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')
# Use this function to convert a list of image URLs to base64
def image_urls_to_base64(image_urls):
return [image_url_to_base64(url) for url in image_urls]
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/playground-v2.5"
# Request payload
data = {
"prompt": "(solo), anthro, male, protogen, high detailed fur, smile, hyperdetailed,realistic",
"negative_prompt": "bad anatomy, bad hands, missing fingers,low quality,blurry",
"samples": 1,
"num_inference_steps": 25,
"guidance_scale": 3,
"seed": 36446545871,
"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
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers,low quality,blurry'
Number of samples to generate.
min : 1,
max : 4
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
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.
Playground V2.5
Playground V2.5 is a diffusion-based text-to-image generative model, designed to create highly aesthetic images based on textual prompts. As the successor to Playground V2, it represents the state-of-the-art in open-source aesthetic quality. Playground v2.5 excels at producing visually attractive images. It achieves this through advancements in color, contrast and human details.
Technical Details
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Model Type: Playground V2.5 operates as a Latent Diffusion Model.
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Text Encoders: It utilizes two fixed, pre-trained text encoders: OpenCLIP-ViT/G and CLIP-ViT/L.
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Architecture: The model follows the same architecture as Stable Diffusion XL.
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Resolution: Playground V2.5 generates images at a resolution of 1024x1024 pixels, catering to both portrait and landscape aspect ratios.
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Scheduler Options: The default scheduler is EDMDPMSolver Multistep Scheduler, which enhances fine details. A guidance scale of 3.0 works well with this scheduler.
Playground V2.5 outperforms SDXL, PixArt-α, DALL-E 3, Midjourney 5.2, and even its predecessor, Playground V2.
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