This model uses diffusion-denoising mechanism as first proposed by SDEdit, Stable Diffusion is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers
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')
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 image
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.
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
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.
Text-Guided Imagery: Integrates textual prompts to steer the image transformation process, ensuring outputs are aligned with user intent.
Seamless Style Transfers: Adapts the style of one image to another, enabling a smooth transition that feels natural and intentional..
Detail Enhancement:Amplifies the details within images, bringing clarity and vibrance to visual elements.
Creative Flexibility: Offers a wide range of possibilities, from subtle alterations to complete thematic overhauls..
Creative Artwork: Artists can evolve their work, experimenting with different styles and motifs without starting from scratch.
Marketing Material: Marketers can tailor images to fit brand narratives, ensuring consistency across campaigns.
Product Design: Designers can visualize product variations quickly, streamlining the development process.
Entertainment Media: Content creators in film and gaming can modify and enhance visual assets to fit evolving storylines.
Educational Tools: Educators can create custom visuals to aid in teaching complex concepts.
SDXL Img2Img is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training