Instructions to use 43ntropy/NEvo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 43ntropy/NEvo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("43ntropy/NEvo", 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
| from stimulus_synthesis.spaces import Candidate, PromptSearchSpace | |
| def test_prompt_space_decode_mutate_and_crossover(): | |
| space = PromptSearchSpace( | |
| prompt_banks={ | |
| "subject": ["person", "face"], | |
| "action": ["running", "walking"], | |
| } | |
| ) | |
| cand = Candidate((0, 1)) | |
| assert space.decode(cand) == "person walking" | |
| assert len(space.mutate(cand, mutation_rate=1.0).genes) == 2 | |
| child_a, child_b = space.crossover(Candidate((0, 0)), Candidate((1, 1)), crossover_rate=1.0) | |
| assert len(child_a.genes) == 2 | |
| assert len(child_b.genes) == 2 | |