Instructions to use jplumail/matthieu-v2-pipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jplumail/matthieu-v2-pipe with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jplumail/matthieu-v2-pipe", 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
jplumail
end of training. runwayml/stable-diffusion-v1-5. vae from stabilityai/sd-vae-ft-mse. lr=5e-7. 4000 steps. batch size=2. mixed precision. trained text encoder. 1k reg images. 50 instance images.
4b77f27 Download text_encoder/pytorch_model.bin from jplumail/matthieu-v2-pipe: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/jplumail/matthieu-v2-pipe/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jplumail/matthieu-v2-pipe/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jplumail/matthieu-v2-pipe/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 76e630667814c0bebdd0ed62a593daf355bfc2a7e706966d9acbb051674df412
- Size of remote file:
- 492 MB
- SHA256:
- 53282a8375c1721781380216a38e63d0c9f57dc6e79e734ade76eb13df07ff71
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