Instructions to use Aybeeceedee/knollingcase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aybeeceedee/knollingcase with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aybeeceedee/knollingcase", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
knollingcase Dreambooth model trained by Aybeeceedee with TheLastBen's fast-DreamBooth notebook
Use 'knollingcase' anywhere in the prompt and you're good to go.
Example: knollingcase, isometic render, a single cherry blossom tree, isometric display case, knolling teardown, transparent data visualization infographic, high-resolution OLED GUI interface display, micro-details, octane render, photorealism, photorealistic
Example: (clockwork:1.2), knollingcase, labelled, overlays, oled display, annotated, technical, knolling diagram, technical drawing, display case, dramatic lighting, glow, dof, reflections, refractions