((selfie)) photo of an american girl and guy, smiling, (yosemite:1.3), mountains, wearing a backpack, red top, hiking jacket, rocks, river, wood, analog style (look at viewer:1.2) (skin texture), close up, cinematic light, ((night sky:1.2)), (milkiway:1.4), sidelighting, Fujiflim XT3, DSLR, 50mm, (long windblown hair)
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-realisticvision"
# Request payload
data = {
"prompt": "((selfie)) photo of an american girl and guy, smiling, (yosemite:1.3), mountains, wearing a backpack, red top, hiking jacket, rocks, river, wood, analog style (look at viewer:1.2) (skin texture), close up, cinematic light, ((night sky:1.2)), (milkiway:1.4), sidelighting, Fujiflim XT3, DSLR, 50mm, (long windblown hair)",
"negative_prompt": "\"(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, render, 3d, 2d, sketch, painting, digital art, drawing, disfigured, ((nsfw)), ((breasts))",
"scheduler": "dpmpp_2m",
"num_inference_steps": 25,
"guidance_scale": 6,
"samples": 1,
"seed": 4082622942,
"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 image
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.
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 Realistic Vision model is a state-of-the-art AI model based on Stable Diffusion 1.5 that is capable of creating super realistic portraits that look like real photos. It can generate portraits in different styles, ages, and clothing, and can even create people with specific clothing. The portraits created by the model are described as absolutely amazing and mind-blowing.
The Realistic Vision model operates on a stable diffusion framework and uses SD 1.5 as it's base model. Suggested schedulers are Euler A and DPM++ SDE Karras. It works best when you combine it with an upscaler like ESRGAN.
The Realistic Vision model is capable of creating realistic and modern pictures. The model is flexible with the prompts, allowing users to use square brackets and negative prompts. Although the images created using the model look great, the clothing in the photos may appear run-down, adding a touch of authenticity to the images.
Creating realistic portraits for digital art.
Generating diverse characters for video games or animations.
Producing unique avatars for social media or virtual reality platforms.
Designing fictional characters for books or graphic novels.
Providing a tool for fashion designers to visualize different styles and outfits on various models.
The license for the Realistic Vision 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.
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
Story Diffusion turns your written narratives into stunning image sequences.
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