Prashant Flux LORA

Unleash creative potential with Prashant Flux LORA, an advanced image generation model. Ready to experience the power of AI? Start your journey here!

Platform: Replicate

Image GenerationImage InpaintingLoRA ModelDiffusion Model

79 runs

H100

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🚀Function Overview

A diffusion-based image generation model using LoRA adapters for style or object control, supporting text-to-image, image-to-image transformations, and inpainting with customizable parameters.

Key Features

Generates images from text prompts with optional trigger words
Supports image-to-image and inpainting modes using masks
Adjustable image dimensions, quality, and denoising steps
FP8 quantization for faster execution
Multiple LoRA scale adjustments for style control

Use Cases

•Creating custom portraits (e.g., professional LinkedIn photos)
•Transforming existing images via inpainting or style transfer
•Generating concept art from descriptive text prompts
•Applying specialized LoRA adaptations for personalized styling

⚙️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": "Professional portrait of Prashant for LinkedIn. He should have a confident and approachable expression, exuding professionalism and charisma. Prashant is dressed in a well-fitted, modern linen greyish-blue business suit with a crisp shirt. The background should be clean and neutral, with soft lighting that highlights his face and brings out his sharp features. The image should convey a sense of sophistication, success, and leadership, ideal for a LinkedIn profile, while maintaining a polished, corporate look. DSLR quality image.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "jpg",
  "guidance_scale": 2,
  "output_quality": 80,
  "prompt_strength": 0.7,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/a6d4D3fYfouYUEqoW6BquXe7fxd5LkxYNZ2VZcL2vga3qYuSB/out-0.jpg

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Technical Specifications

Hardware Type: H100
Run Count: 79
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Image GenerationLoRA AdaptationsImage InpaintingText-to-ImageImage-to-ImageStyle ControlConcept Art

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