Jan Image Generation

Discover Jan Image Generation, a powerful tool for generating and editing images with text prompts, image modifications, and inpainting capabilities.

Platform: Replicate

Diffusion ModelsImage InpaintingLoRA IntegrationText-to-Image

18 runs

H100

License Check Required

🚀Function Overview

Generates and edits images through text prompts with options for aspect ratio control, image-to-image transformations, inpainting, and LoRA-based customization.

Key Features

Text-to-image generation with trigger word activation
Image-to-image translation using prompt strength controls
Mask-based inpainting for targeted edits
Multiple model variants for speed vs quality tradeoffs
LoRA adapter support for fine-tuned style/object generation
Custom aspect ratio and resolution settings
Quality/format controls for output images

Use Cases

•Creative art generation from text descriptions
•Photo editing via inpainting (object removal/replacement)
•Style transfer using LoRA weights
•Concept visualization with prompt refinement
•Rapid prototyping with fast generation mode

⚙️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": "Janice  is not wearing glasses, she is sitting on a white box in the studio wearing a white blouse & black pants talking to customers with one leg hanging down and one leg tucked under her & she is holding a camera on her lap",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/vJxWmG22At66MNl4fh4zXG8emwGp8Mpy6jZC7s7TOWU2ANfoA/out-0.webp

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 18
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Image EditingText-to-ImageImage-to-ImageInpaintingLoRA SupportCreative Art GenerationPhoto EditingConcept Visualization

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