Cudatrabajos

Desarrollo e implementación del sistema RoadGuard (Alcance Técnico MVP según Anexo NDA). • Entorno: Desarrollo Headless (SSH/CUDA) en servidor con GPU RTX 3090. • Modelos: Implementación de YOLOv8/v10 para detección de baches, grietas y señales, incluyendo preparación de dataset y ajustes de entrenamiento. • Tracking: ByteTrack para evitar duplicidad de registros. • Georreferenciación: Sincronización de Timestamps vs Logs GPS (interpolación lineal y compensación OFFSET_MS). • Entregables: Pipeline funcional integrado y código fuente documentado.

...corre sin interrupciones perceptibles. El proyecto consiste en el desarrollo de un motor de inferencia de alta performance para la detección, clasificación y seguimiento de múltiples clases de objetos en entornos dinámicos complejos, utilizando hardware dedicado. Implementación de arquitecturas de detección (YOLO/RT-DETR) y algoritmos de tracking.• Optimización de modelos para hardware NVIDIA (CUDA/TensorRT).• Fusión de datos provenientes de sensores externos (Telemetría/GPS) con flujos de video 4K.• Desarrollo de lógica de persistencia en bases de datos geoespaciales. • Seniority comprobable en Python y OpenCV. Experiencia en el ciclo completo de vida de modelos de visión: desde ...

...Engineer - Real-time Edge AI (OpenCV, ONNX, CUDA & TensorRT) Busco un Senior Computer Vision Engineer con experiencia demostrable en Edge AI para desarrollar un sistema de asistencia táctica en tiempo real basado en captura de vídeo externa. Desafío Técnico Principal: El sistema debe procesar un flujo de vídeo HDMI, realizar OCR de alta precisión y detección de objetos, y consultar una base de datos local con una latencia end-to-end inferior a 100ms. Stack Tecnológico Requerido: • Lenguaje: Python 3.10+ con arquitectura OOP escalable (Interfaces abstractas). • Visión: OpenCV avanzado y procesamiento de imágenes para OCR de stacks y botes. • Motores de Inferencia: Experiencia obligatoria con ...

Instalar CUDA, Cudnn, PyTorch para proyecto de Python YOLOv5

Necesito instalar y compilar un programa que es público en mi Ubuntu 20.04.

Hola, Mi hijo busca ayuda con un trabajo: Implementar el algoritmo de multiplicación de matrices con números en coma flotante en las librerías paralelas OpenMP, OpenMPI y CUDA utilizando un ordenador sobremesa. Tareas a realizar: - Implementación en un multiprocesador usando OpenMP - Implementación en un multicomputador usando OpenMPI - Implementación en un coprocesador de tipo GPU usando CUDA - Evaluación de prestaciones usando contadores hardware

Hola Diego, Mi hijo busca ayuda con un proyecto, puedes ayudarlo? Implementar el algoritmo de multiplicación de matrices con números en coma flotante en las librerías paralelas OpenMP, OpenMPI y CUDA utilizando un ordenador sobremesa. Tareas a realizar: - Implementación en un multiprocesador usando OpenMP - Implementación en un multicomputador usando OpenMPI - Implementación en un coprocesador de tipo GPU usando CUDA - Evaluación de prestaciones usando contadores hardware

Buenas tardes marlon, hemos hablado recientemente sobre un trabajo de cuda y openmp, si me das garantía de que me lo haces en menos de 10 días te pagaré lo que me propusistes 24 euros. Gracias. Saludos

Necesito que desarrollen un software para mí. Me gustaría que este software sea desarrollado.

tengo que hacer trabajo openmp y cuda

Hola necesito hacer trabajos de c, omp,cuda.

