Mayo3 Image Generation

Mayo3 Image Generation offers powerful text-to-image capabilities and detailed image inpainting. Let's explore what this AI model can do for you!

Platform: Replicate

Image GenerationImage-to-ImageInpaintingLoRA Fine-tuning

28 runs

H100

License Check Required

🚀Function Overview

Generates and modifies images using text prompts, with support for image-to-image translation, inpainting, adjustable resolution, and LoRA-based customizations.

Key Features

Text-to-image generation
Image-to-image translation
Image inpainting with masks
LoRA weights for style/object customization
Multiple model versions (quality vs speed)
Adjustable resolution and aspect ratio
Safety checker toggle
Control via guidance scale/steps/seed

Use Cases

•Creating digital art from textual descriptions
•Modifying existing images (e.g., object insertion/removal)
•Restoring damaged photos via inpainting
•Style transfer using LoRA adapters
•Batch image generation

⚙️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

{
  "model": "dev",
  "prompt": "Photograph of mayo3 as human male, bald, wearing sunglasses and a navy blue polo shirt, driving a sailboat with a steering wheel on the deck. He is standing at the helm of the sailing yacht, with the Mediterranean Sea as the background during summertime, captured using a Sony Alpha A7 IV camera at F8, ISO 200. The style should be similar to the artist\n\"Lovereau\"\n', with a detailed focus on the\nhands holding the steering wheel and nautical elements.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 2.18,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 45
}

Output Results

https://replicate.delivery/xezq/WF5l8oD4RaoPCdpRwOZAxO3JhEmzoBQVLoZN2i77S0ZloULF/out-0.webp

Quick Actions

Use NowView Documentation

Technical Specifications

Hardware Type: H100
Run Count: 28
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Text-to-image generationImage InpaintingImage-to-image translationLoRA customizationAdjustable resolutionDigital art creationPhoto restoration

Related Models

Vladu Image Generation

A model for generating and editing images using prompts, input images, and masks.

VKNG LoRA Test

A model for generating and editing images using LoRA-tuned diffusion techniques with support for text prompts, image inputs, and inpainting.

Oyadi Image Generation Model

A model for generating and editing images using text prompts with support for inpainting, img2img, and LoRA adjustments.