Instructions to use aztro/iatnat-ma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aztro/iatnat-ma 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("aztro/iatnat-ma") prompt = "A close-up shot of iara, a woman with dark hair, she is confidently models an exquisite piece of lingerie. The soft, golden lighting illuminates her features, creating a warm and inviting atmosphere. Framed by a plain white background, the focus is solely on the model's captivating presence.\n" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
iatnat

- Prompt
- A close-up shot of iara, a woman with dark hair, she is confidently models an exquisite piece of lingerie. The soft, golden lighting illuminates her features, creating a warm and inviting atmosphere. Framed by a plain white background, the focus is solely on the model's captivating presence.
- Negative Prompt
- EasyNegative
Model description
iar
Trigger words
You should use ia to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for aztro/iatnat-ma
Base model
black-forest-labs/FLUX.1-dev