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/ssd-depth"
# Request payload
data = {
"image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/outputs/ssd_depth_input.jpeg"), # Or use image_file_to_base64("IMAGE_PATH")
"prompt": "cinematic photo kung-fu-panda in mountains",
"negative_prompt": "low quality, ugly, painting",
"samples": 1,
"scheduler": "UniPC",
"num_inference_steps": 30,
"guidance_scale": 7.5,
"seed": 5357285110,
"controlnet_scale": 0.5,
"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
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
Seed for image generation.
min : -1,
max : 999999999999999
Scale for classifier-free guidance
min : 0,
max : 1
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.
Segmind Stable Diffusion 1B (SSD-1B) Depth
Segmind Stable Diffusion 1B (SSD-1B) Depth Model transcends traditional image processing by generating depth maps that convert flat visuals into rich, three-dimensional experiences. The resulting images are not just seen but felt, as they offer a tangible sense of depth that elevates the visual narrative.
At its core, the SSD-1B Depth Model utilizes advanced algorithms to interpret and render depth from two-dimensional images. It meticulously analyzes image masks to gauge depth variations, crafting a multi-layered depth map that breathes life into each pixel. While its depth perception is profound, the model's intelligence can sometimes extrapolate beyond the visible, introducing unexpected elements into the scene, particularly with images that defy natural structures.
Advantages
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Realistic Depth Rendering: Elevates 2D images with a convincing sense of depth, making visuals more engaging and realistic.
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Dynamic Image Creation: Produces images that virtually leap from the screen, captivating the audience with their realism.
-
Sophisticated Mask Analysis: Employs complex mask analysis to accurately render the depth of various elements within an image.
Use Cases
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3D Visualizations: Transform architectural plans or product designs into interactive 3D models that offer a true sense of space and depth.
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Artistic Innovation: Artists can utilize the depth model to create visually stunning pieces that draw viewers into the scene.
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Enhanced Image Editing: Provide a new dimension to flat images, turning them into more realistic and engaging visuals.
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Game Environment Design: Implement in gaming to craft environments that offer a more authentic and immersive experience.
Other Popular Models
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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
instantid
InstantID aims to generate customized images with various poses or styles from only a single reference ID image while ensuring high fidelity
sdxl1.0-txt2img
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
