Instructions to use Muapi/chilloutmix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/chilloutmix with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Muapi/chilloutmix", 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
File size: 853 Bytes
e35f364 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- sd-1.5
pipeline_tag: text-to-image
---
# ChilloutMix
**Base model**: SD 1.5
Originally published by [stablediffusionapi](https://huggingface.co/stablediffusionapi).
Mirrored here for use with [muapi.ai](https://muapi.ai) — a unified API for generative media.
## 🧠 Usage via muapi.ai
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/sd-image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality",
"model": "chilloutmix",
"width": 512,
"height": 512,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
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