Instructions to use Erog0291/reton-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Erog0291/reton-v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Erog0291/reton-v1") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Reton v1
Custom SDXL LoRA (rank 32) fine-tuned by MUTYINT on 480 luxury design images (web UI, flyers, posters, portraits). Trained 1500 steps on a Kaggle T4.
Usage
- Trigger word:
retonstyle(prepend to every prompt) - Settings: 30 steps, guidance 5.0, 1024x1024
- Negative: blurry, low quality, watermark, text artifacts, distorted, amateur, cluttered
- Load with PEFT: keys are
unet.base_model.model.*format; strip theunet.prefix and useset_peft_model_state_dict, orpipe.load_lora_weightson older diffusers.
Example prompt: retonstyle, luxury event flyer for a Lagos tech summit, dark and gold, elegant serif typography, premium minimal layout
Sample images in samples/.
- Downloads last month
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Model tree for Erog0291/reton-v1
Base model
stabilityai/stable-diffusion-xl-base-1.0