Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
sadxzero
Instructions to use Yntec/Luma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/Luma with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/Luma", 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
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Download README.md from Yntec/Luma: direct link, hf CLI and curl.
- Browser
- Download file 276 Bytes
-
https://huggingface.co/Yntec/Luma/resolve/5d843ffc1a928c15cc1bdce737f026522e2923b9/README.md
- Command line
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hf download hf://Yntec/Luma@5d843ffc1a928c15cc1bdce737f026522e2923b9/README.md
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curl -L -o README.md https://huggingface.co/Yntec/Luma/resolve/5d843ffc1a928c15cc1bdce737f026522e2923b9/README.md
276 Bytes
metadata
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- sadxzero
SXZ Luma 0.98 VAE
Original pages: