Stable Diffusion 3 Medium Text to Image
Stable Diffusion is a type of latent diffusion model that can generate images from text. It was created by a team of researchers and engineers from CompVis, Stability AI, and LAION. Stable Diffusion v2 is a specific version of the model architecture. It utilizes a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. When using the SD 2-v model, it produces 768x768 px images. It uses the penultimate text embeddings from a CLIP ViT-H/14 text encoder to condition the generation process.
Examples
A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space
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')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/stable-diffusion-3-medium-txt2img"
# Request payload
data = {
"prompt": "A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space",
"negative_prompt": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
"samples": 1,
"scheduler": "DPM++ 2M",
"num_inference_steps": 25,
"guidance_scale": 5,
"denoise": 1,
"seed": 468685,
"img_width": 1024,
"img_height": 1024,
"modelsamplingsd3_shift": 3,
"conditioningsettimesteprange_start": 0.1,
"conditioningsettimesteprange_stop": 1,
"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'
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 10,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
How much to transform the reference image
min : 0.1,
max : 1
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
Model Sampling SD3 Shift
min : 1,
max : 10
Conditioning set timestep range start
min : 0.1,
max : 1
Conditioning set timestep range stop
min : 0.1,
max : 1
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.
PixelFlow allows you to use all these features
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.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Stable Diffusion 3 Medium Text-to-Image
Stable Diffusion 3 Medium Text-to-Image (SD3 Medium) is the latest and most advanced addition to the Stable Diffusion family of image-to-image models. SD3 text-to-image Medium is designed to be more resource-efficient, making it a better choice for users with limited computational resources. Due to its smaller size, SD3 Medium can run efficiently on consumer-grade hardware, including consumer PCs and laptops, as well as enterprise-tier GPUs. SD3 Medium is designed to be more resource-efficient, making it a better choice for users with limited computational resources.
Stable Diffusion 3 Medium Text-to-Image Capabilities
SD3 Medium crafts stunningly realistic images, breaking new ground in photorealistic generation. It also tackles intricate prompts with multiple subjects, even if you have a typo or two. SD3 Medium incorporates typography within your images with unparalleled precision, making your message shine.
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The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
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