Instructions to use NeuralGL/meatcanyon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NeuralGL/meatcanyon 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("NeuralGL/meatcanyon") prompt = "Donald Trump, closeup, wearing blue suit, white house building on background, in style of meatcanyon" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
MeatCanyon
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- Donald Trump, closeup, wearing blue suit, white house building on background, in style of meatcanyon

- Prompt
- A bald man with goatee beard, wearing gray jacket, american desert with van on background, in style of meatcanyon

- Prompt
- A blonde woman wearing pink dress, big lips, posing, closeup, aesthetic, full-body shot, forest background, in style of meatcanyon
Trigger words
You should use meatcanyon, papameat 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 NeuralGL/meatcanyon
Base model
black-forest-labs/FLUX.1-dev