Instructions to use cocktailpeanut/yoshimitsu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/yoshimitsu 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/yoshimitsu") prompt = "a photo of yoshimitsu jogging in new york central park" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
yoshimitsu
Trained with Fluxgym

- Prompt
- a photo of yoshimitsu jogging in new york central park

- Prompt
- a photo of yoshimitsu playing a contrabass, jazz club.

- Prompt
- a photo of yoshimitsu cooking in the kitchen

- Prompt
- a photo of yoshimitsu driving a car
Trigger words
You should use yoshimitsu 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 cocktailpeanut/yoshimitsu
Base model
black-forest-labs/FLUX.1-dev