Instructions to use brushpenbob/graffiti-tattoo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use brushpenbob/graffiti-tattoo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("brushpenbob/graffiti-tattoo") prompt = " " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Graffiti tattoo

- Prompt

- Prompt
Model description
This was an interesting experience, using civet AI to try to train a LoRa model. It's meant to pair with tattoo and graffiti art styles.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('brushpenbob/graffiti-tattoo', weight_name='Graffiti_tattoo-000005.safetensors')
image = pipeline('Your custom prompt').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for brushpenbob/graffiti-tattoo
Base model
runwayml/stable-diffusion-v1-5