Instructions to use tuwonga/dbluth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tuwonga/dbluth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tuwonga/dbluth", 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 History
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Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default
Since `diffusers==0.9.0` the width and height is automatically inferred from the `sample_size` attribute of your unet's config. It seems like your diffusion model has the same architecture as Stable Diffusion 1 which means that when using this model, by default an image size of 512x512 should be generated. This in turn means the unet's sample size should be **64**. In order to suppress to update your configuration on the fly and to suppress the deprecation warning added in this PR: https://github.com/huggingface/diffusers/pull/1406/files#r1035703505 it is strongly recommended to merge this PR.3585802
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Update README.md
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Add Diffusers weights (#1)
- Add Diffusers weights (3d42bb11fc24228a0c71d0fe89024e1b83dce4fd)7b2151d -
Update README.md
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Upload dbluth.ckpt
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Upload 2 files
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initial commit
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