9min
Optimize

Optimize your Stable Diffusion Models for deploymentSegmind's stable diffusion optimize API helps optimize the performance of your SD 1.4 and 1.5 models, for production. The API compiles the model into a GPU accelerated format, which helps in faster inference, better CUDA core utilization and optimal memory footprint. 
Users can send their checkpoint files, LoRA, PyTorch (.ckpt, LoRA, .bin) files for optimization, select the GPU and optimization parameters and Segmind will convert them into highly optimized .plan files or .so files for optimal production deployment on NVIDIA GPUs. Below is how to use the API to optimize your models.
Optimize modelUse this endpoint to submit an optimization request for a huggingface model. The dynamic field 
POST/optimize/v1
Request body
# Model name in HuggingFace﻿
﻿model: str

# Set to false if image dimensions need to be specified.
# Default: True
dynamic: bool
﻿
# If both height and width is set to zero, 
# Min/Max: 256-1024
height: int
﻿weight: int

# GPU to optimize for; Choose from: ['A10','A100']
gpu: str﻿

# Model type; ﻿Choose from ['INPAINT', 'IMAGETTOIMAGE', 'TEXTTOIMAGE']﻿
type: str﻿

Response﻿
Response:
    # Job URL for polling. Eg: "https://api.segmind.com/job/<job_id>"
    job_link: str﻿﻿﻿﻿

Example
Python
Curl
|

import requests

endpoint_url = "https://api.segmind.com/optimize/v1/"

response = requests.post(
    endpoint_url,
    headers={
        "Content-Type": "application/json",
        "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
    },
    json={
        "model_path": "CompVis/stable-diffusion-v1-4"
        "height": "512"
        "width": "512"
        "dynamic": "False"
        "gpu": "A10"
        "model_type": "TEXTTOIMAGE"
    }
)

    job_link = response.json("job_link")

import requests

endpoint_url = "https://api.segmind.com/optimize/v1/"

response = requests.post(
    endpoint_url,
    headers={
        "Content-Type": "application/json",
        "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
    },
    json={
        "model_path": "CompVis/stable-diffusion-v1-4"
        "height": "512"
        "width": "512"
        "dynamic": "False"
        "gpu": "A10"
        "model_type": "TEXTTOIMAGE"
    }
)

    job_link = response.json("job_link")
﻿
Job StatusThis endpoint provides the job status for the optimization request.
GET/status/v1/<job_id>/
Response
Response:
    ﻿# Status of the job
    # Can be any of ["PENDING", "PROCESSING", "COMPLETED", "FAILED"]﻿
    status: str﻿﻿﻿﻿
    # Will be empty string till job status is "COMPLETED"
    download_link: str

Example
Python
Curl
|

import requests
from time import sleep

job_link = "https://api.segmind.com/status/v1/311c158d"

# Add an initial delay for 30 mins if processing synchronously
# sleep(30*60*60)

status = "PENDING"

while(status not in ["COMPLETED, FAILED"]) {

    response = requests.get(
        endpoint_url,
        headers={
            "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
        }
    )

    status = response["status"]

    # 10s interval
    sleep (10);

}

if status == "COMPLETED" {
    download_link = response["download_link"]
}

import requests
from time import sleep

job_link = "https://api.segmind.com/status/v1/311c158d"

# Add an initial delay for 30 mins if processing synchronously
# sleep(30*60*60)

status = "PENDING"

while(status not in ["COMPLETED, FAILED"]) {

    response = requests.get(
        endpoint_url,
        headers={
            "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
        }
    )

    status = response["status"]

    # 10s interval
    sleep (10);

}

if status == "COMPLETED" {
    download_link = response["download_link"]
}
﻿
List Jobs APIUse this endpoint to get a list of all optimization requests.
GET/jobs/v1/
Request Params
Allows filter by the following fields
# Model name in HuggingFace﻿
﻿model: str﻿

# GPU Type: ["A10" or "A100"]﻿
gpu: ﻿str

# Model TYPE. One of ["INPAINT", "IMAGETTOIMAGE", "TEXTTOIMAGE"]﻿
model_type: str﻿
﻿
Response
﻿Response:

  # count of objects
  count: int
﻿
  # List of jobs
  data: list(object {
    job_id: str ,﻿
    status: str ,
    created_at: str﻿﻿
   })﻿
﻿
Example
Python
Curl
|

import requests
from time import sleep

endpoint = "https://api.segmind.com/jobs/v1/"

response = requests.get(
    endpoint_url,
    headers={
        "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
    }
)

for jobs in response["data"]:
    # do something here ...


import requests
from time import sleep

endpoint = "https://api.segmind.com/jobs/v1/"

response = requests.get(
    endpoint_url,
    headers={
        "X-Segmind-Access-Token": "<YOUR ACCESS TOKEN>",
    }
)

for jobs in response["data"]:
    # do something here ...
﻿
﻿
Updated 14 Mar 2023
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TABLE OF CONTENTS
Optimize your Stable Diffusion Models for deployment
Optimize model
Job Status
List Jobs API