Instructions to use fal/Bernini-R-Aux-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Bernini-R-Aux-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Bernini-R-Aux-FlashPack", 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
FlashPack conversion manifest for ByteDance/Bernini-R-Diffusers@de8c4621d3ac75cc33efe3db8deaed2023e9ac8c
Browse files- flashpack_conversion.json +26 -0
flashpack_conversion.json
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{
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"epoch": 4,
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"source_repo": "ByteDance/Bernini-R-Diffusers",
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"source_revision": "de8c4621d3ac75cc33efe3db8deaed2023e9ac8c",
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"dtype": "keep",
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"components": [
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"text_encoder",
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"vae"
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],
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"components_only": true,
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"variant": null,
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"include_root": false,
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"ignore_names": [],
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"ignore_prefixes": [],
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"ignore_suffixes": [],
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"files": null,
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"pack_files": null,
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"pickle_key": null,
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"rename_prefixes": null,
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"include_names": null,
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"include_prefixes": null,
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"include_suffixes": null,
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"script_sha256": "0d39c755c7e9476e2879cf63682143c06753a24dc3810ce489f25b25e1ad0586",
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"n_packs": 2,
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"total_pack_bytes": 11869438887
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}
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