Cuda , programacion paralela

Otra o no estoy seguro Python Solicito programadores con conocimientos en CUDA

Hola José. Necesito una mano para implementar en CUDA una serie de funciones que tengo escritas en C++. Mayormente manipulación de matrices para entrenamiento de redes neuronales, deep learning. La idea es implementarlas en una DLL CUDA (para que se ejecute en la GPU) y llamarlas desde Excel VBA. Estamos hablando de unas 300 lineas de código (una vez eliminados huecos y comentarios). Pásame, por pavor, una estimación o tarifa horaria, si te interesa el trabajo. Gracias.

Hola Juan. Necesito una mano para implementar en CUDA una serie de funciones que tengo escritas en C++. Mayormente manipulación de matrices para entrenamiento de redes neuronales. La idea es implementarlas en una DLL CUDA (para que se ejecute en la GPU) y llamarlas desde Excel VBA. Estamos hablando de unas 300 lineas de código (una vez eliminados huecos y comentarios). Por cierto, somos colegas, yo también soy ingeniero aeronáutico. Pásame, por pavor una estimación o tarifa horaria, si te interesa el trabajo. Gracias.

Hola allenross356, vi tu perfil y me gustaría ofrecerte mi proyecto. Podemos conversar los detalles en el chat. I'd like to have a simple neural network(3 layers) in CUDA 8 using the back propagation.

Necesito convertir un proyecto que tengo de un algoritmo de compresión de texto llamado lzw a CUDA y que tenga un speed up de al menos 10 veces. Referencia que puede ser de utilidad:

...paralelizar el código siguiente que corresponde al método de jacobi, hecho en C/C++ en cada uno de los paradigmas (memoria compartida (openMP), Memoria distribuida (MPI) y uso hibrido (CUDA). En el código se deben indicar (con comentarios) las partes paralelizadas . En un archivo en pdf, debera decirme como se debe compilar y correr cada archivo, y que tanto esta acelerando su solución con respecto al original que se le envia. (por ejemplo, puede analizar cuanto tiempo se demora en ejecutarse en la versión secuencial que le envío vs cuanto tiempo en cada una de las versiones paralelas (openMP, MPI, cuda). Puede ser en una gráfica de comparación. Adjunto el código en un archivo de texto, el códig...

Por favor, regístrate o inicia sesión para ver los detalles.

Tenemos una aplicación y deseamos programar, utilizando C++, OpenCV, CUDA. En la actualidad está desarrollado en ambiente LabView  * Categoría: IT & Programación * Subcategoría: Desktop Applications * Es un proyecto o una posición?: Un proyecto * Actualmente tengo: Tengo el diseño * Experiencia en este tipo de proyectos: Si (He administrado este tipo de proyectos anteriormente) * Disponibilidad requerida: Tiempo completo * Integraciones de API: Otros (Otras APIs) * Plataformas requeridas: Windows

...Ubuntu and I need the open-source WAN Video 2.7 stack fully installed and running on it. You will connect over AnyDesk, handle the complete setup, pull all required libraries and system dependencies, and verify the application launches cleanly with GPU acceleration enabled. During the session I will stay online to provide root access and restart the box if needed. Please be comfortable working in a CUDA-centric environment, compiling from source when binaries are not available, and troubleshooting driver or codec conflicts that sometimes appear on DGX hardware. Acceptance criteria • WAN Video Open Source 2.7 starts without errors and streams a test feed. • All supporting packages and services are documented in a short README so I can replicate the build later. &...

...Long-term work possible if successful Scope of Work: - Diagnose deep learning pipeline issues - Fix model execution errors - Debug training / inference workflow - Resolve dependency or environment conflicts - Optimize pipeline stability - Ensure end-to-end execution works correctly - Provide brief documentation of fixes Technical Stack: - Python - PyTorch / TensorFlow - HuggingFace / Transformers - CUDA / GPU acceleration - Docker / Linux environment - API integration & Data preprocessing pipeline Requirements: - Strong experience in Deep Learning production workflows - Experience debugging complex AI pipelines - Comfortable working under urgent timelines and ability to start immediately Timeline: Start: Immediately. Expected turnaround: 24–48 hours. Proposal Requi...

