Instructions to use wavymulder/ASCII-flux-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavymulder/ASCII-flux-LoRA 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("wavymulder/ASCII-flux-LoRA") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Created this LoRA as a first test of training with FLUX.1-dev - thank you to fal.ai for providing the compute!
Trained with activation token ASCII art but I recommend using this entire prompt schema for best effect: ASCII art on a white background, made of letters, numbers, and other symbols, dithering effect,
To test this LoRA, I used this workflow (not using the Upscale pass). Flux Guidance of 3.5, euler 40 steps, using fp8 unet and clip - Tenofas v1.0 - FLUX with LoRA's and Ultimate SD Upscaler | ComfyUI Workflow (openart.ai)
Obviously, it's not perfect ASCII. I'm still impressed with the performance VS previous test models trained on SDXL and SD1.5. Hit rate can be prompt dependent.
Usage subject to Flux.1-dev's license.
Also available on Civit.ai
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Model tree for wavymulder/ASCII-flux-LoRA
Base model
black-forest-labs/FLUX.1-dev

