Qolaba

GETTING STARTED

Introduction

Authentication

Pricing

Errors

API REFERENCE

Text To Image

Image To Image

ControlNet

Inpainting

Replace Background

Face Consistency

Face Avatar

Image Variation

Illusion Diffusion

Upscaling

Background Removal

Text To Speech

Get Status

ChatBot API

Store File in Vector Database

FastAPI

ML APIs

Gen AI API's

Production

Sandbox

The GETTING STARTED section provides essential information for developers to quickly familiarize themselves with the FastAPI API. Users can learn how to authenticate, make basic API requests, and navigate through the documentation to get started with integrating FastAPI into their projects efficiently.

-> Each model is uniquely characterized by its own app_id.

app_id

-> The height parameter represents the vertical dimension of an image.  -> The valid range for the parameter is between 256 and 1536 pixels. 

height

-> The width parameter represents the horizontal dimension of an image.  -> The valid range for the parameter is between 256 and 1536 pixels. 

width

-> The num_inference_steps parameter represents the number of denoising iterations to perform during the image generation process. Generally, more iterations can result in higher-quality images, but they also increase the time required for generation. -> The valid range for the num_inference_steps parameter is between 1 and 50.

num_inference_steps

-> The guidance_scale parameter determines how closely the generated image adheres to the provided prompt. Higher values result in the model following the prompt more closely, while lower values allow for more creative deviation. -> The valid range for the guidance_scale parameter is between 1 and 30.

guidance_scale

-> The batch parameter allows you to specify the number of images to generate at once.  -> The valid range for this parameter is between 1 and 8.

batch

-> The prompt parameter is the textual input that guides the image generation process. This prompt serves as an artistic compass, shaping the visual output.

prompt

-> The negative_prompt parameter allows you to specify content that you want the image generation model to avoid or minimize in the output. This can be useful for excluding certain visual elements or styles that you do not want to be present in the generated image.

negative_prompt

-> The celery parameter is used for queuing tasks that require extended processing time. When you enqueue a task, you receive a unique task_id. This task_id allows you to check the task's status later using the task status API, which is useful for managing and tracking long-running tasks.

celery

-> The inference_type parameter allows you to specify the GPU to be used for the image generation task. The supported values are: a10g a100 h100 This parameter is only applicable for Qolaba-deployed models, including Turbo Vision, Qolaba Style, Cartoon, Realistic, and Anime Style. The different GPU options provide varying levels of performance and capabilities, allowing you to choose the most suitable GPU based on your requirements and the demand for the task.

inference_type

Authorization

-> The file_url parameter specifies the URL of an existing image that will be used as a reference for the generation process.  -> If the original image dimensions exceed 1536x1536 pixels, the image will be adjusted to fit within this size while preserving the original aspect ratio.

image

-> The strength parameter specifies the degree of transformation applied to the reference image.  -> A higher strength value (up to 1) results in the generated image deviating more from the initial reference image, as more noise is introduced. A strength of 1 completely disregards the initial image.

strength

-> The height parameter represents the vertical dimension of an image.  -> The valid range for the parameter is between 256 and 1536 pixels.  -> This parameter is only applicable for the SDXL API, as it requires specific height and width values.

-> The width parameter represents the horizontal dimension of an image.  -> The valid range for the parameter is between 256 and 1536 pixels.  -> This parameter is only applicable for the SDXL API, as it requires specific height and width values.

-> The strength parameter specifies the degree of transformation applied to the reference image. 

-> The inference_type parameter allows you to specify the GPU to be used for the image generation task. The supported values are: a10g a100 h100 The different GPU options provide varying levels of performance and capabilities, allowing you to choose the most suitable GPU based on your requirements and the demand for the task.

-> The mask_url parameter is used for providing image mask. It specifies a binary mask image that defines the regions of the input image that should be inpainted.

mask_url

The concept of a &quot;Consistent Face Model&quot; involves the creation of images where the face maintains a consistent appearance across multiple generated Images. To achieve this, you start with an initial facial image and provide it as a reference. Using this reference, a sophisticated model goes to work, crafting new images while keeping the facial features in line with the original picture. In essence, the model uses your input to generate images where the face remains faithful to the structure and characteristics you&apos;ve specified in your prompt. This way, you can effortlessly create a series of images with a consistent and recognizable faceFor more insights and a deeper dive into how it all works, you can find a treasure trove of details right over <a href="https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter-plus-face_demo.ipynb" target="_self">here</a>.

