Stable Diffusion API

Generate images with Stability AI’s Stable Diffusion 3.5 Large and 3 Medium models via the Runcrate API.

Generate images using Stability AI’s Stable Diffusion models through the Runcrate API. No GPU setup, no model hosting — one API call, one image.

Available models

Model	Quality	Speed	Best for
SD 3.5 Large	High	Medium	Production images, detailed scenes
SD 3 Medium	Good	Fast	Prototyping, high-volume generation

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Basic image generation

Python

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

image = client.models.generate_image(
    model="stabilityai/stable-diffusion-3-5-large",
    prompt="A serene Japanese garden in autumn, koi pond with stone bridge, "
           "golden maple leaves, soft morning mist, photorealistic",
    aspect_ratio="16:9",
)

image.data[0].save("japanese-garden.png")

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Using negative prompts

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

image = client.models.generate_image(
    model="stabilityai/stable-diffusion-3-5-large",
    prompt="Professional headshot of a business executive, studio lighting, neutral background",
    negative_prompt="cartoon, illustration, anime, blurry, low quality, deformed",
    aspect_ratio="1:1",
)

image.data[0].save("headshot.png")

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Controlling generation parameters

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

image = client.models.generate_image(
    model="stabilityai/stable-diffusion-3-5-large",
    prompt="Detailed architectural blueprint of a modern house, clean lines, cross-section view",
    num_inference_steps=40,   # default ~28, higher = more detail
    guidance=7.5,             # how closely to follow the prompt
    seed=42,                  # reproducible results
)

image.data[0].save("blueprint.png")

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Batch generation

from runcrate import Runcrate
from concurrent.futures import ThreadPoolExecutor

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

products = [
    {"name": "candle", "prompt": "Luxury scented candle in matte black jar, warm lighting, product photography"},
    {"name": "notebook", "prompt": "Premium leather notebook on wooden desk, fountain pen, natural light"},
    {"name": "mug", "prompt": "Ceramic coffee mug with steam, dark moody background, product shot"},
]

def generate(p):
    image = client.models.generate_image(
        model="stabilityai/stable-diffusion-3-5-large",
        prompt=p["prompt"], aspect_ratio="1:1",
    )
    image.data[0].save(f"{p['name']}.png")
    print(f"Saved {p['name']}.png")

with ThreadPoolExecutor(max_workers=4) as pool:
    pool.map(generate, products)

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SD 3.5 Large vs. SD 3 Medium

	SD 3.5 Large	SD 3 Medium
Detail	Fine textures, realistic lighting	Sufficient for most uses
Speed	Medium	Fast
Best for	Final production assets	Prototyping, high-volume

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Tips

• Negative prompts significantly improve quality — exclude common artifacts.
• Seed parameter makes results reproducible. Save it for prompt iteration.
• SD 3.5 Large for customer-facing images. SD 3 Medium for internal/prototype.
• Aspect ratio options: 1:1, 16:9, 9:16, 4:3, 3:2.

Next steps

• Image generation reference
• AI Image Generation API — FLUX and multi-model comparison
• Ideogram 3 API — best text rendering in images