Chahan Image Diffusion

Chahan Image Diffusion elevates your creative process. Generate and edit images with precise LoRA integration & prompt control.

Platform: Replicate

Diffusion Image GenerationLoRA IntegrationImage InpaintingPrompt-to-Image

239 runs

H100

License Check Required

🚀Function Overview

Generates or edits images based on text prompts, input images, or masks, with extensive customization options including aspect ratio, resolution, model selection, LoRA scaling, and diffusion parameters.

Key Features

Supports text-to-image, image-to-image, and inpainting modes
LoRA integration for applying custom styles/concepts
Option to choose between quality-optimized (dev) or speed-optimized (schnell) models
Precise control over diffusion steps, guidance scale, and output quality
Customizable image dimensions and aspect ratios

Use Cases

•Creating original images from text descriptions
•Modifying existing images using inpainting techniques
•Applying artistic styles/objects via LoRA adjustments
•Batch generation of images with consistent seeds
•Experimenting with diffusion parameters for artistic effects

⚙️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": "chahan sitting confidently and stylishly on the hood of a white sports car, slightly turned to the side with a thoughtful, intense gaze away from the camera. The background shows Tokyo at night with vibrant neon lights casting colorful glows and dramatic shadows. The image has a moody, cinematic feel like a professional artist photo shoot, with sharp contrasts and atmospheric lighting. The woman is realistically proportioned to the car, with the car front and part of her body visible in the frame.  \nBREAK,  \nOutfit: (black T-shirt, denim shorts, white sneakers:1.4),  \nBREAK,  \nHair style: (Two Buns White Hair:1.2),",
  "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": 30
}

Output Results

https://replicate.delivery/xezq/ZRNBTXRBjz7BMd7NwwOUOHPKlkfEeVKG4QAt66XCUMZaTxtUA/out-0.webp

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 239
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

LoRA integrationImage InpaintingText-to-Image GenerationImage EditingCustomizable DimensionsDiffusion Parameter ControlBatch Image Generation

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