Instructions to use Madushan996/yenetg-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Madushan996/yenetg-lora 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("Madushan996/yenetg-lora") prompt = " [trigger] A woman in a bustling cafe " image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
yenetg-lora
Model trained with AI Toolkit by Ostris

- Prompt
- [trigger] A woman in a bustling cafe

- Prompt
- [trigger] A woman taking a iPhone selfie

- Prompt
- [trigger] A woman relaxing in a beach
Trigger words
You should use yenetlezero to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Madushan996/yenetg-lora', weight_name='yenetg-lora.safetensors')
image = pipeline(' [trigger] A woman in a bustling cafe ').images[0]
image.save("my_image.png")
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for Madushan996/yenetg-lora
Base model
black-forest-labs/FLUX.1-dev