Instructions to use SD-Next/MicroDecoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SD-Next/MicroDecoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SD-Next/MicroDecoder", 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 Settings
- Draw Things
- DiffusionBee
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## Models
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MicroDecoder includes following pre-trained models:
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And each VAE model can be used for different base models as long as they share the same VAE architecture
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### VAE to Model Map
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## Models
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MicroDecoder includes following pre-trained models:
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- `sd, sdxl, f1, f2, sd3, wan21, wan22, minimaxh3, qwen21, dcae, mage, cog, hy`
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And each VAE model can be used for different base models as long as they share the same VAE architecture
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### VAE to Model Map
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