Instructions to use mann-e/mann-e_4-2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mann-e/mann-e_4-2-merged with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mann-e/mann-e_4-2-merged", 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
- Draw Things
- DiffusionBee
metadata
license: mit
library_name: diffusers
Mann-E 4.2 Merged
Technical Information about the model
- Base Model : runwayml/stable-diffusion-v1-5
- Merge : mann-e/mann-e_4_rev-1-3
- Merge amount : %70 fine-tuned SD 1.5 (or Mann-E version 4.2 base) and %30 of Mann-E 4.1.3 in order to get the old styles such as Model Shoot, Elden Ring, Arcane, Analog Style and GTA V Style. Also this merge can be helpful for Midjourney version 4 style artwork as well.
Training process
The code for pre-processing data and fine-tuning the model is available in this repository and you can run it on your own as well.
- Text encoder iterations : 1440 (number of pics times two in order to understand
mstylewhich can give the user a Midjourney version 5 vibe). - Stable Diffusion iterations : 16000 iterations for one epoch
- Time: around 4 hours on a single T4 GPU.