Instructions to use apletea/lora-trained-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use apletea/lora-trained-xl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("apletea/lora-trained-xl") prompt = "A photo of D26M4KZ man on table" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of D26M4KZ man license: openrail++
SDXL LoRA DreamBooth - apletea/lora-trained-xl

- Prompt
- A photo of D26M4KZ man on table

- Prompt
- A photo of D26M4KZ man on table

- Prompt
- A photo of D26M4KZ man on table

- Prompt
- A photo of D26M4KZ man on table
Model description
These are apletea/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use a photo of D26M4KZ man to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
- Downloads last month
- -