Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use digiplay/MGM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use digiplay/MGM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("digiplay/MGM", 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 digiplay/MGM: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/digiplay/MGM/resolve/refs%2Fpr%2F1/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://digiplay/MGM@refs/pr/1/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/digiplay/MGM/resolve/refs%2Fpr%2F1/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- b1fbc222fd82d826688955bc4d6c6d7d16d26f38f568f5be16d05fde77f4a016
- Size of remote file:
- 492 MB
- SHA256:
- a217e8eb856c738a2d0f5b64a6d44179f8b1ca4c6b40c9ecdc59f4239d13137d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.