Instructions to use CowLiker/overflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CowLiker/overflow 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("CowLiker/overflow") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
overflow

- Prompt
- -
Trigger words
You should use which is noticeably stretched and under a significant strain to trigger the image generation.
You should use suggesting it is too small for her large breasts to trigger the image generation.
You should use that barely contains her breasts to trigger the image generation.
You should use which are significantly larger to trigger the image generation.
You should use large full breasts which spill out of to trigger the image generation.
You should use Spilling out of to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for CowLiker/overflow
Base model
black-forest-labs/FLUX.1-dev