Instructions to use mkshing/scedit-trained-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mkshing/scedit-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("mkshing/scedit-trained-xl", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sbu dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
SDXL SCEdit DreamBooth - mkshing/scedit-trained-xl
Model description
These are mkshing/scedit-trained-xl SC-Tuner adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
Special VAE used for training: None.
Trigger words
You should use a photo of sbu dog to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for mkshing/scedit-trained-xl
Base model
stabilityai/stable-diffusion-xl-base-1.0