Instructions to use aakashrajaraman/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aakashrajaraman/output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aakashrajaraman/output", dtype=torch.bfloat16, device_map="cuda") prompt = "<new1> photo of traffic" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_custom_diffusion_weights.bin from aakashrajaraman/output: direct link, hf CLI and curl.
- Browser
- Download file 76.7 MB
-
https://huggingface.co/aakashrajaraman/output/resolve/main/pytorch_custom_diffusion_weights.bin
- Command line
-
hf download hf://aakashrajaraman/output/pytorch_custom_diffusion_weights.bin
-
curl -L -o pytorch_custom_diffusion_weights.bin https://huggingface.co/aakashrajaraman/output/resolve/main/pytorch_custom_diffusion_weights.bin
76.7 MB
- Xet hash:
- 3d4e7d3472a480bd25df44757ba69e8645b0b91935ea8e0e4238a5e96accef89
- Size of remote file:
- 76.7 MB
- SHA256:
- 19fbd4e2202514842e8e4ba900d3bae5ff029ccc18846f50468cd09abdf7233f
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