to use cocktailpeanut/jojo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/jojo 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") prompt = "two people playing chess, jojo style" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/jojo_001600_00_20240921120826.png
text: two people playing chess, jojo style
- output:
url: sample/jojo_001600_01_20240921120842.png
text: a little kid eating ice cream, jojo style
- output:
url: sample/jojo_001600_02_20240921120857.png
text: an old doctor, jojo style
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: jojo style
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
jojo
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- two people playing chess, jojo style

- Prompt
- a little kid eating ice cream, jojo style

- Prompt
- an old doctor, jojo style
Trigger words
You should use jojo style 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.