Instructions to use VHKE/bernese-dog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VHKE/bernese-dog 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("VHKE/bernese-dog") prompt = "Bernese dog walking next to a beach --d 45" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Bernese dog
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- Bernese dog walking next to a beach --d 45

- Prompt
- Bernese dog sitting in an open field --d 45

- Prompt
- Bernese dog walking on a road --d 45
Trigger words
You should use Bernese dog 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 VHKE/bernese-dog
Base model
black-forest-labs/FLUX.1-dev