Instructions to use DaveLoay/Riffusion_FineTuning_Tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DaveLoay/Riffusion_FineTuning_Tutorial with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DaveLoay/Riffusion_FineTuning_Tutorial", 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
- Local Apps
- Draw Things
- DiffusionBee
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README.md
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@@ -12,7 +12,6 @@ The porpuse of this work is to evaluate the performance of the model to generate
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## Notes
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* This is the way I found to achieve this goal, if you have a better idea for doing this, please share it with me.
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* If you download the whole repository it's about 100 GB, so I recommend leaving out the checkpoint directories.
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## Quickstart Guide
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## Notes
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* This is the way I found to achieve this goal, if you have a better idea for doing this, please share it with me.
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## Quickstart Guide
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