Instructions to use PlixAI/BitDiffusionV0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PlixAI/BitDiffusionV0.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PlixAI/BitDiffusionV0.1", dtype=torch.bfloat16, device_map="cuda") prompt = "Three cow grazing in a bay window" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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@@ -23,6 +23,16 @@ This is the initial version of the image model trained on the Bittensor network
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**Trained on the dataset of Subnet 19 Vision.**
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## Settings for BitDiffusionV0.1
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Use these settings for the best results with BitDiffusionV0.1:
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**Trained on the dataset of Subnet 19 Vision.**
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## Subnet 17 Checkpoint
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Model ID : gtsru/sn17-dek-012
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Revision : 5852d39e8413a377a3477b8278ade9af311f83a4
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UID : 42
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Perplexity : 1.1325
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## Settings for BitDiffusionV0.1
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Use these settings for the best results with BitDiffusionV0.1:
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