Instructions to use brushpenbob/graffiti-canvas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use brushpenbob/graffiti-canvas with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("brushpenbob/graffiti-canvas") prompt = " " image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Graffiti canvas

- Prompt

- Prompt

- Prompt

- Prompt
Model description
The remake of the graffiti xl model. The data is set for this does not encompass as many of the “sticker” looks and more so just graffiti tags
Trigger words
You should use evang, graffiti to trigger the image generation.
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('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('brushpenbob/graffiti-canvas', weight_name='Graffiti_canvas.safetensors')
image = pipeline('`evang`, `graffiti`').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-canvas
Base model
stabilityai/stable-diffusion-xl-base-1.0