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Slingit1 AI Image Generator

Discover Slingit1, a powerful AI model for image generation and editing. Ready to experience the power of AI? Start your journey here!

Platform: Replicate
Text-to-ImageImage InpaintingLoRA CustomizationStable Diffusion
102 runs
H100
License Check Required

🚀Function Overview

Generates or edits images from text prompts, input images, and masks, with extensive customization options including LoRA integration and quality controls.

Key Features

  • Text-to-image generation with prompt activation triggers
  • Image inpainting using mask overlays
  • Support for custom aspect ratios and resolutions
  • Multiple model options for speed/quality trade-offs (dev/schnell)
  • LoRA integration for style/object customization
  • Safety checker for generated content
  • Adjustable guidance scales and denoising steps

Use Cases

  • Creating artwork from text descriptions
  • Editing or restoring photos via inpainting
  • Product visualization with branded elements
  • Style transfer using LoRA weights
  • Rapid prototyping of visual concepts

⚙️Input Parameters

prompt

string

Prompt for generated image. If you include the `trigger_word` used in the training process you are more likely to activate the trained object, style, or concept in the resulting image.

image

string

Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

mask

string

Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

aspect_ratio

string

Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode

height

integer

Height of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation

width

integer

Width of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation

prompt_strength

number

Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image

model

string

Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.

num_outputs

integer

Number of outputs to generate

num_inference_steps

integer

Number of denoising steps. More steps can give more detailed images, but take longer.

guidance_scale

number

Guidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

output_quality

integer

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

disable_safety_checker

boolean

Disable safety checker for generated images.

go_fast

boolean

Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16

megapixels

string

Approximate number of megapixels for generated image

lora_scale

number

Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

extra_lora

string

Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'

extra_lora_scale

number

Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

💡Usage Examples

Example 1

Input Parameters

{
  "mask": "https://replicate.delivery/pbxt/MsnPG8X4StlKMniwuAtdruSrGGXsTlJJFKshYYOL3vIAPxQM/replicate-prediction-vy0yx2aj7nrj40cpbsw8q4qdh8.png",
  "model": "dev",
  "prompt": "A cinematic golden hour scene on a premium golf course surrounded by lush pine trees and distant snow-capped mountains. Two stylish golfers—one male, one female—walk along the fairway with golf bags over their shoulders, chatting and smiling, casually holding cans of Slingit Light Lager in hand. One can is placed naturally on the dashboard of a sleek black golf cart in the foreground. The sunlight casts long shadows and gives a soft, warm glow across the green. Ultra-realistic photographic style, shot on a Canon EOS R5 with an RF 85mm f/1.2L lens, shallow depth of field (f/1.4), bokeh background, detailed textures, subtle highlights on the aluminum can, natural skin tones, vibrant but soft color grading. Editorial quality, commercial product photography with lifestyle branding aesthetics.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 7.53,
  "output_quality": 80,
  "prompt_strength": 0.73,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/f08eG2QP5nncuUJ7IQkEh8UENDsCvW9Cq1eXeySfQFd1KPrkC/out-0.png