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-dvrach"
# Request payload
data = {
"prompt": "dvArchModern, 85mm, f1.8, portrait, photo realistic, hyperrealistic, orante, super detailed, intricate, dramatic, sun lighting, shadows, high dynamic range, modern interior, beach house, mexico",
"negative_prompt": "signature, soft, single floor,unclear, watermark, blurry, drawing, sketch, poor quality, ugly, text, type, word, logo, pixelated, low resolution, saturated, high contrast, oversharpened",
"scheduler": "euler_a",
"num_inference_steps": 20,
"guidance_scale": 7,
"samples": 1,
"seed": 5499784543,
"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.
DvArch
DvArch, a bespoke trained model tailored to breathe life into architectural visions. With its unique approach of utilizing three distinct trigger words - dvArchModern, dvArchGothic, and dvArchVictorian - dvArch offers users a specialized avenue to explore and manifest architectural styles spanning centuries.
At the core of DvArch lies a sophisticated mechanism designed to respond to specific trigger words. Each trigger word acts as a gateway to a distinct architectural style, ensuring that users can seamlessly transition between modern minimalism, the grandeur of Gothic, and the elegance of Victorian, all within a single model framework.
Advantages
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Tailored Outputs: The model's response to individual trigger words ensures designs that resonate with the chosen architectural style.
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Versatile Design Palette: From the sleek lines of modern architecture to the intricate details of Victorian designs, DvArch offers a broad spectrum of design possibilities.
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User-Centric Approach: The trigger word mechanism provides an intuitive user experience, allowing for easy navigation between styles.
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High Precision: DvArch's custom training ensures accurate and authentic architectural renderings aligned with the chosen style.
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Innovative Integration: The model's unique approach sets it apart, making it a valuable tool for architects and designers seeking specialized outputs.
Use Cases
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Architectural Visualization: Architects can harness DvArch to visualize structures in various styles, aiding client presentations and design iterations.
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Educational Platforms: Students and enthusiasts can explore architectural styles in-depth, understanding nuances and design principles.
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Virtual Tours: Real estate and travel platforms can use DvArch to create immersive virtual tours, showcasing properties in different architectural renditions.
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Game Design: Game developers can integrate DvArch to craft diverse in-game structures, enhancing world-building and player immersion.
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Media and Film Production: Film producers and directors can utilize the model for pre-visualization, setting the tone for scenes and backdrops.
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