Instructions to use neinah/tingtel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neinah/tingtel 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("neinah/tingtel") prompt = "Screenshot" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
tingtel
- Prompt
- Screenshot
- Negative Prompt
- blurry, low quality, low resolution, deformed, disfigured, bad anatomy, extra limbs, watermark, text, logo, oversaturated,
Model description
LoRA fine-tuned on Stable Diffusion XL (SDXL) base model. Trained on a custom dataset to generate images of "tingtel." Use trigger word "tingtel" in prompts. Trained for 10 epochs at rank 8.
Trigger words
You should use tingtel to trigger the image generation.
Download model
Download them in the Files & versions tab.
- Downloads last month
- 8
Model tree for neinah/tingtel
Base model
stabilityai/stable-diffusion-xl-base-1.0