SSD-Depth

This model leverages SSD-1B to generate the images with ControlNet conditioned on Depth Estimation

Playground

Try the model in real time below.

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PNG, JPG or GIF, Up-to 2048 x 2048 px

output image


Examples

Check out what others have created with SSD-Depth
Example preview

cinematic photo kung-fu-panda in mountains

seed: 5357285110guidance_scale: 7.5

API

If you're looking for an API, you can choose from your desired programming language.

POST
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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 image
RESPONSE
image/jpeg
HTTP Response Codes
200 - OKImage Generated
401 - UnauthorizedUser authentication failed
404 - Not FoundThe requested URL does not exist
405 - Method Not AllowedThe requested HTTP method is not allowed
406 - Not AcceptableNot enough credits
500 - Server ErrorServer had some issue with processing

Attributes


imageimage *

Input Image


promptstr *

Prompt to render


negative_promptstr ( default: None )

Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'


samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

max : 4


schedulerenum:str ( default: DPM2 Karras )

Type of scheduler.

Allowed values:


num_inference_stepsint ( default: 30 ) Affects Pricing

Number of denoising steps.

min : 20,

max : 100


guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 1,

max : 25


seedint ( default: -1 )

Seed for image generation.

min : -1,

max : 999999999999999


controlnet_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0,

max : 1


base64boolean ( default: 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.


Pricing

Serverless Pricing

Buy credits that can be used anywhere on Segmind

$ 0.001 /per second

Dedicated Cloud Pricing

For enterprise costs and dedicated endpoints

$ 0.0007 - $ 0.0031 /per second
FEATURES

PixelFlow allows you to use all these features

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.

Segmented Creation Workflow

Gain greater control by dividing the creative process into distinct steps, refining each phase.

Customized Output

Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.

Layering Different Models

Integrate and utilize multiple models simultaneously, producing complex and polished creative results.

Workflow APIs

Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.

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

  1. Realistic Depth Rendering: Elevates 2D images with a convincing sense of depth, making visuals more engaging and realistic.

  2. Dynamic Image Creation: Produces images that virtually leap from the screen, captivating the audience with their realism.

  3. Sophisticated Mask Analysis: Employs complex mask analysis to accurately render the depth of various elements within an image.

Use Cases

  1. 3D Visualizations: Transform architectural plans or product designs into interactive 3D models that offer a true sense of space and depth.

  2. Artistic Innovation: Artists can utilize the depth model to create visually stunning pieces that draw viewers into the scene.

  3. Enhanced Image Editing: Provide a new dimension to flat images, turning them into more realistic and engaging visuals.

  4. Game Environment Design: Implement in gaming to craft environments that offer a more authentic and immersive experience.

F.A.Q.

Frequently Asked Questions

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