Acheun Image Generation & Editing

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

Platform: Replicate

Image GenerationImage InpaintingLoRA IntegrationDiffusion Model

899 runs

H100

License Check Required

🚀Function Overview

A versatile diffusion-based model for generating, editing, and transforming images through text prompts with advanced controls for quality, aspect ratios, and specialized weights.

Key Features

Prompt-driven image generation with trigger words
Image inpainting and mask-based editing
Customizable aspect ratios and resolutions
Multiple model variants for speed/quality tradeoffs
LoRA weights integration from multiple sources
Safety checker toggle and output quality/format controls
Fast generation mode for optimized speed

Use Cases

•Creating original artworks from textual descriptions
•Editing existing images via inpainting transformations
•Prototyping visual concepts quickly with fast mode
•Applying custom styles through LoRA integrations

⚙️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": "a photo of CHA, Cha Eun-woo of K-POP Artist, a muscular male model with a sculpted physique, broad shoulders and toned arms, defined jawline, full face clearly visible in soft natural light, wearing an ethereal, translucent robe with subtle glowing patterns, flowing like mist around his body, a solitary angelic figure sitting on a rock by the river, delicate porcelain-inspired wings with intricate blue floral engravings, serene flowing river with gentle ripples, soft mist rising from the water, dreamy and surreal atmosphere, blending fantasy and reality, ultra-detailed feathers, cinematic depth of field, editorial photography, shot on 35mm film",
  "go_fast": false,
  "lora_scale": 2,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 6,
  "output_quality": 80,
  "prompt_strength": 0.6,
  "extra_lora_scale": 1,
  "num_inference_steps": 40
}

Output Results

https://replicate.delivery/xezq/oxxUZr9RV346PRBESf5tswnq0nwt4MZG8gfN4Ne8rDX9ejHSB/out-0.png

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 899
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Prompt-driven image generationImage inpaintingLoRA integrationCustomizable aspect ratiosMultiple model variantsArtistic creationVisual concept prototyping

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