Stable Diffusion 3 Large 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.
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-large-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",
"mode": "text-to-image",
"aspect_ratio": "1:1",
"output_format": "jpeg",
"base64": False,
"negative_prompt": "ugly, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft"
}
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
Type of mode.
Allowed values:
aspect_ratio
Allowed values:
Output format.
Allowed values:
Base64 encoding of the output image.
Prompts to exclude, eg. 'bad anatomy, bad hands'
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.
Stable Diffusion 3 Large Text-to-Image
Stable Diffusion 3 Large Text-to-Image (SD3 Large) is the latest and most advanced addition to the Stable Diffusion family of image-to-image models. The 8 billion parameter count in SD3 Large empowers it to tackle intricate tasks such as text understanding, typography, and generate highly detailed images. However, SD3 Large might require more powerful hardware to run smoothly. While optimized for performance, it may necessitate additional computational resources due to its larger size.
Stable Diffusion 3 Large Text-to-Image Capabilities
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Detailed descriptions: You can provide detailed descriptions including objects, characters, settings, lighting, and even artistic styles. Stable Diffusion 3 can translate these descriptions into high-quality images.
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Complex prompts: It can handle intricate prompts with multiple subjects and even account for slight variations in spelling or phrasing.
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Photorealism: The model excels at generating images that are incredibly close to real photographs, overcoming artifacts often seen in hands and faces in previous versions.
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Typography: It can render text within the generated images more accurately than previous models.
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sdxl1.0-txt2img
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sd2.1-faceswapper
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