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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')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/sd1.5-img2img"
# Request payload
data = {
"image": image_url_to_base64("https://www.segmind.com/sd-img2img-input.jpeg"), # Or use image_file_to_base64("IMAGE_PATH")
"samples": 1,
"prompt": "A fantasy landscape, trending on artstation, mystical sky",
"negative_prompt": "nude, disfigured, blurry",
"scheduler": "DDIM",
"num_inference_steps": 25,
"guidance_scale": 10.5,
"strength": 0.75,
"seed": 98877465625,
"img_width": 512,
"img_height": 512,
"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
Input Image.
Number of samples to generate.
min : 1,
max : 4
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 0.1,
max : 25
How much to transform the reference image
min : 0.1,
max : 1
Seed for image generation.
Image resolution.
Allowed values:
Image resolution.
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.
Stable Diffusion Img2Img
Stable Diffusion Img2Img is a transformative AI model that's revolutionizing the way we approach image-to-image conversion. This model harnesses the power of machine learning to turn concepts into visuals, refine existing images, and translate one image to another with text-guided precision. It's an invaluable asset for creatives, marketers, and developers seeking to push the boundaries of digital imagery.
At the heart of Stable Diffusion Img2Img is a robust algorithm capable of understanding and manipulating visual content at a granular level. It takes an existing image and, guided by textual prompts, morphs it into a new creation that aligns with the user's vision. This model excels in tasks such as style transfer, detail enhancement, and subject transformation, all while maintaining the integrity of the original composition.
Advantages
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Text-Guided Imagery: Integrates textual prompts to steer the image transformation process, ensuring outputs are aligned with user intent.
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Seamless Style Transfers: Adapts the style of one image to another, enabling a smooth transition that feels natural and intentional..
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Detail Enhancement:Amplifies the details within images, bringing clarity and vibrance to visual elements.
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Creative Flexibility: Offers a wide range of possibilities, from subtle alterations to complete thematic overhauls..
Use Cases
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Creative Artwork: Artists can evolve their work, experimenting with different styles and motifs without starting from scratch.
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Marketing Material: Marketers can tailor images to fit brand narratives, ensuring consistency across campaigns.
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Product Design: Designers can visualize product variations quickly, streamlining the development process.
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Entertainment Media: Content creators in film and gaming can modify and enhance visual assets to fit evolving storylines.
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Educational Tools: Educators can create custom visuals to aid in teaching complex concepts.
Popular Models
