Instructions to use MegaTronX/MetartLoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MegaTronX/MetartLoRA 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("MegaTronX/MetartLoRA") prompt = "[metart] young woman holding a sign that says 'I LOVE PROMPTS!'" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
MetartLoRA
Model trained with AI Toolkit by Ostris

- Prompt
- [metart] young woman holding a sign that says 'I LOVE PROMPTS!'

- Prompt
- woman with red hair and green eyes playing chess in the nude at the park
Trigger words
You should use metart 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('MegaTronX/MetartLoRA', weight_name='MetartLoRA.safetensors')
image = pipeline('[metart] young woman holding a sign that says 'I LOVE PROMPTS!'').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 MegaTronX/MetartLoRA
Base model
black-forest-labs/FLUX.1-dev