I’m building a camera-based ... log every reading with a timestamp, and trigger a visual or audible alert whenever negative emotions are detected repeatedly within a short window. A lightweight dashboard served with either Streamlit or Flask will let me: • watch the annotated video feed • view rolling emotion statistics and charts • review and download the timestamped log of events and alerts Optimisation for Jetson (CUDA, cuDNN, TensorRT where appropriate) is essential, and the finished app should launch from a single command, open the dashboard in a browser, sustain real-time performance, and shut down cleanly. Please keep the code modular and well commented so I can retrain or swap models later and, if convenient, provide a Dockerfile or setup script ...

...personalized address generator written in C/C++ and CUDA. The tool should have the following features: • Read any number of prefix-suffix patterns from the `` file (example format: `Taaa*1111` or `Tbbb*222`). • Launch a GPU kernel to continuously generate wallet addresses and compare each address with all patterns. If a match is found, write the matching address and its private key to disk. • Fully utilize GPU performance, achieving the same speed as my current test version (approximately 8 billion addresses per second). Please display a "addresses per second" counter in real-time during program execution. • Generate a plain text log file recording key events: startup time, device information, running hash rate snapshots, and each match found. ...

... "Zero-Shot" Virtual Try-On pipeline into an existing Flutter/Python e-commerce stack. Technical Stack Requirements AI/ML: Experience with IDM-VTON, Cat-VTON, or OOTDiffusion. Mastery of Stable Diffusion (ControlNet/IP-Adapter) is mandatory. Computer Vision: Expertise in MediaPipe or OpenPose (pose estimation) and DensePose (surface mapping). Backend: Python (FastAPI/PyTorch), gRPC/REST, and CUDA optimization. Frontend Integration: Flutter (Dart) for image handling and state management. Key Deliverables The "Zero-Retrain" Pipeline: A model that accepts a flat garment image and a user photo to produce a drape-accurate result without per-SKU training. Latency Optimization: Implementation of TensorRT or AITemplate to bring inference time under 3 seconds on ...

...post_content string. No Raw HTML: Mapping must use native Divi 5 module settings (Colors, Padding, Fonts, Flexbox) to ensure the layout is fully editable. Technical Stack Language: Python (FastAPI/Flask for backend, PyQt or Streamlit for local UI). Browser Automation: Playwright or Selenium (Stealth mode). OS: Windows 11. Optimization: Must be able to handle local inference calls via RTX 5090 (CUDA). Budget & Milestones ($1000 Total) Milestone 1 ($200): Functional Site Crawler (URL Listing & Selection). Milestone 2 ($400): Core Conversion Engine (Successfully importing a complex Section into Divi 5 at 100% progress). Milestone 3 ($400): Full UI Implementation, Section Slicing, and Local API Integration. Note to Freelancers: I will provide a Reference JSON file ...

...from a watch-list I will provide. Because the cameras operate 24/7 in very mixed environments—low-light corridors, exposed outdoor zones that face rain or glare, and busy high-traffic entry points—the model must remain accurate under those conditions. Solutions that leverage YOLO, TensorFlow, PyTorch, OpenCV or comparable frameworks are fine as long as they run on my existing Nvidia GPU server (CUDA-enabled). Deliverables 1. Trained model files plus any custom scripts. 2. A lightweight API or service (Python preferred) that ingests RTSP streams, performs detection, and triggers my existing alerting webhook. 3. Setup instructions and a brief validation report showing performance in the three stated conditions (night-time, outdoor weather, high traffic). I ...

... Here is what I need delivered: • High-quality masks for every image, respecting a class list that includes typical road-scene elements (road, sidewalk, vehicles, sky, vegetation, building façades, pedestrians) plus key indoor objects you would expect in a café setting (tables, chairs, walls, floor, counter). • A training pipeline in PyTorch or TensorFlow that I can run on Ubuntu 22.04 with CUDA, along with a clear README covering dataset preparation, training, and inference. • A model that reaches at least 0.75 mIoU on a private test split I will share once the annotations are complete. You are free to use tools such as CVAT, LabelMe, Detectron2, DeepLabV3+, SegFormer—or any comparable framework—as long as the final workflow remain...

