Instructions to use Heasterian/AsymmetricAutoencoderKLUpscaler_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Heasterian/AsymmetricAutoencoderKLUpscaler_v2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Heasterian/AsymmetricAutoencoderKLUpscaler_v2", 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
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license: mit
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license: mit
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base_model:
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- ostris/vae-kl-f8-d16
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Another take on upscaler using AsymmetricAutoencoderKL, in this case I did re-use decoder of ostris/vae-kl-f8-d16 as decoder and trained just encoder.
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Oversharpening was reduced by usage of sobel in loss calculation. I guess that without high resolution image used as reference, it might look more blury than previous one.
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