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')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/sd1.5-controlnet-softedge"
# Request payload
data = {
"image": image_url_to_base64("https://segmind.com/soft-edge-input.jpeg"), # Or use image_file_to_base64("IMAGE_PATH")
"samples": 1,
"prompt": "royal chamber with fancy bed",
"negative_prompt": "Disfigured, cartoon, blurry, nude",
"scheduler": "UniPC",
"num_inference_steps": 25,
"guidance_scale": 7.5,
"strength": 1,
"seed": 131487365682,
"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.
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.
ControlNet SoftEdge
ControlNet SoftEdge model is designed to enhance diffusion models with added conditions. Going beyond traditional contours, ControlNet Softedge offers a refined approach, emphasizing the preservation of essential features while minimizing the prominence of brush strokes, leading to captivating visuals that resonate with depth and subtlety.
At the core of ControlNet Softedge lies an intricate neural network structure, meticulously crafted to condition diffusion models based on Soft edges. This unique approach ensures that while the fundamental features of an image are retained, the softer edges provide a seamless blend, eliminating the harshness often associated with rigid contours.
Advantages
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Preservation of Features: Unlike traditional models, Softedge prioritizes the retention of core image features, ensuring authenticity.
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Superior Blending: Softedge's strength lies in its ability to merge and blend elements seamlessly, creating harmonious compositions.
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Flexibility over Rigid Contours: Offers a softer and more adaptable approach compared to sharp contours, providing artists with greater creative freedom.
Use Cases:
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Digital Artistry: Artists can leverage Softedge to create digital masterpieces that exude depth and subtlety.
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Image Enhancement: Ideal for refining images, eliminating harshness, and ensuring a seamless visual blend.
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Film and Animation: Animators can use Softedge to create scenes that require nuanced blending and merging of elements.
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Graphic Design: Designers can craft captivating visuals, from posters to digital ads, harnessing the model's blending prowess.
ControlNet SoftEdge License
ControlNet SoftEdge, in its commitment to ethical AI practices, has embraced the CreativeML OpenRAIL M license. This decision not only underscores the model's dedication to responsible AI but also aligns it with the principles set forth by BigScience and the RAIL Initiative. Their collaborative work in AI ethics and responsibility has set the benchmark for licenses like the OpenRAIL M.
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instantid
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sd2.1-faceswapper
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
