Instructions to use kmarplov/runpodlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmarplov/runpodlora 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("kmarplov/runpodlora") prompt = "zdbabc dancing in a bikini" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
zdbabc
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- zdbabc dancing in a bikini

- Prompt
- zdbabc taking a bath

- Prompt
- zdbabc lying in a play

- Prompt
- zdbabc giving a concert
Trigger words
You should use zdbabc 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 kmarplov/runpodlora
Base model
black-forest-labs/FLUX.1-dev