Instructions to use esheremeteva06/material-herbarium-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use esheremeteva06/material-herbarium-lora 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("esheremeteva06/material-herbarium-lora") 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
Material Herbarium LoRA
SDXL LoRA trained on 20 historical botanical illustrations, manually selected and cropped to 512 × 512. Source collection: https://commons.wikimedia.org/wiki/Category:Thom%C3%A9,_Flora_von_Deutschland
Training: DreamBooth LoRA, 400 steps. Style token: matflora1885 style.
This repository contains LoRA weights only; the base SDXL model is required for inference.
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Model tree for esheremeteva06/material-herbarium-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0