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/sd1.5-juggernaut"
# Request payload
data = {
"prompt": "Portrait photo of bearded guy in a worn mech suit, ((light bokeh)), intricate, (steel metal [rust]), elegant, sharp focus, photo by greg rutkowski, soft lighting, vibrant colors, (masterpiece), ((streets)), (detailed face)+, eye iris",
"negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art)++++, (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name)+, (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur)++, (3D ,3D Game, 3D Game Scene, 3D Character), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities)++",
"scheduler": "dpmpp_2m",
"num_inference_steps": 25,
"guidance_scale": 5,
"samples": 1,
"seed": 5873893888003329,
"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.
Juggernaut
Juggernaut is anchored in the stable diffusion 1.5, and is renowned for its precision and accuracy in image generation. This foundation ensures that Juggernaut crafts visuals that resonate with lifelike clarity, capturing the essence of subjects with impeccable detail.
Use cases
-
Digital Photography: Ideal for professionals seeking to create lifelike images without the constraints of traditional photography.
-
Film and Animation: Filmmakers and animators can utilize Juggernaut for character visualization and scene creation, ensuring photorealistic outputs.
-
Advertising and Marketing: Marketers can craft compelling visuals for campaigns, captivating audiences with images that resonate with realism.
-
Art and Design: Artists and designers can harness Juggernaut to enhance their projects with hyper-realistic elements.
Popular Models
