jdreetz Fans Toys Generator

Unleash your creativity with the jdreetz Fans Toys Generator! Easily create and edit captivating fan art and toy designs using text prompts and image editing tools.

Platform: Replicate

Fan Art GeneratorImage InpaintingLoRA IntegrationDiffusion Models

654 runs

H100

License Check Required

🚀Function Overview

Generates customizable images of fan-designed characters and toys using text prompts, supports inpainting and style customization via LoRA models.

Key Features

Text-to-image generation with prompt triggers
Image-to-image and inpainting capabilities
Dual model options (dev/schnell) for quality/speed tradeoffs
LoRA weight scaling for fine-tuned style control
Customizable resolution and aspect ratios
Support for HuggingFace/CivitAI model integration

Use Cases

•Creating fan art character designs
•Generating toy concepts from descriptions
•Editing existing images with inpainting
•Prototyping merchandise designs

⚙️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",
  "width": 1024,
  "height": 1024,
  "prompt": "a masterpiecescale dark gray mecha with sparse yellow-orange accents. he has a human like face with a helmet that's brim extends out past the front of his face and two yellow orange circles on each side. he has silver skin with few details, if any (clean lines). his armor covers his foreams, shins, and knees. his armor is angular and simple, with a few bits of text on it in japanese. it has various vents and clean lines on it. it has a extruded symbol of fire across his wide chest. his body is facing away from the camera, with his head angled towards the camera. the camera is slightly lower than shins, but facing upwards at him. his legs are slightly longer than usual. his quads and biceps are slightly muscular, but his torso is small. he is heroic, but slightly stand-offish. ready to fight, but unsure whom to trust. his hands should be balled into fists, with a simple sword in his right hand, that hangs down slightly because of the weight. ",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "9:16",
  "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/a5Qh2AdUxJ6lHxlhlrTErczXc9hYH0rkx9ZxY3YcEiZqYrMF/out-0.webp

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 654
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Fan Art GenerationToy DesignImage InpaintingLoRA IntegrationText-to-ImageImage CustomizationCharacter PrototypingMerchandise Design

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