-> The width parameter represents the horizontal dimension of an image. 

-> The strength parameter specifies the degree of transformation applied to the reference image.  -> A higher strength value (up to 1) results in the generated image closely following the reference image.

The concept of a &quot;Face Avatar&quot; involves the creation of images where the face maintains a consistent appearance across multiple generated Images. To achieve this, you start with an initial facial image and provide it as a reference. Using this reference, a sophisticated model goes to work, crafting new images while keeping the facial features in line with the original picture. In essence, the model uses your input to generate images where the face remains faithful to the structure and characteristics you&apos;ve specified in your prompt. This way, you can effortlessly create a series of images with a consistent and recognizable face.Working insights :In this model, the default prompt settings includes a prompt, “black plain background,  VECTOR CARTOON ILLUSTRATION, half-body shot portrait {gender_word},  5 o clock shadow, 3d bitmoji avatar render, pixar, high def textures 8k, highly detailed, 3d render, award winning, no background elements”. If you provide a custom prompt, it will override these default settings. To include a specific gender in your custom prompt, you must explicitly mention it in custom prompt. If you prefer to use the default settings without specifying any custom prompt, simply pass <code class="slate-code">None</code> in the prompt parameter. Subsequently, you can define the gender by using the gender parameter, which will apply the specified gender to the default image configuration.

-> The file_url parameter specifies the URL of an existing image that will be used as a reference for the generation process. 

-> The gender parameter is used to guide the image generation model towards producing outputs with a specific gender representation. This can help avoid gender-related confusion or bias in the AI model's outputs.

gender

-> When the remove_background parameter is enabled, the background of the generated image will be removed and replaced with a transparent background.

remove_background

-> The bg_color parameter allows you to set the background color of the generated image. The color value should be provided in hexadecimal format (e.g., #FFFFFF for white).

bg_color

This model based on <a href="https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter-plus_sdxl_demo.ipynb" target="_self">IP-Adapter</a> possesses the remarkable ability to create diverse visual image variation by drawing inspiration from an input image it receives.

-> The guidance_scale parameter determines how closely the generated image adheres to the provided prompt. Higher values result in the model following the prompt more closely, while lower values allow for more creative deviation.

-> The strength parameter specifies the degree of transformation applied to the reference image.  -> A higher strength value (up to 1) results in the generated image closely following the initial reference image.

-> The num_inference_steps parameter represents the number of denoising iterations to perform during the image generation process. Generally, more iterations can result in higher-quality images, but they also increase the time required for generation.

scale

-> The bg_image parameter allows you to specify a URL for an image that will be used as the background for the generated image.

bg_img

-> To use a custom background color, set the bg_color parameter to true. This will allow you to specify the desired RGB color values for the background.

-> The r_color parameter controls the strength of the red hue in the background color, with a range from 0 to 255. Higher values result in a more vibrant red, while lower values make the red more subdued.

r_color

-> The g_color parameter controls the strength of the green hue in the background color, with a range from 0 to 255. Higher values result in a more vibrant green, while lower values make the green more subdued.

g_color

-> The b_color parameter controls the strength of the blue hue in the background color, with a range from 0 to 255. Higher values result in a more vibrant blue, while lower values make the blue more subdued.

b_color

-> Enable the blur parameter to apply a blurring effect to the background of the generated image, creating a captivating and enchanting visual effect.

blur

-> The prompt parameter is the textual input that guides the audio generation process. This prompt serves as an artistic compass, shaping the audio output. -> The minimum length of the prompt is 10 characters, and the maximum length is 2500 characters.

-> Enable the generate_audio parameter to generate audio output in the form of speech.

generate_audio

-> The audio_parameters parameter is a dictionary that allows you to specify various audio-related settings. Here's an example of the structure: "audio_parameters": {   "voice_id": "21m00Tcm4TlvDq8ikWAM",   "stability": 0.5,   "similarity_boost": 0.75,   "style": null,   "use_speaker_boost": true } You can customize the values within this dictionary to adjust the audio generation according to your preferences.

audio_parameters

-> The id parameter is used to provide the unique identifier of the scheduled task you want to check the status for.

