Instructions to use jimipatel/gigachad123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jimipatel/gigachad123 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("jimipatel/gigachad123") prompt = "movie scene, still of gigachad man, halloween, halloween costume, night sky, outdoors, 1boy, vampire costume, mascot, , faded colors, washed out image, film grain, 90s analog photo" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
gigachad

- Prompt
- movie scene, still of gigachad man, halloween, halloween costume, night sky, outdoors, 1boy, vampire costume, mascot, , faded colors, washed out image, film grain, 90s analog photo
Trigger words
You should use gigachad 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 jimipatel/gigachad123
Base model
black-forest-labs/FLUX.1-dev