Instructions to use BiliSakura/NiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/NiT-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("BiliSakura/NiT-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
Delete pipeline.py
Browse files- pipeline.py +0 -21
pipeline.py
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"""Custom pipeline entrypoint for Diffusers dynamic loading."""
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from .custom_pipeline.pipeline_nit import NiTPipeline
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from .custom_pipeline.scheduling_flow_match_nit import NiTFlowMatchScheduler
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from .custom_pipeline.transformer_nit import NiTTransformer2DModel
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try:
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import diffusers
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setattr(diffusers, "NiTPipeline", NiTPipeline)
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setattr(diffusers, "NiTTransformer2DModel", NiTTransformer2DModel)
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setattr(diffusers, "NiTFlowMatchScheduler", NiTFlowMatchScheduler)
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except Exception:
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pass
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__all__ = [
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"NiTPipeline",
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"NiTTransformer2DModel",
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"NiTFlowMatchScheduler",
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]
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