-> The reference_id parameter is an optional field that allows you to provide a reference ID to identify the request, if required. If you did not specify a reference_id when passing the input parameters, there is no need to provide this parameter.

refID

-> The llm_model parameter specifies the name of the Large Language Model (LLM) to be used. The supported values for this parameter depend on the llm you have selected. For example, if the llm is OpenAI, the supported llm_model values include: "gpt-4-turbo-2024-04-09" "gpt-3.5-turbo-0125" Similarly, if the llm is MistralAI, the supported llm_model values include: "mistral-large-latest" "open-mistral-7b" Ensure that the llm_model value you provide is compatible with the selected model_type.  -> Supported values are :  "mistral-large-latest" "open-mistral-7b" "gemini-pro" "gemini-1.5-pro-latest" "gpt-4-turbo-2024-04-09" "gpt-3.5-turbo-0125" "claude-3-haiku-20240307" "claude-3-opus-20240229"

llm_model

-> The temperature parameter accepts a float value between 0 and 1. This parameter helps control the level of determinism in the output from the Large Language Model (LLM).

temperature

-> The system_msg parameter allows you to set a system message for the Large Language Model (LLM). This message can be used to provide context or instructions to the model, which can influence the tone and behavior of the generated responses.

system_msg

-> The llm parameter specifies the type of Large Language Model (LLM) to be used. The supported values are: ClaudeAI MistralAI OpenAI GeminiAI.

-> If you are passing image URLs and want the model to analyze the images, set the image_analyze parameter to true.

image_analyze

-> To use the tools supported by the Chat API, enable the enable_tool parameter. The Chat API currently supports two tools: Vector Search Internet Search -> After enabling the enable_tool parameter, you can provide the details of the tool you want to use in the tools parameter.

enable_tool

-> The history parameter allows you to provide the previous chat history and the last user message. The history should follow a specific pattern: The history should start with a user message. After each user message, there should be a response from the assistant. The last message in the history should be a user message or query. -> The history should be provided as a list of dictionaries, where each dictionary represents a message. The dictionary should have the following structure: {     "content": {         "text": "User query or assistant response",         "image_data": [             {                 "url": "URL of the image",                 "details": "low" or "high" (optional, only for OpenAI models)             }         ]     },     "role": "user" or "assistant" } The image_data parameter is a list that can contain up to 5 image URLs. The details parameter is optional and can be used to specify the level of detail required for the image analysis (only for OpenAI models). Here's an example of the history parameter: "history": [     {         "content": {             "text": "hey",             "image_data": []         },         "role": "user"     },     {         "content": {             "text": "Hello! How can I assist you today?",             "image_data": []         },         "role": "assistant"     },     {         "content": {             "text": "Please analyze this image",             "image_data": [                 {                     "url": "https://res.cloudinary.com/qolaba/image/upload/v1695690455/kxug1tmiolt1dtsvv5br.jpg",                     "details": "low"                 }             ]         },         "role": "user"     } ]

history

-> The tools parameter allows you to specify the tool you want to use with the Chat API. The supported tools and their configurations are as follows: Internet Search Tool: tool_name: "Tavily" tool_type: "InternetSearch" PDF Search Tool: tool_name: "QdrantDB" tool_type: "DBSearch" pdf_references: A list of PDF IDs from which you want to retrieve details. These PDF IDs can be obtained by using the pdfVectorStore endpoint to index the PDFs. -> The tools parameter should be provided as a dictionary with the above structure, specifying the desired tool and its configuration.

tools

take_route

router

The url parameter specifies the URL of the document to be indexed.

-> The voice_id parameter specifies a unique identifier for the voice to be used in the audio generation process.

voice_id

-> The stability parameter controls the stability of the generated audio. Higher values (up to 1) result in more stable output, while lower values can lead to more variable output.

stability

-> The similarity_boost parameter adjusts the similarity boost applied to the generated audio. Higher values (up to 1) result in output that is more similar to the target, while lower values can lead to more variation.

similarity_boost

-> The style parameter allows you to control the style of the generated speech. Higher values (up to 1) can result in more exaggerated or closely following the given voice style, but may also lead to increased instability in the generated speech. -> Setting this parameter to 0.0 (the default) will greatly increase the generation speed.

style

-> When enabled, the use_speaker_boost parameter will boost the similarity of the synthesized speech to the selected voice, at the cost of some generation speed. This option makes the model try to generate speech that more closely aligns with the chosen voice.

use_speaker_boost

This parameter could be utilized to specify the name of tool within given tool_type..

Header Parameters

Body Parameters

Response