Instructions to use cocktailpeanut/akira-schnell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/akira-schnell 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-schnell", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cocktailpeanut/akira-schnell") prompt = "a scene from akira, a 8 year old girl and an old woman walking across a street" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
akira-schnell
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- a scene from akira, a 8 year old girl and an old woman walking across a street

- Prompt
- a scene from akira, a guy eating ramen

- Prompt
- a scene from akira, two people playing tennis
Trigger words
You should use from akira 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/akira-schnell
Base model
black-forest-labs/FLUX.1-schnell