View source for Runwayml/stable-diffusion-v1-5 model

{{Model infobox
| hugging-face-uri = runwayml/stable-diffusion-v1-5
| creator = 
| type = Multimodal
| task = Text-to-Image
| library = Diffusers
| dataset = 
| language = 
| paper = 
| license = arxiv:2207.12598, arxiv:2112.10752, arxiv:2103.00020, arxiv:2205.11487, arxiv:1910.09700, creativeml-openrail-m
| related-to = stable-diffusion, stable-diffusion-diffusers
| all-tags = Text-to-Image, Diffusers, stable-diffusion, stable-diffusion-diffusers, arxiv:2207.12598, arxiv:2112.10752, arxiv:2103.00020, arxiv:2205.11487, arxiv:1910.09700, License: creativeml-openrail-m
| all-lang-tags = 
}}

==Model Description==

==Clone Model Repository==
<tabber>
|-|HTTPS=
<pre>
#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
</pre>
  
|-|SSH=
<pre>
#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone git@hf.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
</pre>
</tabber>

==Hugging Face Transformers Library==
<pre>
undefined
</pre>

==Deployment==
===Inference API===
<tabber>
|-|Python=
<pre>
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))
</pre>
|-|JavaScript=
<pre>
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
});
</pre>
|-|cURL=
<pre>
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}"

</pre>
</tabber>

===Amazon SageMaker===
<tabber>
</tabber>

===Spaces===
<pre>
import gradio as gr

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

==Training==
===Amazon SageMaker===
<tabber>
</tabber>

==Model Card==