Instructions to use kratadata/glitch-landscape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kratadata/glitch-landscape with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kratadata/glitch-landscape") prompt = "SK3TCHING" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from kratadata/glitch-landscape: direct link, hf CLI and curl.
- Browser
- Download file 561 Bytes
-
https://huggingface.co/kratadata/glitch-landscape/resolve/main/README.md
- Command line
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hf download hf://kratadata/glitch-landscape/README.md
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curl -L -o README.md https://huggingface.co/kratadata/glitch-landscape/resolve/main/README.md
561 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: SK3TCHING
license: other
glitch landscape
Model description
Trigger words
You should use SK3TCHING to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-2-trainer.