...into the transcript with millisecond accuracy. Both real-time feedback (small overlay suggestions) and post-video analytics (downloadable PDF/CSV plus on-screen dashboard) are needed. I’m happy for you to build with tools such as OpenCV, MediaPipe, TensorFlow, PyTorch, spaCy or similar—use what you are fastest with as long as the models run efficiently in a web environment (GPU acceleration via CUDA or WebGL is a plus). Deliverables 1. Source-controlled codebase ready to deploy on a standard cloud stack (Docker image or Heroku-style procfile). 2. Front-end UI (React, Vue or vanilla JS) that lets users toggle between real-time and upload modes. 3. Modular inference services for vision and audio that can be retrained or swapped if I add new metrics later. 4. C...

...data and route only the most promising parameter sets back to the gate model. Latencies must stay sub-millisecond from signal to order, so a coherent design for GPU–FPGA–QPU orchestration is essential. Deliverables • A documented architecture diagram showing data flow between classical AI, middleware, and the chosen quantum SDK (Qiskit, Braket or similar). • Clean, modular Python code with C++/CUDA kernels where latency demands it, fully containerised for reproducibility. • Back-test and forward-test reports on at least one major FX pair and a US equity futures contract, including Sharpe, max drawdown, and execution slippage statistics. • Deployment guide for a colocation environment, covering queue management to the quantum back-end and f...

Por favor, regístrate o inicia sesión para ver los detalles.

...job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection model and ROS 2 node • A launch file that brings everything up with default parameters • A brief README explaining setup, parameters and expect...

...environment that emulates Jet Nano hardware for research and development on machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so any teammate can spin up the en...

... • Accepts at least JPEG files for input; adding PNG or BMP later should remain possible. • Generates a short video (MP4 preferred) by feeding the image through Stable Diffusion and WAN2.6. • Interface must feel intuitive for non-technical users while exposing advanced settings in an “expert” panel. • Conversion speed is critical; please optimise GPU utilisation and let me choose device (CUDA / DirectML). • Output parameters—resolution, frame rate, length, prompt text, CFG scale, seed—should all be editable before rendering. Deliverables 1. Executable installer (or portable folder) with all weights and dependencies bundled for offline use. 2. Source code with clear build instructions so I can re-compile if models up...

Backend for a my app using FastAPI, WebSock...and concurrency: Comfortable designing and debugging async workflows. Hands‑on AI integration experience with at least one of: Whisper STT or other speech‑to‑text engines. LLaMA/transformer‑based LLMs or OpenAI‑style APIs. TTS systems such as Coqui, Kokoro, or Piper. Realtime systems: WebSockets, WebRTC, or other low‑latency streaming architectures. Nice to Have GPU & deployment experience: CUDA, GPU environments, and performance tuning (CPU vs GPU). Docker, nginx, PM2, and production deployment pipelines. Background processing: Job queues/workers for heavy audio/video processing. Experience orchestrating long‑running media/AI tasks. Video processing tools: FFmpeg, Wav2Lip, or similar for video generation and post‑processing.

...Predict response to therapy (Responder / Non-responder) Predict survival category Predict recurrence risk For MVP: Start with diagnosis, then add treatment prediction. STEP 2: Setup Development Environment Install Dependencies Python 3.9+ PyTorch MONAI pydicom numpy scikit-learn FastAPI or Flask Example: pip install monai torch torchvision pydicom fastapi uvicorn scikit-learn Setup GPU Local CUDA GPU OR Cloud (AWS/GCP/Azure) STEP 3: PET Scan Dataset Preparation Collect Dataset Public PET database (e.g., TCIA) Research partnership dataset Must include: PET images Diagnosis labels (Optional) treatment outcome labels Organize Data Structure: data/ train/ val/ test/ Handle DICOM Files Use pydicom to read images Convert to 3D tensors Normalize voxel intensity STEP 4:...

