Instructions to use jimipatel/Akuma1500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jimipatel/Akuma1500 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("jimipatel/Akuma1500") prompt = "a2u7a" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: a2u7a
output:
url: images/1000024053.webp
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: a2u7a
license: apache-2.0
Akuma1500

- Prompt
- a2u7a
Trigger words
You should use a2u7a to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.