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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-protovis-lightning"
# Request payload
data = {
"prompt": "Disney Animation, Pixar Studios, 3D Rendering, bear in a wooden rocking chair, wearing a red pastel dress with beautiful embellishments, reading a book, near a fireplace, in a log cabin, with blue sky and clouds with a rainbow in the window",
"negative_prompt": "(octane render, render, drawing, bad photo, bad photography:1.3), (worst quality, low quality, blurry:1.2), (bad teeth, deformed teeth, deformed lips), (bad anatomy, bad proportions:1.1), (deformed iris, deformed pupils), (deformed eyes, bad eyes), (deformed face, ugly face, bad face), (deformed hands, bad hands, fused fingers), morbid, mutilated, mutation, disfigured",
"samples": 1,
"scheduler": "DPM++ SDE",
"num_inference_steps": 9,
"guidance_scale": 1,
"seed": 2897745,
"img_width": 1024,
"img_height": 1024,
"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
blur, noisy, disfigured
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 1,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
Seed for image generation.
min : -1,
max : 999999999999999
Can only be 1024 for SDXL
Allowed values:
Can only be 1024 for SDXL
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.
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
The ProtoVision Lightning SDXL model stands at the forefront of innovation, specializing in the production of High Fidelity 3D, Photorealism, Anime, and hyperrealism images. This cutting-edge model is engineered to deliver unparalleled visual quality and creativity across a spectrum of artistic styles.Distinguished by its focus on High Fidelity 3D, Photorealism, Anime, and hyperrealism images, the ProtoVision Lightning SDXL represents a significant advancement in image generation technology, setting a new standard for excellence in creating visually stunning and artistically diverse images.
A standout feature of the ProtoVision Lightning SDXL is its exceptional ability to swiftly and efficiently produce high-quality images across various styles, catering to applications that demand rapid and diverse image production. With the capacity to generate intricate and detailed images spanning different genres such as Anime and hyperrealism, this model offers a unique blend of speed and artistic versatility.
To optimize the performance of the ProtoVision Lightning SDXL, ensuring compatibility with the DPM++ SDE Karras / DPM++ SDE sampler is essential. Leveraging 4-6 sampling steps and a CFG Scale set between 1 and 2 is recommended to achieve peak performance and efficiency in the image generation process. These tailored settings are crucial for unlocking the full creative potential of this advanced model.
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
Audio-based Lip Synchronization for Talking Head Video
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