Projects

### Overview: Flow Music Prompts: A collection of 8 tested recipes for generating high-quality image prompts using the Flow Music model. These recipes are designed to provide clear and effective instructions for generating professional-looking images at a low cost. ### Key Features: - **Model ID**: `flowmusic` - **Cost per Image**: $0.01 per second - **Example Renders**: 4 recipes with detailed prompts and examples - **Aspect Ratios**: 85mm, 24mm, macro, overhead flat lay - **Pricing**: Estimated cost of $0.04 for the entire set ### Tech Stack: Python ### Why It Matters: - **Cost Efficiency**: Generates high-quality images at a low cost, making it ideal for creative projects on a budget. - **Model Compatibility**: Compatible with OpenAI models, allowing seamless integration with other tools and platforms. - **Pricing Transparency**: Real-time billing and clear pricing information, ensuring cost transparency and ease of use. ### Repository: - **Repository**: `jyp5o6wi5bbg1/flow-music-prompts-id` - **Stars**: 13 - **Description**: A collection of 8 tested recipes for generating image prompts using the Flow Music model. This repository includes detailed prompts, examples, and pricing information.

### Overview: 通义万相 — Wan 3.0 API (`wan3.0-video`): 通义万相提供了一站式的视频模型访问，包括文本到视频、图像到视频和文件/网页生成。只需一个 API 密钥，即可访问 Wan 3.0（`wan3.0-video`）以及其他 300 多个模型，无需预付费套餐。最低充值 $1，按量付费，费用随响应一并返回。 ### Key features: - **一个 API 密钥，整个目录访问**：同一个 `https://api.apimart.ai/v1` 基础 URL 和 `Authorization` 请求头即可访问 Wan 3.0（`wan3.0-video`）以及图像、视频、语言等 300+ 模型。 - **$1 起充，按量付费**：没有订阅、也没有需要预先估算用量的预付费套餐：最低充值 $1，调用多少扣多少。 - **费用随响应一并返回**：每个完成的任务都会报告实际计费金额（`cost` / `credits_cost`）；状态为 `failed` 的任务会自动退款。 - **原生异步设计**：提交任务、拿到 `task_id`、轮询 `GET /v1/tasks/{id}`——批处理和重试都是普通的队列工作，不需要定制集成。 ### Tech stack: Python ### Why it matters: - **一个 Key，整个目录**：同一个

### Overview: Seedance 2.0 Fast is a faster version of ByteDance's Seedance 2.0 route on APIMart, providing text-to-video and image-to-video functionalities. It offers a per-generated-second pricing model, allowing direct access to the model through a single API key without the need for a subscription. ### Key Features: - **Direct Access:** Utilizes a single `Authorization` header for accessing the model through the APIMart endpoint. - **Pricing Model:** Per-generated-second pricing, billed by resolution. - **Model Compatibility:** Compatible with OpenAI-compatible APIMart endpoint, providing a wide range of models and parameters. - **Model Page:** Accessible through the model page provided in the README. ### Tech Stack: - Python ### Why It Matters: - **Cost Efficiency:** The per-generated-second pricing model makes it highly cost-effective, eliminating the need for a subscription. - **Model Flexibility:** Direct access to the model through a single API key allows for easy integration and experimentation. - **Model Diversity:** Access to over 300+ models, including text-to-video and image-to-video functionalities, providing versatility in content creation. ### Repository: - Repository: `k9fc4/seedance-2-0-fast-api-ko` - GitHub Stars: 13 - GitHub Description: Seedance 2.0 Fast (`doubao-seedance-2.0-fast`): model id, pricing, parameters, and copy-paste examples for the OpenAI-compatible APIMart endpoint.

