Instructions to use tftgregrge/mpid-uber-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tftgregrge/mpid-uber-3000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tftgregrge/mpid-uber-3000", 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 text_encoder/pytorch_model.bin from tftgregrge/mpid-uber-3000: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/tftgregrge/mpid-uber-3000/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://tftgregrge/mpid-uber-3000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tftgregrge/mpid-uber-3000/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 2abb2c7f1a8c8952422752d893c14b541a7f122142ff29a70e6a2af15387120c
- Size of remote file:
- 246 MB
- SHA256:
- fb2e2a4ed2a0fcbe1b01c967dc2ed383c9004c5fb7acb4f3c571c74b1b4d9a59
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