Instructions to use nightknocker/Anima-6.66b-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nightknocker/Anima-6.66b-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nightknocker/Anima-6.66b-diffusers", 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
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Credits to Gazingstars123 for the detailed documentation about the expansion.
The model is provided as it is, no further updates are planned.
If you want to make it compatible with the inference engine, copy the missing keys from the previous complete layer that is closest to it.
Don't be a moron - share the missing keys between the blocks during training.
In its current form, the output should be exactly the same as the 2B model.
This is the opposite of what I have been doing for years, which is shrinking and looping through the model layers.
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
