Instructions to use AiAF/D-ART-18DART5_LoRA_Flux1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AiAF/D-ART-18DART5_LoRA_Flux1 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("AiAF/D-ART-18DART5_LoRA_Flux1") prompt = "D-ART \\(Artist\\), @18dart5, @18dart3, @18dart2, @18dart1, illustration of a woman with blonde hair, wearing a black lace bra, She has a small silver heart-shaped pendant necklace around her neck. She is looking directly at the camera with a neutral expression. The background is a plain, light-colored wall. The image is high quality ." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Ctrl+K
/D-ART-18DART5_LoRA_Flux1 Repo update. Uploading the following: Sample images of training steps 2000-4000 session. LoRA file. README.md, _latent_cache archive, aptimizer.pt archive, config.yaml file. Itterations archive (2000 steps - 4000 steps LoRA File.). Style accuracy while minimizing anatomical degradation seems to have improved in this session. I`ve also just not realized that the last major commit message I uploaded the update incorectly named `PixelNinjaArt_LoRA_Flux1 Repo update.` instead of `/D-ART-18DART5_LoRA_Flux1`. I hope that doesn`t caue any confusion later on.
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