Flux Prehistoric Art

Discover Flux Prehistoric Art, a powerful tool for generating stunning prehistoric-themed artwork from simple text prompts.

Platform: Replicate

Prehistoric Art GeneratorImage InpaintingLoRA IntegrationStable Diffusion

1.4k runs

H100

License Check Required

🚀Function Overview

Generates images in a prehistoric art style using diffusion models, supporting text-to-image conversion, image inpainting, style customization via LoRA weights, and multiple output configurations.

Key Features

Text-to-image generation with trigger words for better style activation
Image-to-image transformation and inpainting capabilities
Multiple aspect ratios and custom dimension settings
Choice between detailed (dev) and fast (schnell) inference models
LoRA integration for custom style/object adaptation
Adjustable denoising steps and guidance scales
Safety checker toggle for content filtering

Use Cases

•Creating prehistoric-themed artwork from text descriptions
•Editing/modifying existing images through inpainting
•Style transfer using custom LoRA weights
•Generating concept art for games or educational content
•Rapid prototyping of artistic concepts

⚙️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": "TOK A solemn woman stands before a tribunal of church officials in a medieval courtroom, sunlight piercing through stained glass, in Renaissance-style watercolor painting, warm tones and aged parchment background, painterly texture, emotionally tense mood, side composition\n\n",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "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/y4Fyj8lEfIwVLK5Pb1fAzx1m767lQCyRWgXA6fEUUXe5BERTB/out-0.webp

Quick Actions

Use NowView Documentation

Technical Specifications

Hardware Type: H100
Run Count: 1.4k
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Prehistoric Art GenerationImage InpaintingLoRA IntegrationText-to-ImageImage-to-Image TransformationStyle CustomizationConcept Art Generation

Related Models

Divin AI Image Generation

Un modèle qui génère des images qui ressemble à 100/100 Divin

SarthakD Image Generator

A model for generating and editing images based on text prompts using diffusion techniques.

WAW PFP Flux V2

A text-to-image model to generate real images of american women that look like they're shot on an iPhone