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-allinonepixel"
# Request payload
data = {
"prompt": "futuristic abandoned city landscape, pale colors, 16 bit style, pixelart, gamedev, game asset background",
"negative_prompt": "out of frame, duplicate, watermark, signature, text, error, deformed",
"scheduler": "euler",
"num_inference_steps": 20,
"guidance_scale": 7.5,
"samples": 1,
"seed": 587685639,
"img_width": 512,
"img_height": 768,
"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'
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 0.1,
max : 25
Number of samples to generate.
min : 1,
max : 4
Seed for image generation.
Width of the image.
Allowed values:
Height of the Image
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.
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.
All in One Pixe
The All in One Pixe Model is a groundbreaking Stable Diffusion model, meticulously trained using Dreambooth to master the art of pixel creation. This model is a game-changer for pixel art enthusiasts, offering two distinct styles: sprite art and scene art. By using the trigger words "pixelsprite" and "16bitscene," users can effortlessly switch between these styles, making it a versatile tool for various pixel art applications.
Advantages
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Dual Style Capability: Offers sprite art and scene art styles, easily accessible through specific trigger words.
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High-Quality Pixel Art: Generates crisp, detailed pixel art that captures the essence of classic and modern styles.
Use Cases
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Game Development:Create unique sprite art and scenes for video games.
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Digital Art: Craft pixel-based artwork for personal or commercial use.
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Animation: Develop pixel art animations with a classic or modern flair.
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Graphic Design: Enhance graphic projects with pixel art elements.
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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.
