Instructions to use DDhaven/lyra-vex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DDhaven/lyra-vex 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("DDhaven/lyra-vex") prompt = "lyravex, close-up portrait, neutral expression, studio lighting" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
lyra-vex
Model trained with AI Toolkit by Ostris

- Prompt
- lyravex, close-up portrait, neutral expression, studio lighting

- Prompt
- lyravex, standing in a dim concrete room, red ambient light, cinematic sci-fi still, film grain

- Prompt
- lyravex, medium shot, arms crossed, plain background
Trigger words
You should use lyravex 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('DDhaven/lyra-vex', weight_name='lyra-vex.safetensors')
image = pipeline('lyravex, close-up portrait, neutral expression, studio lighting').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 DDhaven/lyra-vex
Base model
black-forest-labs/FLUX.1-dev