Serverless GPU inference for ML models

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import os
from pipeline.cloud.pipelines import run_pipeline

os.environ["PIPELINE_API_TOKEN"] = "YOUR_TOKEN"

output = run_pipeline(
	#pipeline pointer or ID
	"stabilityai/stable-diffusion-xl-refiner-1.0:v1",
	#:Prompt
	"a cool dog, holding a coffee wearing a comfy hoodie",
	#:Model kwargs
	dict(
		denoising_end = 0.8,
		num_inference_steps = 25,
	),
	async_run = False,
)

print(output.result.result_array())

const body = {
  pipeline_id_or_pointer: "stabilityai/stable-diffusion-xl-refiner-1.0:v1",
  input_data: [
    {
      type: "string",
      value: "a cool dog, holding a coffee wearing a comfy hoodie",
    },
    {
      type: "dictionary",
      value: {
        denoising_end: 0.8,
        num_inference_steps: 25,
      },
    },
  ],
  async_run: false,
};

const res = await fetch("https://www.mystic.ai/v3/runs/anonymous", {
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify(body),
  method: "POST",
});

curl -X POST 'https://www.mystic.ai/v3/runs' 
--url http://mystic.ai/v3/runs 
--header 'Authorization: Bearer YOUR_TOKEN' 
--header 'Content-Type: application/json' 
--data '{
	"pipeline_id_or_pointer": "stabilityai/stable-diffusion-xl-refiner-1.0:v1",
	"async_run": false,
	"input_data":
		[
			{
				"type": "string",
				"value": "a cool dog, holding a coffee wearing a comfy hoodie"
			},
			{
				"type": "dictionary",
				"value": {
					"denoising_end": 0.8,
					"num_inference_steps": 25
				}
			}
		]
	}
'