Instructions to use danbrown/MeinaMix-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use danbrown/MeinaMix-v9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("danbrown/MeinaMix-v9", 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 danbrown/MeinaMix-v9: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/danbrown/MeinaMix-v9/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://danbrown/MeinaMix-v9/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/danbrown/MeinaMix-v9/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- faa4a31fb812514a064adc294a1f2effeaf1345a3d438a6f2b9682a6544e55fc
- Size of remote file:
- 492 MB
- SHA256:
- 65c4ab8b5606143025ce5bf798b373f520aa132a65e358609f94978926fcfc9b
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