...training worker (Docker, from scratch) - PHP/MySQL licensing backend + Stripe webhook integration - Unified cross-platform installer (detects DAWs, installs everything in one pass) - GitHub Actions CI/CD (Windows + macOS builds) - Full Apple + Windows code signing pipeline - Documentation (User Guide + Developer Guide + BYOK Setup) Key technical requirements: - CPU default with automatic NVIDIA CUDA detection for Live Mode - RMVPE primary pitch extraction + user toggle (Harvest/Crepe/FCPE) - High-quality resampling (44.1k-96k) in C++ wrapper - AI Cleaning (de-reverb/isolation) in front of inference chain - Index Rate + .index file exposed in UI/API - Batch processing via ZMQ socket bridge Terms agreed: - Budget: $2,500 (6 milestones) - Timeline: 6 weeks (Feb 23 - Apr 3, 2026) -...

...similar) - Weasyprint or ReportLab for PDF - Typer CLI with subcommands: - transcribe - diarize - lesson-report - aggregate - YAML config file - Logging, progress bars, caching (skip if output exists), error handling Deliverables: - Full repo structure - All source code (src/ layout, CLI, config, prompts, PDF renderer) - Installation instructions for Windows 11 (Python, ffmpeg, Poetry, CUDA) - Example commands - Test guide with sample audio Please show experience with WhisperX / faster-whisper, Pyannote, Ollama, and Weasyprint on Windows + GPU setups in your proposal. Thank you! Vladimir...

...与准确性，同时保持训练过程的稳定性和可读性。 目前的情况 • 代码环境：Python（PyTorch） • 优化重点：提高模型准确性（训练时间和显存占用可稍后微调） • 评估方式：我已准备好一套一致的指标与验证脚本，可即时对比优化前后的表现 你需要完成的工作 1. 审阅现有实现，定位瓶颈与冗余计算 2. 重新实现或改写损失函数（含向后传播部分），确保梯度计算无误 3. 添加必要的张量操作优化（向量化、批处理、内存共享等） 4. 在我的测试集上运行并提交结果报告，其中至少包括： ‑ 指标提升幅度与对比表 ‑ 主要改动点与实现思路 ‑ 后续可扩展或进一步精简的建议 交付标准 • 指标提升需在我提供的基线之上达到统计显著 • 代码应符合 PEP-8，包含注释与简明 README • 所有修改应能在标准 GPU 环境（CUDA 11+）一次性跑通，无额外依赖冲突 如果你熟悉元学习以及高效的 PyTorch 实践，并对性能调优有系统方法，请直接告诉我你做过的相关项目、预计的优化思路与最快可投入的时间。我期待与你合作，把这段关键代码打磨到科研级水准。

...rapid target motion • Adapt to scale and orientation changes • Maintain lock under partial occlusion • Recover gracefully if tracking confidence drops • Avoid drift over time A re-detection or hybrid tracking strategy is preferred if it improves robustness. Technical Requirements Preferred stack: • Python + OpenCV OR C++ + OpenCV • Modular architecture • Hardware acceleration support (CUDA / TensorRT) is a strong plus • Experience with: • Siamese-based trackers • DeepSORT-like approaches • Hybrid detection + tracking pipelines Clean, well-documented code is mandatory. Deliverables 1. Fully functional Linux application 2. Source code repository 3. Setup instructions + dependency list 4. Short demo video...

...website. I have the hardware available but need an expert who can install the model, configure all dependencies, and expose an endpoint that my front-end widget can call. Here is what I have in mind: • Select and download an open-weight GPT-like model that can reasonably run on local hardware (e.g., Llama-2, Mistral, or another suitable alternative). • Set up the execution environment—Python, CUDA, PyTorch or TensorFlow—plus any supporting libraries (LangChain, FastAPI, uvicorn, etc.). • Create or refine an inference script that keeps response times low enough for smooth chat. • Build a lightweight API (REST or WebSocket) so the website can pass the user’s prompt and receive the model’s reply. • Hand me clear, repeatable...

