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Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model with Adversarial Diffusion Distillation (ADD) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps. Powered By Stability AI. Stable Diffusion 3.5 Medium is deployed as a Flumina app. See for more details: https://huggingface.co/Cyfuture AI-ai/stable-diffusion-3.5-medium-flumina

Python
import requests
  import json
  
  url = "https://api.cyfuture.ai/aiapi/inferencing/response"
  payload = {
      "model": "Model Name",
      "max_tokens": 16384,
      "top_p": 1,
      "top_k": 40,
      "presence_penalty": 0,
      "frequency_penalty": 0,
      "temperature": 0.6,
      "messages": [
          {
              "role": "user",
              "content": "Hello, how are you?"
          }
      ]
  }
  headers = {
      "Accept": "application/json",
      "Content-Type": "application/json",
      "Authorization": "Bearer <API_KEY>"
  }
  response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
await fetch("https://api.cyfuture.ai/aiapi/inferencing/response", {
      method: "POST",
      headers: {
          "Accept": "application/json",
          "Content-Type": "application/json",
          "Authorization": "Bearer <API_KEY>"
      },
      body: JSON.stringify({
          model: "Model Name",
          max_tokens: 16384,
          top_p: 1,
          top_k: 40,
          presence_penalty: 0,
          frequency_penalty: 0,
          temperature: 0.6,
          messages: [
              {
                  role: "user",
                  content: "Hello, how are you?"
              }
          ]
      })
  });
URI uri = URI.create("https://api.cyfuture.ai/aiapi/inferencing/response");
  HttpClient client = HttpClient.newHttpClient();
  
  HttpRequest request = HttpRequest.newBuilder()
      .uri(uri)
      .header("Accept", "application/json")
      .header("Content-Type", "application/json")
      .header("Authorization", "Bearer <API_KEY>")
      .POST(HttpRequest.BodyPublishers.ofString("{...}"))
      .build();
  
  HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
package main
  
  import (
      "bytes"
      "net/http"
      "fmt"
  )
  
  apiUrl := "https://api.cyfuture.ai/aiapi/inferencing/response"
  
  req, err := http.NewRequest("POST", apiUrl, bytes.NewBuffer(jsonData))
  req.Header.Set("Accept", "application/json")
  req.Header.Set("Content-Type", "application/json")
  req.Header.Set("Authorization", "Bearer <API_KEY>")
  
  client := &http.Client{}
  resp, err := client.Do(req)
  fmt.Println("response Status:", resp.Status)
curl --request POST \
      --url https://api.cyfuture.ai/aiapi/inferencing/response \
      -H 'Accept: application/json' \
      -H 'Content-Type: application/json' \
      -H 'Authorization: Bearer <API_KEY>' \
      --data '{
          "model": "Model Name",
          "max_tokens": 16384,
          "top_p": 1,
          "top_k": 40,
          "temperature": 0.6
      }'
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On-demand deployments

On-demand deployments allow you to use Stable Diffusion 3.5 Medium on dedicated GPUs with Cyfuture AI' high-performance serving stack with high reliability and no rate limits.

See the On-demand deployments guide for details.

Model Details

Created by
[email protected]
Created
12/30/2024
Visibility
Public
Kind
Base model
Model size
14B parameters