Instructions to use vladmandic/MicroDecoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladmandic/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("vladmandic/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
Commit ·
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Parent(s): ad7a32e
update readme
Browse filesSigned-off-by: Vladimir Mandic <mandic00@live.com>
README.md
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# MicroDecoder
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**MicroDecoder** VAE can be trained on *any* diffusion model in **~15min** and provides low-quality reconstruction from latents to RGB in **~0.01sec**.
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Inference includes noise correction based on current timestep, blurring corrections plus upscale interpolation: all with intention of providing as fast-as-possible reconstruction that is viable and consistent with any noise levels.
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# MicroDecoder
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**MicroDecoder** VAE can be trained on *any* diffusion model in **~5-15min** and provides low-quality reconstruction from latents to RGB in **~0.01sec**.
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Inference includes noise correction based on current timestep, blurring corrections plus upscale interpolation: all with intention of providing as fast-as-possible reconstruction that is viable and consistent with any noise levels.
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