Instructions to use Linaqruf/hitokomoru-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Linaqruf/hitokomoru-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Linaqruf/hitokomoru-diffusion", 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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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#4)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (52a85c7244bb10e4de0d75f2a65b3e9352948dd4)5a70775
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Add `scale_factor` to vae config. (#3)
- Add `scale_factor` to vae config. (a47ee37a2c3639d7efab142ac63110d7811e295b) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>9f202f1
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Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (#1)
- Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default (365880d698ac4f7a34e9bfeb9b97996bb9e7d76f) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>8acb31c
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upload 25k step
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upload 25k step
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upload 25k step
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Update README.md
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Update README.md
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Add prompt and setting for example images
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Upload example images for hitokomoru-15000.ckpt and hitokomoru-20000.ckpt
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Add folder to store example images
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Upload 4 files
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add diffuser and inference based on hitokomoru-20000-pruned.ckpt
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Upload diffusers weights
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Upload 15k and 20k
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Delete last-state
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add sample: human
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Fix: Wrong commit message, actually trained model
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Fix: Wrong commit message, actually trained model
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Push: Rename Merged Model
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Push: hitokomoru-10000
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Push: hitokomoru-5000
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initial commit
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