Instructions to use TornikeO/Future-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TornikeO/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("TornikeO/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
Download unet/diffusion_pytorch_model.bin from TornikeO/Future-Diffusion: direct link, hf CLI and curl.
- Browser
- Download file 1.73 GB
-
https://huggingface.co/TornikeO/Future-Diffusion/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://TornikeO/Future-Diffusion/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/TornikeO/Future-Diffusion/resolve/main/unet/diffusion_pytorch_model.bin
1.73 GB
- Xet hash:
- 5e358a32c8c0a360cc6da1cdb04b444174dbf82172d4b85b58dd5946d4bec333
- Size of remote file:
- 1.73 GB
- SHA256:
- e60e4107727bf4aeb739b50065e8dbe6892a803cd681cecb3238c32c581b3fe5
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