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
Test the concept via A1111 Colab fast-Colab-A1111
Or you can run your new concept via diffusers Colab Notebook for Inference
Sample pictures of this concept:
01931-2623739578-steampunk
01717-83533703-knollingcase,
01859-2749987773-knollingcase,
01679-3206223110-knollingcase,
01985-3961032444-(knollingcase
01973-4286255503-knollingcase,
01743-2973635670-knollingcase,
01932-3921352636-clockwork
01647-3325571965-knollingcase,
01810-4148395994-knollingcase,
,_a_hamburger,_display_case,_labelled,_overlays,_oled_display,_annotated,_annotations,_technical,_knolling,_di.png)





,_a_hamburger,_display_case,_labelled,_overlays,_oled_display,_annotated,_annotations,_technical,_knolling,_di.png)



