Text-to-Image
Diffusers
StableDiffusionPipeline
stablediffusionapi.com
stable-diffusion-api
ultra-realistic
Instructions to use Muapi/simpsons with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/simpsons 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/simpsons", 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
Add README
Browse files
README.md
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---
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license: openrail++
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library_name: diffusers
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tags:
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- text-to-image
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- stable-diffusion
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- sd-1.5
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pipeline_tag: text-to-image
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---
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# Simpsons
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**Base model**: SD 1.5
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Originally published by [stablediffusionapi](https://huggingface.co/stablediffusionapi).
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Mirrored here for use with [muapi.ai](https://muapi.ai) — a unified API for generative media.
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## 🧠 Usage via muapi.ai
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🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
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```python
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import requests, os
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url = "https://api.muapi.ai/api/v1/sd-image"
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headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
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payload = {
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"prompt": "masterpiece, best quality",
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"model": "simpsons",
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"width": 512,
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"height": 512,
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"num_images": 1
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}
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print(requests.post(url, headers=headers, json=payload).json())
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```
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