Dandan Actor Professional Headshot Creator

Unlock your acting potential with Dandan Actor, the AI-powered professional headshot creator. Generate cinematic portraits and perfect your portfolio.

Platform: Replicate

Headshot GenerationPortrait PhotographyStyle-Based Image CreationActor Portraits

148 runs

H100

License Check Required

🚀Function Overview

Generates professional actor headshots inspired by photographer Daniel Spencer's style through textual prompts, image inputs, and precise control over composition/styling parameters.

Key Features

Text-to-image generation with style activation triggers
Image-to-image and inpainting capabilities
Adjustable aspect ratios and custom dimensions
Model selection (dev/schnell) for quality/speed tradeoffs
LoRA scaling for style intensity control
Precision controls for denoising steps, guidance scale, and output quality

Use Cases

•Professional actor portfolio headshots
•Cinematic-style portrait generation
•Editing/enhancing existing headshots
•Photography style experimentation

⚙️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": 540,
  "height": 720,
  "prompt": "A professional headshot portrait of an actor in dandan style. The subject is centered with a dead pan expression, wearing a simple outfit, body angled 45 degrees away from camera, The background is softly blurred with muted tones (brown, gray, green, or blue), creating a cinematic and sophisticated atmosphere, The lighting is soft and directional, highlighting the subject's facial features, detailed hair, relaxed portrait photography capturing photorealistic skin textures, sharp eyes, natural hair color, and subtle shadows, The overall mood is serious and contemplative, emphasizing the subject's presence and character, High-quality photography, cinematic lighting, shallow depth of field, Canon R6, Canon 70-200mm F2.8",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "custom",
  "output_format": "jpg",
  "guidance_scale": 1.5,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 30
}

Output Results

https://replicate.delivery/xezq/Zyz6egG2H8RoPq6tHlMOmgpLZIcFiExwtot45b4gAjCCeBoUA/out-0.jpg

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 148
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Actor HeadshotsCinematic Portrait GenerationProfessional Actor PortfolioImage-to-Image EditingStyle ControlText-to-Image Generation

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