Instructions to use mzx/NEvo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mzx/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("mzx/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
Commit History
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Add image-only pipeline mode
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Expose encoder input resolution as score_size (default 224)
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Per-stage eval budgets image_max_evals/video_max_evals (200/100)
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Split into image_num_generations (50) / video_num_generations (25)
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Replace num_candidates/max_evals with num_generations (population_size + num_generations)
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Replace num_rounds with population_size (default 20), matching clean_search
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Align two-stage search with canonical: clean scoring, art_data stage-1, image/video batching
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Two-stage pipeline (image search then motion search) with per-stage budgets
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ROI-enhanced search space + docs; EBA caption
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Publish self-contained NEVO stimulus-synthesis pipeline + model card
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