Instructions to use segmind/tiny-sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use segmind/tiny-sd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("segmind/tiny-sd", 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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@@ -46,3 +46,10 @@ These are the key hyperparameters used during training:
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* Image resolution: 512
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* Mixed-precision: fp16
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* Image resolution: 512
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* Mixed-precision: fp16
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## Speed Comparision
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We have observed that the distilled models are upto 85% faster than the Base SD1.5 Models. Below is a comparision
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