Overview: RAG-Gemini is an implementation of the Retrieval-Augmented Generation model in TypeScript, utilizing the Gemini LLM. Key features: - Supports TypeScript development - Integrates with Gemini LLM for advanced language generation - Provides enhanced text generation capabilities through retrieval-based augmentation Tech stack: TypeScript Why it matters: This project showcases the integration of cutting-edge language models with modern programming languages, offering developers a powerful tool for text generation tasks. Repository: kanugurajesh/RAG-Gemini GitHub stars: 13

Overview: This repository contains tested image prompt recipes for Seedream 5.0 Lite, including 8 recipes with example renders, aspect ratios, the model ID (`seedream-5-0-lite`), and the cost per image. Key features: - 8 Seedream 5.0 Lite prompts - Example renders, aspect ratios, and model ID - Cost per image Tech stack: Python Why it matters: This repository provides practical image prompt recipes for Seedream 5.0 Lite, offering a straightforward way to generate high-quality images without the need for complex integration or prepayment plans.

### Overview: This repository contains tested Veo 3.1 prompts with example renders, aspect ratios, the model id (`veo3.1-fast-ext`), and the cost per image. The prompts are designed to help developers create high-quality images efficiently. ### Key features: - **Model ID:** `veo3.1-fast-ext` - **Render Examples:** 8 different recipes with their corresponding durations and costs. - **Cost per Image:** $0.064 per second, with a total estimated cost of $0.256 for the set. ### Tech Stack: Python ### Why it Matters: The repository provides a practical guide for developers to create effective Veo 3.1 prompts, ensuring high-quality images without the need for expensive subscription models.

Overview: MiniMax Hailuo 2.3 API is a comprehensive endpoint for OpenAI-compatible API Mart, providing text-to-video and image-to-video functionalities with detailed pricing and parameters. Key features: - Supports OpenAI-compatible endpoint - Offers text-to-video and image-to-video capabilities - Supports multiple resolutions and aspect ratios - Provides detailed model id, pricing, and parameters - Supports API key-based access and real-time monitoring Tech stack: Python Why it matters: This API is crucial for developers looking to integrate cutting-edge AI video generation into their projects, offering high-quality results with flexible pricing and easy integration. Repository: kdnde24c3s5g/hailuo-2-3-api-id Primary language: Python GitHub stars: 13 GitHub description: MiniMax Hailuo 2.3 API (`MiniMax-Hailuo-2.3`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

Overview: Grok Imagine 1.5 API (`grok-imagine-1.5-apimart`) is a model id priced per image with edits included, accessible via an OpenAI-compatible endpoint. It offers a single key for entire access to 300+ models, with a $1 minimum charge for each task and real-time pricing. Key features: - A single key for entire access to Grok Imagine 1.5 - $1 minimum charge per task, with real-time pricing - Supports 300+ models, including image, video, and language generation - Charges based on the actual cost of the response - Supports both default and custom model parameters - Supports image URLs and base64 data URLs as references Tech stack: Python Why it matters: This API provides a cost-effective way to generate images and videos using Grok Imagine 1.5, with real-time pricing and easy integration through a single key. Repository: ke6yg044xwihmo/grok-imagine-1-5-api-cn Primary language: Python GitHub stars: 13

Overview: Seedance 2.5 API, developed by ByteDance, offers a video model with cinematic motion, audio synchronization, and per-second billing by resolution, making it compatible with OpenAI's API. Key features: - Compatible with OpenAI's API - Per-second billing by resolution - Asynchronous processing - Supports 480P, 720P, and 1080P resolutions - Includes a model page for detailed parameters - Supports prompt descriptions, duration, resolution, and image references Tech stack: Python Why it matters: Seedance 2.5 API provides a cost-effective and efficient solution for generating high-quality video content using the OpenAI-compatible API, making it ideal for developers looking to integrate video generation into their applications without the need for a monthly subscription. Repository: khigrnp/seedance-2-5-api-es GitHub stars: 9 GitHub description: Dreamina — Seedance 2.5 API (`seedance-2.5`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

Overview: Dive into the "Reinforcement Learning from Human Feedback" course, taught by Nikita Namjoshi, to align Large Language Models (LLMs) with human values and preferences using Reinforcement Learning from Human Feedback (RLHF). Key features: - Conceptual Understanding of RLHF - Supervised Fine Tuning - RLHF Training with Datasets - Fine-Tuning with Google Cloud Pipeline Components Library - Model Evaluation Tech stack: Jupyter Notebook Why it matters: This course provides a practical guide on how to align LLMs with human values using RLHF, making it essential for developers interested in the intersection of AI and ethics.

