Stable Diffusion 3 Turbo Image to Image
Distilled, few-step version of Stable Diffusion 3 Image to Image
API
If you're looking for an API, you can choose from your desired programming language.
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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
# Use this function to convert a list of image URLs to base64
def image_urls_to_base64(image_urls):
return [image_url_to_base64(url) for url in image_urls]
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/stable-diffusion-3-turbo-img2img"
# Request payload
data = {
"mode": "image-to-image",
"image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/display_images/sd3-turbo-i2i-input.jpg"), # Or use image_file_to_base64("IMAGE_PATH")
"prompt": "cyberpunk style frog, dark colors",
"strength": 1,
"output_format": "jpeg",
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Type of mode.
Allowed values:
Input Image
Prompt to render
How much to transform the reference image
Output format.
Allowed values:
Base64 encoding of the output image.
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
Resources to get you started
Everything you need to know to get the most out of Stable Diffusion 3 Turbo Image to Image
Stable Diffusion 3 Turbo Image to Image
SD3 Turbo Image to Image is a distilled variant of Stable Diffusion 3 designed for efficient, high-quality image generation. By focusing on a smaller, optimized model, SD3 Turbo reduces computational overhead while retaining the core functionality of SD3. Here are key features:
- •
Few-Step Inference: The image generation process is condensed, leading to faster inference times compared to SD3.
- •
Targeted edits: You can provide an existing image and use text prompts to specify the desired changes. This allows for edits like adding or modifying colors, or applying different artistic styles.
- •
Versatility: It can be used for various image editing tasks, from simple tweaks to more creative manipulations.
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