Instructions to use nitrosocke/Future-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/Future-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("nitrosocke/Future-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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Add weights for Future-Diffusion
nitrosocke3042cdf
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Updated the correct sample_size from stabilityai/stable-diffusion-2-base
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Correct `sample_size` of Stable Diffusion 2's unet to have correct width and height default (#10)
- Correct `sample_size` of Stable Diffusion 2's unet to have correct width and height default (12cf6eb616c5848d5ada0dd482aee7d12d03d5af) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>bbfc803
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Removed the ckpt file as it is not working due to the conversion script used
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Upload future-diffusion-thumbnail-2.jpg
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Update README.md
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Delete images/future-diffusion-thumbnail.jpg
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Upload future-diffusion-thumbnail-2.jpg
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Update README.md
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Upload future-diffusion-thumbnail.jpg
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Uploading Diffusers
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Update README.md
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Update README.md
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Create images/test.txt
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Upload future-diffusion-v1.ckpt
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initial commit
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