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 Settings
- Draw Things
- DiffusionBee
| license: mit | |
| library_name: diffusers | |
| # Mann-E 4.2 Merged | |
| ## Technical Information about the model | |
| * Base Model : [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) | |
| * Merge : [mann-e/mann-e_4_rev-1-3](https://huggingface.co/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](https://github.com/prp-e/mann-e_training) and you can run it on your own as well. | |
| * Text encoder iterations : 1440 (number of pics times two in order to understand `mstyle` which 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. | |