Moris Image Generator

Discover Moris, a powerful image generation and editing model that allows you to create stunning visuals from text prompts, perform inpainting, and adapt styles with LoRA.

Platform: Replicate

Text-to-Image GenerationImage InpaintingLoRA AdaptationImage Editing

341 runs

H100

License Check Required

🚀Function Overview

Generates or modifies images based on text prompts and input images, supporting inpainting, style transfer via LoRA, and precise parameter control for dimensions, quality, and generation speed.

Key Features

Text-to-image generation with trigger words
Image-to-image transformation
Inpainting using image masks
LoRA style/concept adaptation
Adjustable dimensions, aspect ratios, and quality settings
Fast generation mode (fp8 quantized) or detailed mode (bf16)
Control over denoising steps, guidance scale, and safety checks

Use Cases

•Creating custom artwork from text descriptions
•Editing photos via inpainting (e.g., object removal)
•Style transfer using LoRA adapters
•Generating image variations from reference inputs

⚙️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": "MORIS, a close-up portrait of a confident and charismatic professional speaker, well-groomed with short styled hair and a subtle smile, wearing a dark blazer and crisp shirt, studio background with soft gradient, sharp front lighting enhancing facial features, eyes focused and expressive, shot with a Canon EOS R3, 85mm f/1.2 lens, clean and neutral color tones with subtle warmth",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "2:3",
  "output_format": "png",
  "guidance_scale": 3.23,
  "output_quality": 100,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 37
}

Output Results

https://replicate.delivery/xezq/II0EKEdwmqqgOxfDjCh6LfqSzUSJKWUDcTWqhu2yN30OSsoUA/out-0.png

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 341
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Image GenerationText-to-ImageImage EditingLoRA AdaptationInpaintingStyle TransferCustom Artwork

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