...expectations • Real-time theft detection logic that raises an event or REST webhook the moment a suspicious removal is spotted • On-screen bounding boxes and confidence scores for detected grocery items and customers • Continuous customer counter with hourly CSV/JSON export • Installers or scripts for Windows 10/11 and Raspberry Pi OS, including all required Python, OpenCV, PyTorch/ONNX, CUDA (where available) dependencies • A simple dashboard that shows live feed thumbnails, current customer count, and the last N theft alerts • Clear instructions on adding new grocery SKUs later Acceptance will be based on: 1. Smooth 25-30 fps inference on 1080p streams under Windows with GPU, and ≥10 fps on Raspberry Pi using CPU or a USB accele...

...the hardware allocated and wallets ready; what I need is an engineer who can take the nodes from zero to profitable operation and then keep them humming. Key tasks • Provision and secure each H100 instance, configure networking, firewalls, SSH keys and wallets • Containerise the stacks with Docker (Kubernetes or Podman are possible later, but Docker is fine for the first iteration) • Tune CUDA-level settings so every GPU cycle counts and rewards are maximised • Build simple Bash or Python scripts that monitor logs, restart on failure and push basic alerts • Produce step-by-step documentation so the setup can be replicated or audited at any time Acceptance criteria • Nodes reach consensus, stay above 99 % uptime and begin generating rewa...

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling.

...modern GPUs and expose a clean, future-proof API for downstream applications. My end goal is to abstract away vendor-specific quirks so a data-scientist, graphics engineer, or researcher can tap into raw parallel power without worrying about whether the machine is running Windows, Linux, or macOS, or whether it ships with NVIDIA, AMD, or Intel silicon. You’re free to recommend the optimal blend of CUDA, ROCm, OpenCL, Vulkan, or even a custom compute layer—what matters is performance, portability, and clean code that’s easy to extend. I’m open to focusing on a single workload first (machine-learning kernels, real-time graphics effects, or heavy scientific simulations) if that helps us validate the core, then scaling outward. Deliverables I’m exp...

Job Title: CUDA Developer Needed – GPU-Accelerated Bitcoin WIF Key Recovery Tool (Verification Required) Project Description: I am looking for an experienced CUDA / GPU developer to build and optimize a high-performance Bitcoin WIF private key recovery program. This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling. Technical Requirements: - Written in C++ with CUDA - Runs on NVIDIA GPUs - Command-line interface (CLI) - Supports Bitcoin WIF (Base58Check) - Supports compressed and uncompressed private keys - Correct che...

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

manual intervention. 3. Re-assemble processed frames back into a single clip using FFmpeg (or similar), ensuring temporal consistency—no flicker or dropped frames. 4. Expose a simple CLI command such as: python --input --output --strength 0.7 --seed 42 5. Provide a short README covering environment setup (Python, diffusers / transformers versions, CUDA requirements), example usage, and expected runtimes Acceptance criteria • The script completes a sample without errors and produces visibly live-action styling throughout. • Code is clean, commented, and includes a or environment.yml. Delivery: source code, README, and one converted sample clip produced by your wrapper.

...short written walkthrough covering hardware requirements, model parameters, and tips for further tuning. Acceptance criteria 1. Frame-by-frame identity preservation ≥ 95 % (verified with face-recognition scores). 2. No temporal flicker visible on 30-fps playback. 3. End-to-end generation time under 2× video length on a single high-end GPU. Tech stack keywords: PyTorch, TensorFlow, FFmpeg, CUDA, Google Colab, facial-landmark detection, GAN inversion. Roadmap beyond this delivery Once the core system is proven, I plan to expand into other AI-driven video features—scene synthesis, automated dubbing, even real-time object tracking—so clean, well-documented code is essential for future extension. Ready to start as soon as we agree on the approach, and ...