Beaver AI Image Generator

Unleash the power of Beaver AI to effortlessly generate stunning environmental visualizations and ecological habitat depictions.

Platform: Replicate

Environmental VisualizationBeaver Habitat GeneratorImage RestorationLoRA Integration

15 runs

H100

License Check Required

🚀Function Overview

Generates and modifies images of creek ecosystems with beaver presence through text prompts, image inputs, and inpainting capabilities.

Key Features

Text-to-image generation with trigger word activation
Image-to-image transformation and inpainting support
Adjustable dimensions and quality parameters
Dual model selection for speed/quality tradeoffs
LoRA (Low-Rank Adaptation) scaling and integration

Use Cases

•Visualizing creek restoration with beaver dams
•Generating ecological habitat depictions
•Environmental education material creation
•Restoring/modifying landscape photos

⚙️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

{
  "image": "https://replicate.delivery/pbxt/My9SMGKJcjsHzxykW9SsFmPinOvCCD8znvY7qmwEd0OFvdL0/test_1.jpg",
  "model": "dev",
  "prompt": "Restore this exact creek segment using BEAVERAI. Keep same landscape and camera view. Add a low beaver dam with woody debris in water. Increase water depth, form small ponds and flooded margins. Lush green sedges, grasses, and willow sprouts fill the riparian zone. No change to uplands or horizon.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.6,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/zfuqJOgQerhTaUHX86FH7wIafgk9vT4h9XVUiefhK7tCRBTlC/out-0.png

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 15
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Environmental VisualizationEcological Habitat DepictionImage-to-Image TransformationText-to-Image GenerationLandscape RestorationEducational Material CreationLoRA Integration

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