Instructions to use MidnightRunner/manuka_fudge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MidnightRunner/manuka_fudge with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MidnightRunner/manuka_fudge", dtype=torch.bfloat16, device_map="cuda") prompt = "male, 1male, aesthetic, realistic, realism, strength, assertiveness, competitiveness, masterpiece, best quality, ultra-detailed, ((solo)), nature, (stars, crescent moon), (full body photo:1.3), (colorful background:1.2), spotlight backlit, psychedelic backdrop, realist detail, standing" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Mānuka Fudge - Base Model
Model description
This checkpoint went through three versions itself with a few merges. This is the fourth version.
Sampling method:
- DPM++ 2M Karras
- DPM++ 2M SDE Karras
Sampling Steps: 25-45 CFG Scale: 7 Clip Skip: 2
Recommended Upscaler Settings:
- Hires. fix Upscaler
- 4x-UltraSharp
Hires steps: 10-20 Hires upscale: 1.5-2 Denoising strength: ~ 0.3-0.5
For better results use Hires.fix for better Results! Use ADetailer for a better face with lora:Pear_v1:0.8!
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Model tree for MidnightRunner/manuka_fudge
Base model
runwayml/stable-diffusion-v1-5