Instructions to use Erog0291/reton-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Erog0291/reton-v2 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-v2") 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 v2
Custom SDXL LoRA trained at 1024px for 3000 steps. Trigger word: retonstyle.
Recommended: 30 inference steps, guidance scale 5.0, 1024x1024. Negative prompt: blurry, low quality, watermark, text artifacts, distorted, amateur, cluttered.
LoRA weights use PEFT-style keys (unet.base_model.model.*). Four generated sample images are in samples/.
Note: SDXL can create attractive designs but exact typography and spelling remain less reliable than purpose-built modern image generation models. For production graphics, overlay final text with a typography/layout tool.
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Model tree for Erog0291/reton-v2
Base model
stabilityai/stable-diffusion-xl-base-1.0