Overview: KosmosG is a model implementation based on "KOSMOS-G: Generating Images in Context with Multimodal Large Language Models". It aims to enhance the understanding and generation of images using multimodal large language models. Key features: - Multi-modality understanding - Integration of text and image inputs - Implementation in Python using PyTorch Tech stack: Python, PyTorch Why it matters: This model could significantly improve the ability to generate images that are contextually relevant and coherent, leveraging the strengths of large language models to enhance their multimodal capabilities. Repository: kyegomez/KosmosG Primary language: Python GitHub stars: 13 GitHub description: My implementation of the model KosmosG from "KOSMOS-G: Generating Images in Context with Multimodal Large Language Models" README excerpt: [](https://discord.gg/qUtxnK2NMf)

Overview: This project integrates the DeepSeek Harness agent with the DSH Doubao desktop client, allowing for the seamless transfer of generated images directly from the backend without any watermarks. The process involves capturing the clean, high-resolution original images from the backend, ensuring that the images are of the highest quality without any modifications. Key features: - Seamless integration of DeepSeek Harness agent - Direct transfer of clean, high-resolution original images from the backend - No need for API keys or per-image fees - No need for watermark removal tools Tech stack: JavaScript Why it matters: This project offers a cost-effective and efficient solution for generating high-quality images without compromising on quality or the original image integrity. It simplifies the process of obtaining clean, high-resolution images from the backend, making it ideal for applications where watermark removal is unnecessary or undesirable. Repository: liangzz1998/dsh-doubao-painter Primary language: JavaScript GitHub stars: 12 GitHub description: ????????????????? DeepSeek Harness:agent ????????,?? CDN ????? 2K ????? CDP ??,??? API Key?????????? README excerpt: ????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ????????????????????? ?????????????????????

### Overview: A production-grade multi-agent system leveraging ReAct orchestration, MCP, A2A, and LMMs for hyper-local property valuation with Evals. ### Key Features: - Multi-Agent Orchestration (LangGraph) - MCP and A2A Integration - Advanced RAG & Vector Search - Large Multimodal Models (LMM) in Production - Classical ML Meets Agentic AI ### Tech Stack: PHP ### Why It Matters: This project demonstrates a cutting-edge implementation of modern AI and ML, providing a production-grade, multi-agent system for complex reasoning, valuation, and analysis in real estate.

### Overview: Qwen-Image 3.0 Prompts: 8 Tested Recipes With Outputs ### Key Features: - **Model ID:** `qwen-image-3.0` - **Recipes:** 8 (machine-readable in `data/prompts.json`, table in `PROMPTS.md`) - **Effective Price:** $0.0206 per image (estimated cost of the whole set: $0.1646) ### Tech Stack: - Python ### Why It Matters: - **Key:** Same Base-URL `https://api.apimart.ai/v1` and same `Authorization`-Header for Qwen-Image 3.0 - **Billing:** No Abo or Prepaid Tarif, pay-as-you-go - **Cost:** $0.0206 per image - **Asynchronous:** Absendungen, `task_id` entgegennehmen, `GET /v1/tasks/{id}` pollen — Batching and Retries are normal Queue-Arbeit, no special integration ### What Separates a Usable Recipe from a Wasted Call: - **Rule 1:** Name the material, not the adjective ("Frosted glass" vs "beautiful") - **Rule 2:** Set the light explicitly ("Single softbox from the left" vs "golden hour backlight") - **Rule 3:** Pin the framing ("85mm", "24mm", "macro", "overhead flat lay") decides whether the output drops into a layout unchanged ### Prompt Structure

### Overview: GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions, published in ICCV 2019. This repository contains code for the paper. The overall architecture is depicted in the figure, with additional details provided in the paper. ### Key features: - GAN-Tree: An Incrementally Learned Hierarchical Generative Framework - Multi-Modal Data Distributions - Code for the paper ### Tech stack: Python ### Why it matters: The GAN-Tree framework is designed to handle multi-modal data distributions in a hierarchical manner, offering a scalable and efficient approach for generating new data points. This is particularly useful in scenarios where data is distributed across multiple modalities, such as images and text, and requires a generative model that can learn from these distributions incrementally.

