Instructions to use cocktailpeanut/pennywise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/pennywise 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("cocktailpeanut/pennywise") prompt = "a photo of pnw dressed as a priest, standing on an altar in a church" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
pennywise
Trained with Fluxgym

- Prompt
- a photo of pnw dressed as a priest, standing on an altar in a church

- Prompt
- a photo of pnw selling hot dogs on a hot dog stand in new york times square

- Prompt
- a photo of pnw dressed in bikini standing on a beach

- Prompt
- pnw wearing a basketball jersey playing basketball

- Prompt
- pnw playing the piano in a dimly lit jazz club
Trigger words
You should use pnw to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
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Model tree for cocktailpeanut/pennywise
Base model
black-forest-labs/FLUX.1-dev