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')
# Use this function to convert a list of image URLs to base64
def image_urls_to_base64(image_urls):
return [image_url_to_base64(url) for url in image_urls]
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/minimaxir-sdxl-wrong-lora"
# Request payload
data = {
"prompt": "presidential painting of realistic human Spongebob Squarepants wearing a suit, (oil on canvas)",
"negative_prompt": "boring, poorly drawn, bad artist, (worst quality:1.4), simple background, uninspired, (bad quality:1.4), monochrome, low background contrast, background noise, duplicate, crowded, (nipples:1.2), big breasts",
"scheduler": "UniPC",
"num_inference_steps": 25,
"guidance_scale": 8,
"samples": 1,
"seed": 3426017487,
"img_width": 1024,
"img_height": 1024,
"base64": False,
"lora_scale": 1
}
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.
Scale of the lora
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.
SDXL Wrong LoRA
SDXL Wrong LoRA is engineered with a focus on delivering higher detail in images, making it ideal for high-resolution projects. The model boasts increased color saturation and vibrance, bringing images to life with stunning clarity. This model is a game-changer for artists, photographers, and digital creators, offering enhanced texture detail, color vibrancy, and overall image sharpness
Advantages
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Enhanced Texture Detail: Exceptional at capturing fine details in images.
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Vibrant Color Saturation: Produces images with rich, vivid colors.
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Reduced Artifacts: Minimizes random artifacts for cleaner, more professional results.
Use Cases
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Digital Art and Illustration:Ideal for artists needing high detail and color vibrancy in their work.
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Fashion and Textile Design: Useful for designers focusing on fabric textures and patterns.
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Graphic Design: Enhances graphic design projects with sharper images and vibrant colors.
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Advertising and Marketing: Creates visually striking images for advertising campaigns.
Other Popular Models
sdxl-img2img
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
fooocus
Fooocus enables high-quality image generation effortlessly, combining the best of Stable Diffusion and Midjourney.
faceswap-v2
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
sd2.1-faceswapper
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