### Overview: The Super Rapid Annotator project is designed to streamline video annotation tasks by integrating advanced multimodal vision and language models with spatiotemporal analysis. This tool addresses the inefficiencies of traditional annotation methods, particularly in handling multiple videos simultaneously, by leveraging state-of-the-art models and efficient deployment strategies. ### Key Features: - **Multimodal Vision and Language Model Integration**: Seamlessly combines images, videos, and text inputs to generate high-quality annotations. - **Spatiotemporal Analysis**: Analyzes video content in terms of spatial and temporal relationships to identify entities and their interactions. - **Efficient Deployment**: Utilizes lightweight models and efficient deployment strategies to ensure quick and accurate annotation. ### Tech Stack: - **Python**: The primary programming language used for development. - **PyTorch**: Framework for building and training deep learning models. - **Hugging Face**: Hosting and deploying the model through Gradio. - **Pydantic**: For defining and validating data models. - **OpenCV**: For image processing and video analysis. - **FFmpeg**: For video processing and manipulation. - **FastAPI**: For building the web application interface. ### Why It Matters: The Super Rapid Annotator project addresses the limitations of traditional annotation methods by providing a more efficient and accurate way to annotate videos. This tool is particularly useful in scenarios where multiple videos need to be annotated simultaneously, making it invaluable for researchers, educators, and anyone involved in video analysis tasks.

Overview: This repository provides a complete deployment kit for running the Qwen-Image-2.1 Uncensored model in ComfyUI, including workflow patches, a 14-second lossless Q8_0 quantization repair tool, and a GGUF-based ComfyUI-GGUF architecture patch. The model weights are not included, and the repository is designed to be used with an 8 GB VRAM GPU. Key features: - Workflow patches - 14-second lossless Q8_0 quantization repair tool - GGUF-based ComfyUI-GGUF architecture patch Tech stack: Python Why it matters: This repository offers a seamless solution for deploying the Qwen-Image-2.1 Uncensored model in ComfyUI, addressing common issues such as missing GGUF metadata, duplicate GGUF files, and incorrect Q8_0 quantization. The 14-second lossless repair tool ensures high-quality outputs without additional downloads, making it ideal for users with limited bandwidth or storage constraints. Repository: mark2008chen-sudo/qwen-image2.1-uncensored-comfyui GitHub stars: 13

Overview: This repository contains open-source AI demos that can be understood, run, and remixed. It offers small, reproducible builds for vision, multimodal models, local or hosted inference, and more. Key features: - Vision: Draw object boxes with a vision model - Documents: Read form checkboxes - Model(s): Multiple models verified with the same code Tech stack: Python Why it matters: This repository strips down each demo to its essential core, making it easy to learn, run, and modify. It is not a production-level framework, but a simple, readable build that can be adapted for various applications.

Overview: This project focuses on distance computation for vector databases, aiming to provide an effective and general solution for approximate nearest neighbor search. Key features: - Distance computation for vector databases - Implementation of ADsampling and FINGER algorithms - Support for both SIMD and non-SIMD acceleration - Utilizes C++ and Python environments Tech stack: C++ Why it matters: This project offers a practical solution for distance computation in vector databases, enhancing the efficiency and accuracy of approximate nearest neighbor search, which is crucial in various applications such as recommendation systems and image retrieval.

Overview: Finetune Web UI is a user-friendly interface designed for training and deploying pre-trained machine learning models, specifically focusing on fine-tuning Llama and GPT models. Key features: - Easy dataset upload - Custom model fine-tuning - Hyperparameter tuning - Real-time monitoring - Simplified deployment - Model management - Clean and intuitive interface - Open source - Customizable - Cross-platform - Scalable Tech stack: Python Why it matters: Finetune Web UI simplifies the process of fine-tuning pre-trained models, making it accessible for data scientists and engineers without requiring deep technical knowledge. GitHub stars: 13 Repository: muhammad-fiaz/finetune-web-ui