Runwayml/stable-diffusion-v1-5 model

Runwayml/stable-diffusion-v1-5 model is a Multimodal model used for Text-to-Image.

Model Description

Clone Model Repository

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone [email protected]:runwayml/stable-diffusion-v1-5
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

Hugging Face Transformers Library

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Deployment

Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.content
image_bytes = query({
	"inputs": "Astronaut riding a horse",
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))

async function query(data) {
	const response = await fetch(
		"https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
		{
			headers: { Authorization: "Bearer {API_TOKEN}" },
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.blob();
	return result;
}
query({"inputs": "Astronaut riding a horse"}).then((response) => {
	// Use image
});

curl https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5 \
	-X POST \
	-d '{"inputs": "Astronaut riding a horse"}' \
	-H "Authorization: Bearer {API_TOKEN}"

Amazon SageMaker

Spaces

import gradio as gr

gr.Interface.load("models/runwayml/stable-diffusion-v1-5").launch()

Training

Amazon SageMaker

Model Card