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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/sdxl1.0-timeless"
# Request payload
data = {
"prompt": "tangerine tango and ultramarine green color blocking, (sci-fi aesthetic:1.4), bright instagram LUT, shot of a (Danish 20 yo woman:1.2) retro-futuristic cosmonaut with a shy smile wearing a glass dome helmet and spacesuit with harness (with futuristic power plant in the background:1.2), skindentation, hourglass figure, waist cincher, on alien landscape with its surface covered in impact craters, valleys, plains and mountains, grey dust, a heavy rain storm, at sunrise, geometric gradients, sci-fi, 1950s retro-futurism, hyperrealistic, cinematic lighting, digital art, concept art, design and details, ultra-detailed, highest detail quality, ultra-realistic, photography lighting, photorealistic, cinematic, movie quality rendering, octane rendering, focused, 8k, depth of field, real shadow, vfx post-production, rtx ray tracing lighting",
"negative_prompt": "(worst quality, low quality:1.4), signature, artist name, text, web address, logo, error, cropped, artifacts, watermark, username, blurry, collage, grid, lens, camera lens, car, truck, road, fat, obese, armor, badges, patches, usa, nasa",
"samples": 1,
"scheduler": "UniPC",
"num_inference_steps": 25,
"guidance_scale": 7,
"seed": 1502673077,
"img_width": 896,
"img_height": 1152,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated image
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
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
Image width can be between 512 and 2048 in multiples of 8
Image height can be between 512 and 2048 in multiples of 8
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.
Copax TimeLess SDXL is a cutting-edge diffusion model dedicated to a broad range of artistic styles. Prioritizing style diversity over genre limitations, it allows users to craft captivating images. Continuously evolving, it boasts enhanced character and facial details. Rooted in the foundational architecture of SDXL 1.0, Copax TimeLess SDXL is meticulously crafted to prioritize artistic versatility.
Unparalleled Style Diversity: TimeLess SDXL breaks free from genre limitations, offering a vast palette of artistic styles for users to explore.
Detailed Renderings: The model excels in capturing the nuances of character and facial details, ensuring lifelike and authentic visual outputs.
Based on Proven Architecture: Building on the robust foundation of SDXL 1.0, TimeLess SDXL combines reliability with innovation.
Digital Art Creation: Artists can harness TimeLess SDXL to craft diverse artworks, from portraits to abstract pieces.
Content Generation: Ideal for content creators aiming to produce visually rich and varied content for their audiences.
Interactive Design: Designers can iteratively shape their creations, experimenting with a myriad of styles.
Educational Tools: Art students and enthusiasts can explore different artistic genres, understanding their nuances and principles.
Entertainment and Media: Film and game producers can utilize it for pre-visualization, setting the artistic tone for scenes and backdrops.
SDXL Img2Img is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers
SDXL ControlNet gives unprecedented control over text-to-image generation. SDXL ControlNet models Introduces the concept of conditioning inputs, which provide additional information to guide the image generation process
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software