ModelsLab/Amateur Photography [Flux Dev] - V2.0

amateur-photography-flux-dev-v2-0

Text to Image Community ModelFree for Premium Users LLMs.txt

GPT4o Prompt:

I am planning to train a LoRA for the Stable Diffusion text-to-image model, which uses the T5XXL transformer in its architecture. The prompts should be in natural language and follow a specific format. I will upload images and need you to help me create detailed prompts based on those images. The prompts should start with "Amateur photography of" and end with "on flickr in 2007, 2005 blog, 2007 blog." Always give me the prompt in a single paragraph.

The format should be:

Subject Description: Start by describing all the people in the image in detail. It is very important to include their race and ethnicity, physical attributes (such as height, build, skin tone, and hair color), facial features, attire, and any expressions or poses they are making. Be as specific as possible. Make sure to always include the build of the subjects (e.g., plus size, slim, petite) without missing it.

Scene Description: Accurately convey what exactly the people are doing in the picture. Describe the setting, background elements, any objects they are interacting with, and the overall environment (urban, rural, indoor, outdoor, etc.).

Image Quality Tags: Include descriptive tags that highlight the quality of the image. Use terms like slight motion blur, cluttered background, warm tones, bright natural light, high contrast, vivid colors, etc. These tags should reflect the mood and feel of the image as well.

The final output should combine all these elements into a cohesive, detailed prompt that accurately reflects the image.

API Playground API Documentation

API Endpoint URL

Base URL for all API requests to this endpoint.

https://modelslab.com/api/v6/images/text2img

API Authentication

Authentication requires a valid API key included in the request. Generate and manage your API keys from your developer dashboard. Include the key in the key parameter for all API requests.

Integration Examples

Production-ready code samples for API integration

{
  "prompt": "R3alisticF, hauntingly beautiful oriental necromancer, long flowing brown hair, bangs, darkly tanned skin, earrings, bone necklaces, dark eyeshadow, red lips, vibrant, front-laced transparent, filmy silk blouse, cleavage, holding skull, in a sandstone room lit by candles, High Detail, Perfect Composition, high contrast, silhouetted, chiascuro",
  "model_id": "fluxdev",
  "lora_model": "amateur-photography-flux-dev-v2-0",
  "width": "1024",
  "height": "1024",
  "negative_prompt": "(worst quality:2), (low quality:2), (normal quality:2), (jpeg artifacts), (blurry), (duplicate), (morbid), (mutilated), (out of frame), (extra limbs), (bad anatomy), (disfigured), (deformed), (cross-eye), (glitch), (oversaturated), (overexposed), (underexposed), (bad proportions), (bad hands), (bad feet), (cloned face), (long neck), (missing arms), (missing legs), (extra fingers), (fused fingers), (poorly drawn hands), (poorly drawn face), (mutation), (deformed eyes), watermark, text, logo, signature, grainy, tiling, censored, nsfw, ugly, blurry eyes, noisy image, bad lighting, unnatural skin, asymmetry",
  "num_inference_steps": "31",
  "scheduler": "DPMSolverMultistepScheduler",
  "guidance_scale": "7.5",
  "enhance_prompt": false,
  "key": "YOUR_API_KEY"
}

SDKs

Official SDKs

Production-ready SDKs and client libraries for all major programming languages

JavaScript

Python

PHP

API Parameters

Technical specifications for API request parameters.

Field Name	Parameter	Technical Description
prompt	`prompt`	prompt help in image generation
Model	`model_id`	Enter model_id that can help in image generation
lora Model	`lora_model`	No description available
Width	`width`	width of the image
Height	`height`	height of the image
Negative Prompt	`negative_prompt`	Negative prompt help in avoid things that you do not want in image
Steps	`num_inference_steps`	Number of inference steps
Scheduler	`scheduler`	Sampling scheduler
Guidance Scale	`guidance_scale`	How closely to follow the prompt (1-10)
enhance_prompt	`enhance_prompt`	Automatically enhance the prompt