Cast Photos Image Generation

Cast Photos is a powerful image generation model for creating and editing stunning visuals. Discover how this AI model can transform your workflow!

Platform: Replicate

Image EditingPhoto GenerationLoRA Fine-tuningInpainting

79 runs

H100

License Check Required

🚀Function Overview

A diffusion-based image model that creates and modifies photos using text prompts, input images, masks, and LoRA adjustments for customized outputs.

Key Features

Text-to-image generation from prompts
Image-to-image transformation and inpainting
Multiple model modes (dev/schnell) for quality-speed tradeoffs
LoRA fine-tuning support for concept activation
Customizable resolution, aspect ratio, and output formats

Use Cases

•Creating realistic photos from text descriptions
•Editing existing images with inpainting masks
•Generating themed visual content using LoRA embeddings
•Producing documentary-style or portrait photography

⚙️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": "A head and shoulders realistic photograph of jwa2 as a man in a red jumpsuit, against a white background. The jumpsuit is crumpled and a bit dirty. He's looking into the camera, with a big cheeky smile. He's holding his hand out, semi shielding his face, as if he doesn't want his picture taken. He's sweaty. The style is that of a functional photobooth photograph taken with a flash, the shadow showing on the wall behind, with flat lighting and filmic grain. It must look very realistic, like a 70s documentary photograph.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 3,
  "aspect_ratio": "1:1",
  "output_format": "jpg",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/Uerredv7XPkjrkxdZKQ23N3dRK8GzZwfH4pVY74dFJ8P5GqpA/out-0.jpg

https://replicate.delivery/xezq/lfiTRNxCK73ubCXNefRujyEb9W15l9ya2FeteEbSV3P4kbomC/out-1.jpg

https://replicate.delivery/xezq/ZuWzUl0CrHIgAt0qQjkbdfTxl1CvfiK9PNLI58shQyYncD1UA/out-2.jpg

Quick Actions

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Technical Specifications

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

Related Keywords

Text-to-image generationImage-to-image transformationLoRA fine-tuningCreating realistic photosEditing existing imagesGenerating themed visual content

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