Instructions to use JosephusCheung/RuminationDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/RuminationDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/RuminationDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, anime, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
widget:
- text: >-
masterpiece, best quality, anime, 1girl, brown hair, green eyes, colorful,
autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer
example_title: anime 1girl
- text: >-
masterpiece, best quality, anime, 1boy, brown hair, green eyes, colorful,
autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer
example_title: anime 1boy
This is only a test model, not recommended, please don't use it directly!
Fine-tuned off Stable Diffusion v2-1_768-nonema-pruned.ckpt.
