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Examples
API
Describe your edits
⚙️ Settings
Stable Diffusion v2.1 (stability.ai)
Nano Banana Pro (Google)
Nano Banana 2 (Google)
Nano Banana (Google)
FLUX.1 Pro Kontext (fal.ai)
GPT Image 2 (OpenAI)
GPT Image 1.5 (OpenAI)
✨ InstructPix2Pix (Tim Brooks)
DreamShaper (Lykon)
Dreamlike Photoreal 2.0 (dreamlike.art)
Stable Diffusion v1.5 (RunwayML)
Search for our available models here to learn more about them.Please use our default settings for optimal results if you're a beginner.
Control Net models provide a layer of refinement to the image generation process that blends the prompt with the control image. Choose your preferred models:
Canny
Depth
HED Boundary
M-LSD Straight Line
Normal Map
Human Pose
Scribble
Image Segmentation
Tiling
Brightness
QR Monster V2
This allows you to specify what you DON'T want to see in your output.Useful negative prompts can be found here.
Image generation engines can often generate disproportionate body parts, extra limbs or fingers, strange textures etc. Use negative prompting to avoid disfiguration or for creative outputs like avoiding certain colour schemes, elements or styles.
Change the number of outputs for a single run here:
By default, each run produces one output per Model.
How precise, or focused do you want your output to be?
An increase in output quality is comparable to a gradual progression in any drawing process that begins with a draft version and ends with a finished product.
(Text Guidance Scale) How pressurized should the AI feel to produce what you want?How much creative freedom do you want the AI to have when interpreting your prompt?
At lower values the image will effectively be random. The standard value is between 6-8. Values that are too high can also distort the image.
(Prompt Strength)
How much should the original image be modified?
0 will keep the original image intact.0.9 will ignore the original image completely.
0
0.9
Run cost = 5 credits
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