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-paragon"
# Request payload
data = {
"prompt": "a photo of Jason, solo, in frame, somber, apocalyptic city, dark theme, extremely detailed eyes, detailed symmetric realistic face extremely detailed natural skin texture, peach fuzz, messy hair, masterpiece, absurdres, artillery fire in the background, award winning ",
"negative_prompt": "airbrushed,3d, render, painting, anime, manga, illustration, (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation,bige yes, teeth,nose piercing,(((extra arms)))cartoon,young,child ",
"scheduler": "dpmpp_sde_ancestral",
"num_inference_steps": 25,
"guidance_scale": 7.5,
"samples": 1,
"seed": 945216760,
"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.
Paragon v1.0
The Paragon v1.0 model is a boon for artists seeking photorealism in their work. Developed by the collaborative efforts of s6yx, FIKOsofter, Lykon, and CornmeisterNL, this model is built on the Stable Diffusion 1.5 framework. It is renowned for its ability to generate super realistic portraits that are really close to real photographs. The model's strong contrast is a feature that has garnered much appreciation. It is versatile, capable of generating portraits in various styles, ages, and clothing. The LoRa versions of this model have been particularly well-received for their performance.
Delving into the technical architecture of Paragon v1.0, it is designed to work optimally with Euler A or DPM++ SDE Karras schedulers with 25 or more steps. The model's recommended CFG Scale ranges from 5 to 12, and it performs better with Clip Skip 2. The model also comes with a recommended negative prompt that includes terms like "deformed iris", "semi-realistic", "cgi", "3d", "render", "sketch", "cartoon", "drawing", "anime", "mutated hands and fingers", "deformed", "distorted", "disfigured", "poorly drawn", "bad anatomy", "wrong anatomy", "extra limb", "missing limb", "floating limbs", "disconnected limbs", "mutation", "mutated", "ugly", "disgusting", "amputation", and "easynegative".
The advantages of the Paragon v1.0 model are manifold. Its ability to create photorealistic portraits is unmatched, making it a valuable tool for artists who require high levels of realism in their work. The model's strong contrast adds depth and dimension to the portraits, enhancing their lifelike quality. Furthermore, the model's versatility in generating portraits of different styles, ages, and clothing expands the creative possibilities for artists.
Paragon v1.0 use cases:
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Digital Art Creation: Artists can use this model to create realistic portraits, adding a touch of realism to their digital art.
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Character Generation for Video Games or Animations: Game developers and animators can use this model to generate diverse characters, enhancing the visual appeal of their games or animations.
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Social Media or Virtual Reality Avatars: The model can be used to produce unique avatars for users on social media or virtual reality platforms.
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Character Design for Books or Graphic Novels: Authors and graphic novelists can use this model to design fictional characters, bringing their stories to life.
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Fashion Design Visualization: Fashion designers can use this model to visualize different styles and outfits on various models, aiding in the design process.
Paragon v1.0 license
The license for the Paragon v1.0 model, known as the "CreativeML Open RAIL-M" license, is designed to promote both open and responsible use of the model. You may add your own copyright statement to your modifications and provide additional or different license terms for your modifications. You are accountable for the output you generate using the model, and no use of the output can contravene any provision as stated in the license.
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sdxl1.0-txt2img
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sd1.5-epicrealism
This model corresponds to the Stable Diffusion Epic Realism checkpoint for detailed images at the cost of a super detailed prompt
